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.gitignore vendored
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# Copyright (c) 2015-2016, NVIDIA CORPORATION. All rights reserved. # Copyright (c) 2015-2016, NVIDIA CORPORATION. All rights reserved.
/build /build
*.gcov
/coverage/

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Copyright (c) 2015-2020, NVIDIA CORPORATION. All rights reserved. Copyright (c) 2015-2016, NVIDIA CORPORATION. All rights reserved.
Redistribution and use in source and binary forms, with or without Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions modification, are permitted provided that the following conditions
@ -29,11 +29,3 @@
The U.S. Department of Energy funded the development of this software The U.S. Department of Energy funded the development of this software
under subcontract 7078610 with Lawrence Berkeley National Laboratory. under subcontract 7078610 with Lawrence Berkeley National Laboratory.
This code also includes files from the NVIDIA Tools Extension SDK project.
See:
https://github.com/NVIDIA/NVTX
for more information and license details.

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Makefile
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# #
# Copyright (c) 2015-2019, NVIDIA CORPORATION. All rights reserved. # Copyright (c) 2015-2016, NVIDIA CORPORATION. All rights reserved.
# #
# See LICENSE.txt for license information # See LICENCE.txt for license information
# #
.PHONY : all clean
default : src.build CUDA_HOME ?= /usr/local/cuda
install : src.install PREFIX ?= /usr/local
BUILDDIR ?= $(abspath ./build) VERBOSE ?= 0
ABSBUILDDIR := $(abspath $(BUILDDIR)) KEEP ?= 0
TARGETS := src pkg DEBUG ?= 0
clean: ${TARGETS:%=%.clean} PROFAPI ?= 0
test.build: src.build BUILDDIR ?= build
LICENSE_FILES := LICENSE.txt
LICENSE_TARGETS := $(LICENSE_FILES:%=$(BUILDDIR)/%)
lic: $(LICENSE_TARGETS)
${BUILDDIR}/%.txt: %.txt CUDA_LIB ?= $(CUDA_HOME)/lib64
@printf "Copying %-35s > %s\n" $< $@ CUDA_INC ?= $(CUDA_HOME)/include
mkdir -p ${BUILDDIR} NVCC ?= $(CUDA_HOME)/bin/nvcc
cp $< $@
src.%: NVCC_GENCODE ?= -gencode=arch=compute_35,code=sm_35 \
${MAKE} -C src $* BUILDDIR=${ABSBUILDDIR} -gencode=arch=compute_50,code=sm_50 \
-gencode=arch=compute_52,code=sm_52 \
-gencode=arch=compute_52,code=compute_52
pkg.%: CXXFLAGS := -I$(CUDA_INC) -fPIC -fvisibility=hidden
${MAKE} -C pkg $* BUILDDIR=${ABSBUILDDIR} NVCUFLAGS := -ccbin $(CXX) $(NVCC_GENCODE) -lineinfo -std=c++11 -maxrregcount 96
# Use addprefix so that we can specify more than one path
LDFLAGS := $(addprefix -L,${CUDA_LIB}) -lcudart -lrt
ifeq ($(DEBUG), 0)
NVCUFLAGS += -O3
CXXFLAGS += -O3
else
NVCUFLAGS += -O0 -G
CXXFLAGS += -O0 -g -ggdb3
endif
ifneq ($(VERBOSE), 0)
NVCUFLAGS += -Xptxas -v -Xcompiler -Wall,-Wextra
CXXFLAGS += -Wall -Wextra
else
.SILENT:
endif
ifneq ($(KEEP), 0)
NVCUFLAGS += -keep
endif
ifneq ($(PROFAPI), 0)
CXXFLAGS += -DPROFAPI
endif
NCCL_MAJOR := 1
NCCL_MINOR := 3
NCCL_PATCH := 0
CXXFLAGS += -DNCCL_MAJOR=$(NCCL_MAJOR) -DNCCL_MINOR=$(NCCL_MINOR) -DNCCL_PATCH=$(NCCL_PATCH)
CUDA_VERSION ?= $(shell ls $(CUDA_LIB)/libcudart.so.* | head -1 | rev | cut -d "." -f -2 | rev)
CUDA_MAJOR = $(shell echo $(CUDA_VERSION) | cut -d "." -f 1)
CUDA_MINOR = $(shell echo $(CUDA_VERSION) | cut -d "." -f 2)
CXXFLAGS += -DCUDA_MAJOR=$(CUDA_MAJOR) -DCUDA_MINOR=$(CUDA_MINOR)
.PHONY : lib clean test mpitest install deb debian debclean
.DEFAULT : lib
INCEXPORTS := nccl.h
LIBSRCFILES := libwrap.cu core.cu all_gather.cu all_reduce.cu broadcast.cu reduce.cu reduce_scatter.cu
LIBNAME := libnccl.so
INCDIR := $(BUILDDIR)/include
LIBDIR := $(BUILDDIR)/lib
OBJDIR := $(BUILDDIR)/obj
INCTARGETS := $(patsubst %, $(INCDIR)/%, $(INCEXPORTS))
LIBSONAME := $(patsubst %,%.$(NCCL_MAJOR),$(LIBNAME))
LIBTARGET := $(patsubst %,%.$(NCCL_MAJOR).$(NCCL_MINOR).$(NCCL_PATCH),$(LIBNAME))
LIBLINK := $(patsubst lib%.so, -l%, $(LIBNAME))
LIBOBJ := $(patsubst %.cu, $(OBJDIR)/%.o, $(filter %.cu, $(LIBSRCFILES)))
DEPFILES := $(patsubst %.o, %.d, $(LIBOBJ)) $(patsubst %, %.d, $(TESTBINS)) $(patsubst %, %.d, $(MPITESTBINS))
lib : $(INCTARGETS) $(LIBDIR)/$(LIBTARGET)
-include $(DEPFILES)
$(LIBDIR)/$(LIBTARGET) : $(LIBOBJ)
@printf "Linking %-25s\n" $@
mkdir -p $(LIBDIR)
$(CXX) $(CXXFLAGS) -shared -Wl,--no-as-needed -Wl,-soname,$(LIBSONAME) -o $@ $(LDFLAGS) $(LIBOBJ)
ln -sf $(LIBSONAME) $(LIBDIR)/$(LIBNAME)
ln -sf $(LIBTARGET) $(LIBDIR)/$(LIBSONAME)
$(INCDIR)/%.h : src/%.h
@printf "Grabbing %-25s > %-25s\n" $< $@
mkdir -p $(INCDIR)
cp -f $< $@
$(OBJDIR)/%.o : src/%.cu
@printf "Compiling %-25s > %-25s\n" $< $@
mkdir -p $(OBJDIR)
$(NVCC) -c $(NVCUFLAGS) --compiler-options "$(CXXFLAGS)" $< -o $@
@$(NVCC) -M $(NVCUFLAGS) --compiler-options "$(CXXFLAGS)" $< > $(@:%.o=%.d.tmp)
@sed "0,/^.*:/s//$(subst /,\/,$@):/" $(@:%.o=%.d.tmp) > $(@:%.o=%.d)
@sed -e 's/.*://' -e 's/\\$$//' < $(@:%.o=%.d.tmp) | fmt -1 | \
sed -e 's/^ *//' -e 's/$$/:/' >> $(@:%.o=%.d)
@rm -f $(@:%.o=%.d.tmp)
clean :
rm -rf $(BUILDDIR)
install : lib
mkdir -p $(PREFIX)/lib
mkdir -p $(PREFIX)/include
cp -P -v $(BUILDDIR)/lib/* $(PREFIX)/lib/
cp -v $(BUILDDIR)/include/* $(PREFIX)/include/
#### TESTS ####
TEST_ONLY ?= 0
# Tests depend on lib, except in TEST_ONLY mode.
ifeq ($(TEST_ONLY), 0)
TSTDEP = $(INCTARGETS) $(LIBDIR)/$(LIBTARGET)
endif
NCCL_LIB ?= $(LIBDIR)
NCCL_INC ?= $(INCDIR)
MPI_HOME ?= /usr
MPI_INC ?= $(MPI_HOME)/include
MPI_LIB ?= $(MPI_HOME)/lib
MPIFLAGS := -I$(MPI_INC) -L$(MPI_LIB) -lmpi
TESTS := all_gather_test all_gather_scan \
all_reduce_test all_reduce_scan \
broadcast_test broadcast_scan \
reduce_test reduce_scan \
reduce_scatter_test reduce_scatter_scan
MPITESTS := mpi_test
TSTINC := -I$(NCCL_INC) -Itest/include
TSTLIB := -L$(NCCL_LIB) $(LIBLINK) $(LDFLAGS)
TSTDIR := $(BUILDDIR)/test/single
MPITSTDIR := $(BUILDDIR)/test/mpi
TESTBINS := $(patsubst %, $(TSTDIR)/%, $(TESTS))
MPITESTBINS:= $(patsubst %, $(MPITSTDIR)/%, $(MPITESTS))
test : $(TESTBINS)
$(TSTDIR)/% : test/single/%.cu test/include/*.h $(TSTDEP)
@printf "Building %-25s > %-24s\n" $< $@
mkdir -p $(TSTDIR)
$(NVCC) $(TSTINC) $(NVCUFLAGS) --compiler-options "$(CXXFLAGS)" -o $@ $< $(TSTLIB) -lcuda -lcurand -lnvToolsExt
@$(NVCC) -M $(TSTINC) $(NVCUFLAGS) --compiler-options "$(CXXFLAGS)" $< $(TSTLIB) -lcuda -lcurand -lnvToolsExt > $(@:%=%.d.tmp)
@sed "0,/^.*:/s//$(subst /,\/,$@):/" $(@:%=%.d.tmp) > $(@:%=%.d)
@sed -e 's/.*://' -e 's/\\$$//' < $(@:%=%.d.tmp) | fmt -1 | \
sed -e 's/^ *//' -e 's/$$/:/' >> $(@:%=%.d)
@rm -f $(@:%=%.d.tmp)
mpitest : $(MPITESTBINS)
$(MPITSTDIR)/% : test/mpi/%.cu $(TSTDEP)
@printf "Building %-25s > %-24s\n" $< $@
mkdir -p $(MPITSTDIR)
$(NVCC) $(MPIFLAGS) $(TSTINC) $(NVCUFLAGS) --compiler-options "$(CXXFLAGS)" -o $@ $< $(TSTLIB) -lcurand
@$(NVCC) $(MPIFLAGS) -M $(TSTINC) $(NVCUFLAGS) --compiler-options "$(CXXFLAGS)" $< $(TSTLIB) -lcurand > $(@:%=%.d.tmp)
@sed "0,/^.*:/s//$(subst /,\/,$@):/" $(@:%=%.d.tmp) > $(@:%=%.d)
@sed -e 's/.*://' -e 's/\\$$//' < $(@:%=%.d.tmp) | fmt -1 | \
sed -e 's/^ *//' -e 's/$$/:/' >> $(@:%=%.d)
@rm -f $(@:%=%.d.tmp)
#### PACKAGING ####
DEBIANDIR := $(BUILDDIR)/debian
DEBGEN_IN := $(shell (cd debian ; ls *.in))
DEBGEN := $(DEBGEN_IN:.in=)
DEBFILES := compat copyright libnccl-dev.install libnccl-dev.manpages nccl.7 rules $(DEBGEN)
DEBTARGETS := $(patsubst %, $(DEBIANDIR)/%, $(DEBFILES))
DEB_REVISION ?= 1
DEB_TIMESTAMP := $(shell date -R)
DEB_ARCH ?= amd64
debian : $(DEBTARGETS)
deb : lib debian
@printf "Building Debian package\n"
(cd $(BUILDDIR); debuild -eLD_LIBRARY_PATH -uc -us -d -b)
mkdir -p $(BUILDDIR)/deb/
mv $(BUILDDIR)/../libnccl*.deb $(BUILDDIR)/deb/
debclean :
rm -Rf $(DEBIANDIR)
$(DEBIANDIR)/% : debian/%.in
@printf "Generating %-25s > %-24s\n" $< $@
sed -e "s/\$${nccl:Major}/$(NCCL_MAJOR)/g" \
-e "s/\$${nccl:Minor}/$(NCCL_MINOR)/g" \
-e "s/\$${nccl:Patch}/$(NCCL_PATCH)/g" \
-e "s/\$${cuda:Major}/$(CUDA_MAJOR)/g" \
-e "s/\$${cuda:Minor}/$(CUDA_MINOR)/g" \
-e "s/\$${deb:Revision}/$(DEB_REVISION)/g" \
-e "s/\$${deb:Timestamp}/$(DEB_TIMESTAMP)/g" \
-e "s/\$${deb:Arch}/$(DEB_ARCH)/g" \
$< > $@
$(DEBIANDIR)/% : debian/%
@printf "Grabbing %-25s > %-25s\n" $< $@
mkdir -p $(DEBIANDIR)
cp -f $< $@
pkg.debian.prep: lic
pkg.txz.prep: lic

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README.md
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# NCCL # NCCL
Optimized primitives for inter-GPU communication. Optimized primitives for collective multi-GPU communication.
## Introduction ## Introduction
NCCL (pronounced "Nickel") is a stand-alone library of standard communication routines for GPUs, implementing all-reduce, all-gather, reduce, broadcast, reduce-scatter, as well as any send/receive based communication pattern. It has been optimized to achieve high bandwidth on platforms using PCIe, NVLink, NVswitch, as well as networking using InfiniBand Verbs or TCP/IP sockets. NCCL supports an arbitrary number of GPUs installed in a single node or across multiple nodes, and can be used in either single- or multi-process (e.g., MPI) applications. NCCL (pronounced "Nickel") is a stand-alone library of standard collective communication routines, such as all-gather, reduce, broadcast, etc., that have been optimized to achieve high bandwidth over PCIe. NCCL supports an arbitrary number of GPUs installed in a single node and can be used in either single- or multi-process (e.g., MPI) applications.
[This blog post](https://devblogs.nvidia.com/parallelforall/fast-multi-gpu-collectives-nccl/) provides details on NCCL functionality, goals, and performance.
For more information on NCCL usage, please refer to the [NCCL documentation](https://docs.nvidia.com/deeplearning/sdk/nccl-developer-guide/index.html). ## What's inside
## Build At present, the library implements the following collectives:
- all-reduce
- all-gather
- reduce-scatter
- reduce
- broadcast
Note: the official and tested builds of NCCL can be downloaded from: https://developer.nvidia.com/nccl. You can skip the following build steps if you choose to use the official builds. These collectives are implemented using ring algorithms and have been optimized primarily for throughput. For best performance, small collectives should be batched into larger operations whenever possible. Small test binaries demonstrating how to use each of the above collectives are also provided.
To build the library : ## Requirements
NCCL requires at least CUDA 7.0 and Kepler or newer GPUs. Best performance is achieved when all GPUs are located on a common PCIe root complex, but multi-socket configurations are also supported.
Note: NCCL may also work with CUDA 6.5, but this is an untested configuration.
## Build & run
To build the library and tests.
```shell ```shell
$ cd nccl $ cd nccl
$ make -j src.build $ make CUDA_HOME=<cuda install path> test
``` ```
If CUDA is not installed in the default /usr/local/cuda path, you can define the CUDA path with : Test binaries are located in the subdirectories nccl/build/test/{single,mpi}.
```shell ```shell
$ make src.build CUDA_HOME=<path to cuda install> $ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:./build/lib
$ ./build/test/single/all_reduce_test
Error: must specify at least data size in bytes!
Tests nccl AllReduce with user supplied arguments.
Usage: all_reduce_test <data size in bytes> [number of GPUs] [GPU 0] [GPU 1] ...
$ ./build/test/single/all_reduce_test 10000000
# Using devices
# Device 0 -> 0 [0x0a] GeForce GTX TITAN X
# Device 1 -> 1 [0x09] GeForce GTX TITAN X
# Device 2 -> 2 [0x06] GeForce GTX TITAN X
# Device 3 -> 3 [0x05] GeForce GTX TITAN X
# out-of-place in-place
# bytes N type op time algbw busbw res time algbw busbw res
10000000 10000000 char sum 1.628 6.14 9.21 0e+00 1.932 5.18 7.77 0e+00
10000000 10000000 char prod 1.629 6.14 9.21 0e+00 1.643 6.09 9.13 0e+00
10000000 10000000 char max 1.621 6.17 9.25 0e+00 1.634 6.12 9.18 0e+00
10000000 10000000 char min 1.633 6.12 9.19 0e+00 1.637 6.11 9.17 0e+00
10000000 2500000 int sum 1.611 6.21 9.31 0e+00 1.626 6.15 9.23 0e+00
10000000 2500000 int prod 1.613 6.20 9.30 0e+00 1.629 6.14 9.21 0e+00
10000000 2500000 int max 1.619 6.18 9.26 0e+00 1.627 6.15 9.22 0e+00
10000000 2500000 int min 1.619 6.18 9.27 0e+00 1.624 6.16 9.24 0e+00
10000000 5000000 half sum 1.617 6.18 9.28 4e-03 1.636 6.11 9.17 4e-03
10000000 5000000 half prod 1.618 6.18 9.27 1e-03 1.657 6.03 9.05 1e-03
10000000 5000000 half max 1.608 6.22 9.33 0e+00 1.621 6.17 9.25 0e+00
10000000 5000000 half min 1.610 6.21 9.32 0e+00 1.627 6.15 9.22 0e+00
10000000 2500000 float sum 1.618 6.18 9.27 5e-07 1.622 6.17 9.25 5e-07
10000000 2500000 float prod 1.614 6.20 9.29 1e-07 1.628 6.14 9.21 1e-07
10000000 2500000 float max 1.616 6.19 9.28 0e+00 1.633 6.12 9.19 0e+00
10000000 2500000 float min 1.613 6.20 9.30 0e+00 1.628 6.14 9.21 0e+00
10000000 1250000 double sum 1.629 6.14 9.21 0e+00 1.628 6.14 9.21 0e+00
10000000 1250000 double prod 1.619 6.18 9.26 2e-16 1.628 6.14 9.21 2e-16
10000000 1250000 double max 1.613 6.20 9.30 0e+00 1.630 6.13 9.20 0e+00
10000000 1250000 double min 1.622 6.16 9.25 0e+00 1.623 6.16 9.24 0e+00
``` ```
NCCL will be compiled and installed in `build/` unless `BUILDDIR` is set. To install, run `make PREFIX=<install dir> install` and add `<instal dir>/lib` to your `LD_LIBRARY_PATH`.
By default, NCCL is compiled for all supported architectures. To accelerate the compilation and reduce the binary size, consider redefining `NVCC_GENCODE` (defined in `makefiles/common.mk`) to only include the architecture of the target platform : ## Usage
```shell
$ make -j src.build NVCC_GENCODE="-gencode=arch=compute_70,code=sm_70" NCCL follows the MPI collectives API fairly closely. Before any collectives can be called, a communicator object must be initialized on each GPU. On a single-process machine, all GPUs can be conveniently initialized using `ncclCommInitAll`. For multi-process applications (e.g., with MPI), `ncclCommInitRank` must be called for each GPU. Internally `ncclCommInitRank` invokes a synchronization among all GPUs, so these calls must be invoked in different host threads (or processes) for each GPU. A brief single-process example follows, for an MPI example see test/mpi/mpi_test.cu. For details about the API see nccl.h.
```c
#include <nccl.h>
typedef struct {
double* sendBuff;
double* recvBuff;
int size;
cudaStream_t stream;
} PerThreadData;
int main(int argc, char* argv[])
{
int nGPUs;
cudaGetDeviceCount(&nGPUs);
ncclComm_t* comms = (ncclComm_t*)malloc(sizeof(ncclComm_t)*nGPUs);
ncclCommInitAll(comms, nGPUs); // initialize communicator
// One communicator per process
PerThreadData* data;
... // Allocate data and issue work to each GPU's
// perDevStream to populate the sendBuffs.
for(int i=0; i<nGPUs; ++i) {
cudaSetDevice(i); // Correct device must be set
// prior to each collective call.
ncclAllReduce(data[i].sendBuff, data[i].recvBuff, size,
ncclDouble, ncclSum, comms[i], data[i].stream);
}
... // Issue work into data[*].stream to consume buffers, etc.
}
``` ```
## Install ## Copyright and License
To install NCCL on the system, create a package then install it as root. NCCL is provided under the [BSD licence](LICENSE.txt). All source code and
accompanying documentation is copyright (c) 2015-2016, NVIDIA CORPORATION. All
rights reserved.
Debian/Ubuntu :
```shell
$ # Install tools to create debian packages
$ sudo apt install build-essential devscripts debhelper fakeroot
$ # Build NCCL deb package
$ make pkg.debian.build
$ ls build/pkg/deb/
```
RedHat/CentOS :
```shell
$ # Install tools to create rpm packages
$ sudo yum install rpm-build rpmdevtools
$ # Build NCCL rpm package
$ make pkg.redhat.build
$ ls build/pkg/rpm/
```
OS-agnostic tarball :
```shell
$ make pkg.txz.build
$ ls build/pkg/txz/
```
## Tests
Tests for NCCL are maintained separately at https://github.com/nvidia/nccl-tests.
```shell
$ git clone https://github.com/NVIDIA/nccl-tests.git
$ cd nccl-tests
$ make
$ ./build/all_reduce_perf -b 8 -e 256M -f 2 -g <ngpus>
```
## Copyright
All source code and accompanying documentation is copyright (c) 2015-2020, NVIDIA CORPORATION. All rights reserved.

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nccl (${nccl:Major}.${nccl:Minor}.${nccl:Patch}-${deb:Revision}+cuda${cuda:Major}.${cuda:Minor}) trusty; urgency=medium
* Automatic Debian package from build
-- cudatools <cudatools@nvidia.com> ${deb:Timestamp}

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Source: nccl
Section: libs
Maintainer: cudatools <cudatools@nvidia.com>
Priority: optional
Build-depends: debhelper(>=9)
Standards-Version: 3.9.5
Package: libnccl${nccl:Major}
Section: libs
Architecture: ${deb:Arch}
Depends: ${misc:Depends}, ${shlibs:Depends}
Description: NVIDIA Collectives Communication Library (NCCL) Runtime
NCCL (pronounced "Nickel") is a stand-alone library of standard collective
communication routines for GPUs, such as all-gather, reduce, broadcast, etc.,
that have been optimized to achieve high bandwidth over PCIe. NCCL supports up
to eight GPUs and can be used in either single- or multi-process (e.g., MPI)
applications.
Package: libnccl-dev
Section: libdevel
Architecture: ${deb:Arch}
Depends: ${misc:Depends}, ${shlibs:Depends}, libnccl${nccl:Major} (= ${binary:Version})
Description: NVIDIA Collectives Communication Library (NCCL) Development Files
NCCL (pronounced "Nickel") is a stand-alone library of standard collective
communication routines for GPUs, such as all-gather, reduce, broadcast, etc.,
that have been optimized to achieve high bandwidth over PCIe. NCCL supports up
to eight GPUs and can be used in either single- or multi-process (e.g., MPI)
applications.

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../LICENSE.txt

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include/nccl.h usr/include
lib/libnccl.so /usr/lib/x86_64-linux-gnu

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debian/nccl.7

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lib/libnccl.so.${nccl:Major} /usr/lib/x86_64-linux-gnu
lib/libnccl.so.${nccl:Major}.${nccl:Minor}.${nccl:Patch} /usr/lib/x86_64-linux-gnu

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.TH NCCL
.SH NAME
.PP
nccl \- Optimized primitives for collective multi\-GPU communication.
.SH Introduction
.PP
NCCL (pronounced "Nickel") is a stand\-alone library of standard collective communication routines, such as all\-gather, reduce, broadcast, etc., that have been optimized to achieve high bandwidth over PCIe. NCCL supports an arbitrary number of GPUs installed in a single node and can be used in either single\- or multi\-process (e.g., MPI) applications.
.SH What's inside
.PP
At present, the library implements the following collectives:
\- all\-reduce
\- all\-gather
\- reduce\-scatter
\- reduce
\- broadcast
.PP
These collectives are implemented using ring algorithms and have been optimized primarily for throughput. For best performance, small collectives should be batched into larger operations whenever possible. Small test binaries demonstrating how to use each of the above collectives are also provided.
.SH Requirements
.PP
NCCL requires at least CUDA 7.0 and Kepler or newer GPUs. Best performance is achieved when all GPUs are located on a common PCIe root complex, but multi\-socket configurations are also supported.
.PP
Note: NCCL may also work with CUDA 6.5, but this is an untested configuration.
.SH Build & run
.PP
To build the library and tests.
.PP
.RS
.nf
$ cd nccl
$ make CUDA\_HOME=<cuda install path> test
.fi
.RE
.PP
Test binaries are located in the subdirectories nccl/build/test and nccl/build/mpitest.
.PP
.RS
.nf
$ export LD\_LIBRARY\_PATH=$LD\_LIBRARY\_PATH:./build/lib
$ ./build/test/all\_reduce\_test
Error: must specify at least data size in bytes!
Tests nccl AllReduce with user supplied arguments.
Usage: all\_reduce\_test <data size in bytes> [number of GPUs] [GPU 0] [GPU 1] ...
$ ./build/test/all\_reduce\_test 10000000
# Using devices
# Device 0 \-> 0 [0x0a] GeForce GTX TITAN X
# Device 1 \-> 1 [0x09] GeForce GTX TITAN X
# Device 2 \-> 2 [0x06] GeForce GTX TITAN X
# Device 3 \-> 3 [0x05] GeForce GTX TITAN X
# out\-of\-place in\-place
# bytes N type op time algbw busbw res time algbw busbw res
10000000 10000000 char sum 1.628 6.14 9.21 0e+00 1.932 5.18 7.77 0e+00
10000000 10000000 char prod 1.629 6.14 9.21 0e+00 1.643 6.09 9.13 0e+00
10000000 10000000 char max 1.621 6.17 9.25 0e+00 1.634 6.12 9.18 0e+00
10000000 10000000 char min 1.633 6.12 9.19 0e+00 1.637 6.11 9.17 0e+00
10000000 2500000 int sum 1.611 6.21 9.31 0e+00 1.626 6.15 9.23 0e+00
10000000 2500000 int prod 1.613 6.20 9.30 0e+00 1.629 6.14 9.21 0e+00
10000000 2500000 int max 1.619 6.18 9.26 0e+00 1.627 6.15 9.22 0e+00
10000000 2500000 int min 1.619 6.18 9.27 0e+00 1.624 6.16 9.24 0e+00
10000000 5000000 half sum 1.617 6.18 9.28 4e\-03 1.636 6.11 9.17 4e\-03
10000000 5000000 half prod 1.618 6.18 9.27 1e\-03 1.657 6.03 9.05 1e\-03
10000000 5000000 half max 1.608 6.22 9.33 0e+00 1.621 6.17 9.25 0e+00
10000000 5000000 half min 1.610 6.21 9.32 0e+00 1.627 6.15 9.22 0e+00
10000000 2500000 float sum 1.618 6.18 9.27 5e\-07 1.622 6.17 9.25 5e\-07
10000000 2500000 float prod 1.614 6.20 9.29 1e\-07 1.628 6.14 9.21 1e\-07
10000000 2500000 float max 1.616 6.19 9.28 0e+00 1.633 6.12 9.19 0e+00
10000000 2500000 float min 1.613 6.20 9.30 0e+00 1.628 6.14 9.21 0e+00
10000000 1250000 double sum 1.629 6.14 9.21 0e+00 1.628 6.14 9.21 0e+00
10000000 1250000 double prod 1.619 6.18 9.26 2e\-16 1.628 6.14 9.21 2e\-16
10000000 1250000 double max 1.613 6.20 9.30 0e+00 1.630 6.13 9.20 0e+00
10000000 1250000 double min 1.622 6.16 9.25 0e+00 1.623 6.16 9.24 0e+00
.fi
.RE
.PP
To install, run \fB\fCmake PREFIX=<install dir> install\fR and add \fB\fC<instal dir>/lib\fR to your \fB\fCLD\_LIBRARY\_PATH\fR.
.SH Usage
.PP
NCCL follows the MPI collectives API fairly closely. Before any collectives can be called, a communicator object must be initialized on each GPU. On a single\-process machine, all GPUs can be conveniently initialized using \fB\fCncclCommInitAll\fR. For multi\-process applications (e.g., with MPI), \fB\fCncclCommInitRank\fR must be called for each GPU. Internally \fB\fCncclCommInitRank\fR invokes a synchronization among all GPUs, so these calls must be invoked in different host threads (or processes) for each GPU. A brief single\-process example follows, for an MPI example see src/mpi\_test.cu. For details about the API see nccl.h.
.PP
.RS
.nf
#include <nccl.h>
typedef struct \{
double* sendBuff;
double* recvBuff;
int size;
cudaStream\_t stream;
\} PerThreadData;
int main(int argc, char* argv[])
\{
int nGPUs;
cudaGetDeviceCount(\&nGPUs);
ncclComm\_t* comms = (ncclComm\_t*)malloc(sizeof(ncclComm\_t)*nGPUs);
ncclCommInitAll(comms, nGPUs); // initialize communicator
// One communicator per process
PerThreadData* data;
... // Allocate data and issue work to each GPU's
// perDevStream to populate the sendBuffs.
for(int i=0; i<nGPUs; ++i) \{
cudaSetDevice(i); // Correct device must be set
// prior to each collective call.
ncclAllReduce(data[i].sendBuff, data[i].recvBuff, size,
ncclDouble, ncclSum, comms[i], data[i].stream);
\}
... // Issue work into data[*].stream to consume buffers, etc.
\}
.fi
.RE
.SH Copyright
.PP
All source code and accompanying documentation is copyright (c) 2015\-2016, NVIDIA CORPORATION. All
rights reserved.

View File

@ -11,6 +11,3 @@ override_dh_auto_test:
override_dh_auto_clean: override_dh_auto_clean:
# Do not make clean # Do not make clean
override_dh_builddeb:
dh_builddeb -- -Zxz

1
debian/shlibs.local.in vendored Normal file
View File

@ -0,0 +1 @@
libcudart ${cuda:Major}.${cuda:Minor} cuda-cudart-${cuda:Major}-${cuda:Minor}

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@ -1,419 +0,0 @@
# NCCL Net Plugin Documentation
This page describes the NCCL Net plugin API and how to implement a network plugin for NCCL.
# Overview
To allow NCCL to work on any network type, NCCL provides a way to use external plugins. Plugins
implement the NCCL network API, and decouple NCCL binary builds which are built against a
particular version of the GPU stack (i.e. CUDA) from the network code which is built against a
particular version of the networking stack. That way, we can easily integrate any CUDA version
with any network stack version.
NCCL network plugins come as a shared library called `libnccl-net.so`. That shared library
contains one or more implementations of the NCCL NET API, in the form of versioned structs,
filled with pointers to all required functions.
# Plugin architecture
## Plugin name and supporting multiple network plugins
When NCCL is initialized, it will look for a `libnccl-net.so` library and dynamically load it,
then look for symbols inside the library.
The `NCCL_NET_PLUGIN` environment variable allows multiple plugins to coexist. If set, NCCL
will look for a library with a name of `libnccl-net-${NCCL_NET_PLUGIN}.so`. It is therefore
advised to name the library following that pattern, with a symlink pointing `libnccl-net.so`
to `libnccl-net-${NCCL_NET_PLUGIN}.so`. That way, if there are multiple plugins in the path,
setting `NCCL_NET_PLUGIN` will allow users to select the right plugin.
## Struct versioning
Once a library is found, NCCL will look for a symbol named `ncclNet_vX`, with `X` increasing
over time. The versioning ensures that the plugin and the NCCL core are compatible.
Plugins are encouraged to provide multiple of those symbols, implementing multiple versions
of the NCCL NET API, so that the same plugin can be compiled and support a wide range of NCCL
versions.
Conversely, and to ease transition, NCCL can choose to support different plugin versions, looking
for the latest ncclNet struct version, but also looking for older ones so that older plugins
would still work.
## In-network collective operations, a.k.a. collNet
Additionally to the ncclNet structure, network plugins can provide a collNet structure which
implements in-network collective operations, if supported. That can be used by the NCCL collNet
algorithm to accelerate inter-node reductions in allReduce.
The collNet struct is a different, optional struct provided by the network plugin, but its
versioning is tied to the ncclNet struct and many functions are common between the two to
ease the implementation.
## Headers management
To help users build plugins effortlessly, plugins should copy the `ncclNet_vX` definitions
they support to their internal includes. An example is shown in `ext-net/example/` where we keep
all headers in the `nccl/` directory and provide thin layers to implement old versions on top
of newer ones.
The `nccl/` directory is populated with `net_vX.h` files extracting all relevant definitions
from old API versions. It also provides error codes in `err.h`.
# API (v10)
Below is the main `ncclNet_v10` struct. Each function is explained in later sections.
```
typedef struct {
// Name of the network (mainly for logs)
const char* name;
// Initialize the network.
ncclResult_t (*init)(ncclDebugLogger_t logFunction, ncclProfilerCallback_t profFunction);
// Return the number of adapters.
ncclResult_t (*devices)(int* ndev);
// Get various device properties.
ncclResult_t (*getProperties)(int dev, ncclNetProperties_v10_t* props);
// Create a receiving object and provide a handle to connect to it. The
// handle can be up to NCCL_NET_HANDLE_MAXSIZE bytes and will be exchanged
// between ranks to create a connection.
ncclResult_t (*listen)(int dev, void* handle, void** listenComm);
// Connect to a handle and return a sending comm object for that peer.
// This call must not block for the connection to be established, and instead
// should return successfully with sendComm == NULL with the expectation that
// it will be called again until sendComm != NULL.
// If *sendDevComm points to a valid object, then NCCL is requesting device offload for this connection
ncclResult_t (*connect)(int dev, ncclNetCommConfig_v10_t* config, void* handle, void** sendComm, ncclNetDeviceHandle_v10_t** sendDevComm);
// Finalize connection establishment after remote peer has called connect.
// This call must not block for the connection to be established, and instead
// should return successfully with recvComm == NULL with the expectation that
// it will be called again until recvComm != NULL.
// If *recvDevComm points to a valid object, then NCCL is requesting device offload for this connection
ncclResult_t (*accept)(void* listenComm, void** recvComm, ncclNetDeviceHandle_v10_t** recvDevComm);
// Register/Deregister memory. Comm can be either a sendComm or a recvComm.
// Type is either NCCL_PTR_HOST or NCCL_PTR_CUDA.
ncclResult_t (*regMr)(void* comm, void* data, size_t size, int type, void** mhandle);
/* DMA-BUF support */
ncclResult_t (*regMrDmaBuf)(void* comm, void* data, size_t size, int type, uint64_t offset, int fd, void** mhandle);
ncclResult_t (*deregMr)(void* comm, void* mhandle);
// Asynchronous send to a peer.
// May return request == NULL if the call cannot be performed (or would block)
ncclResult_t (*isend)(void* sendComm, void* data, size_t size, int tag, void* mhandle, void* pHandle, void** request);
// Asynchronous recv from a peer.
// May return request == NULL if the call cannot be performed (or would block)
ncclResult_t (*irecv)(void* recvComm, int n, void** data, size_t* sizes, int* tags, void** mhandles, void** pHandles, void** request);
// Perform a flush/fence to make sure all data received with NCCL_PTR_CUDA is
// visible to the GPU
ncclResult_t (*iflush)(void* recvComm, int n, void** data, int* sizes, void** mhandles, void** request);
// Test whether a request is complete. If size is not NULL, it returns the
// number of bytes sent/received.
ncclResult_t (*test)(void* request, int* done, int* sizes);
// Close and free send/recv comm objects
ncclResult_t (*closeSend)(void* sendComm);
ncclResult_t (*closeRecv)(void* recvComm);
ncclResult_t (*closeListen)(void* listenComm);
// Copy the given mhandle to a dptr in a format usable by this plugin's device code
ncclResult_t (*getDeviceMr)(void* comm, void* mhandle, void** dptr_mhandle);
// Notify the plugin that a recv has completed by the device
ncclResult_t (*irecvConsumed)(void* recvComm, int n, void* request);
// Virtual NIC APIs. makeVDevice will create a virtual NIC given the specified properties, and tell the caller
// what index this new vNIC exists at
ncclResult_t (*makeVDevice)(int* d, ncclNetVDeviceProps_t* props);
} ncclNet_t;
```
## Error codes
All plugins functions use NCCL error codes as return value. `ncclSuccess` should be returned upon
success.
Otherwise, plugins can return one of the following:
- `ncclSystemError` is the most common error for network plugins, when a call to the linux kernel
or a system library fails. This typically includes all network/hardware errors.
- `ncclInternalError` is returned when the NCCL core code is using the network plugin in an
incorrect way, for example allocating more requests than it should, or passing an invalid argument
to calls.
- `ncclInvalidUsage` should be returned when the error is most likely a user error. This can
include misconfiguration, but also sizes mismatch.
- `ncclInvalidArgument` should usually not be used by plugins since arguments should be checked by
the NCCL core layer.
- `ncclUnhandledCudaError` is returned when an error comes from CUDA. Since network plugins should
not need to rely on CUDA, this should not be common.
## Operation overview
NCCL will call the `init` function first, then query the number of network devices with the
`devices` function, getting each network device properties with `getProperties`.
If NCCL wishes to initialize virtual devices, used in NIC fusion currently, it can call `makeVDevice`
specifying a list of physical devices (the original devices listed from `devices`) it wishes to
merge together. If the plugin does not support NIC fusion, it can set `makeVDevice` to null.
To establish a connection between two network devices, NCCL will first call `listen` on the
receiving side, pass the returned handle to the sender side of the connection, and call `connect`
with that handle. Finally, `accept` will be called on the receiving side to finalize the connection
establishment.
`connect` and `accept` can receive an optional `netDevComm` pointer from the caller, if the caller
wishes to make use of device networking. This parameter may be ignored by the plugin if it does
not support device-side networking.
Once the connection is established, communication will be done using the functions `isend`,
`irecv` and `test`. Prior to calling `isend` or `irecv`, NCCL will call the `regMr` function on
all buffers to allow RDMA NICs to prepare buffers. `deregMr` will be used to unregister buffers.
In certain conditions, `iflush` will be called after a receive calls completes to allow the network
plugin to flush data and ensure the GPU will observe the newly written data.
To close the connections NCCL will call `closeListen` to close the object returned by `listen`,
`closeSend` to close the object returned by `connect` and `closeRecv` to close the object returned
by `accept`.
## API Functions
### Initialization
`name`
The `name` field should point to a character string with the name of the network plugin. This will
be used for all logging, especially when `NCCL_DEBUG=INFO` is set.
Note: setting `NCCL_NET=<plugin name>` will ensure a specific network implementation is used, with
a matching `name`. This is not to be confused with `NCCL_NET_PLUGIN` which defines a suffix to the
`libnccl-net.so`library name to load.
`init`
As soon as NCCL finds the plugin and the correct ncclNet symbol, it will call the `init` function.
This will allow the plugin to discover network devices and make sure they are usable. If the
`init` function does not return `ncclSuccess`, then NCCL will not use the plugin and fall back on
internal ones.
To allow the plugin logs to integrate into the NCCL logs seemlessly, NCCL provides a logging
function to `init`. This function is typically used to allow for `INFO` and `WARN` macros within
the plugin code adding the following definitions:
```
#define WARN(...) logFunction(NCCL_LOG_WARN, NCCL_ALL, __FILE__, __LINE__, __VA_ARGS__)
#define INFO(FLAGS, ...) logFunction(NCCL_LOG_INFO, (FLAGS), __func__, __LINE__, __VA_ARGS__)
```
The `ncclProfilerCallback_t` argument is a NCCL core callback that allows the plugin to define and
record its own events with the NCCL profiler plugin.
`devices`
Once the plugin is initialized, NCCL will query the number of devices available. It should not
be zero, otherwise NCCL initialization will fail. If no device is present or usable, the `init`
function should not return `ncclSuccess`.
`getProperties`
Right after getting the number of devices, NCCL will query properties for each available network
device. These properties are critical when multiple adapters are present to ensure NCCL uses each
adapter in the most optimized way.
The `name` is only used for logging.
The `pciPath` is the base for all topology detection and should point to the PCI device directory
in /sys. This is typically the directory pointed by `/sys/class/net/eth0/device` or
`/sys/class/infiniband/mlx5_0/device`. If the network interface is virtual, then `pciPath` should
be `NULL`.
The `guid` field is used to determine when network adapters are connected to multiple PCI
endpoints. For normal cases, it can be set to the device number. If multiple network devices have
the same guid, then NCCL will consider the are sharing the same network port to the fabric, hence
it will not use the port multiple times.
The `ptrSupport` field indicates whether or not CUDA pointers are supported. If so, it should be
set to `NCCL_PTR_HOST|NCCL_PTR_CUDA`, otherwise it should be set to `NCCL_PTR_HOST`. If the plugin
supports `dmabuf`, it should set `ptrSupport` to `NCCL_PTR_HOST|NCCL_PTR_CUDA|NCCL_PTR_DMABUF` and
provide a `regMrDmaBuf` function.
The `regIsGlobal` field allows NCCL to register buffers in advance using e.g. a loopback connection
and later on, expect that another registration on a buffer contained within a previous registration
will be nearly immediate, as the buffer is already known by the network adapter. A typical
implementation would maintain a registration cache; the call to ncclCommRegister will create the
initial entry in the cache using regMr() on a loopback connection. Any later call to NCCL
operations will call regMr() again on the real connection, with the real buffer (could be at a
different offset within the original buffer, with a smaller size, etc), then deregMr() right after.
The call to ncclCommDeregister should call the final deregMr() and effectively remove the mapping
on the network adapter.
The `forceFlush` field can request the NCCL core to call flush for all transfers. By default,
flushes are only called when the GPU architecture or PCI topology would not not guarantee correct
PCI ordering. Plugins can set it to one if the NIC operates in a mode where e.g. the data and the
completion paths use different PCI links and therefore need a call to flush() to guarantee
ordering.
The `speed` field indicates the speed of the network port in Mbps (10^6 bits per second). This is
important to ensure proper optimization of flows within the node.
The `port` field indicates the port number. This is important again for topology detection and flow
optimization within the node when a NIC with a single PCI connection is connected to the fabric
with multiple ports.
The `latency` field indicates the network latency in microseconds. This can be useful to improve
the NCCL tuning and make sure NCCL switches from tree to ring at the right size.
The `maxComms` field indicates the maximum number of connections we can create.
The `maxRecvs` field indicates the maximum number for grouped receive operations (see grouped
receive).
The `netDeviceType` indicates which type of device networking this plugin supports. The current supported
options are `NCCL_NET_DEVICE_HOST` and `NCCL_NET_DEVICE_UNPACK`.
The `netDeviceVersion` indicates the version of device networking this plugin supports. Currently, this must match the associated netDeviceVersion of this netDeviceType compiled into NCCL core. Net device functionality is built as apart of NCCL core's device code.
The `maxP2pBytes` and `maxCollBytes` fields indicate the maximum size the plugin can handle for
point-to-point and collective calls. This will tell the NCCL core to cut large operations into
multiple smaller chunks if needed.
`vProps` is the list of devices that have been fused into the current device. Each entry is an index pointing to the child device.
### Connection establishment
Connections are used in an unidirectional manner. There is therefore a sender side and a receiver
side.
`listen`
To create a connection, NCCL will start by calling `listen` on the receiver side. This function
takes a device number as input argument, and should return a local `listenComm` object, and a
`handle` to pass to the other side, so that the sender side can connect to the receiver.
The `handle` is a buffer of size `NCCL_NET_HANDLE_MAXSIZE` and is provided by NCCL.
This call should never block, but contrary to `connect` and `accept`, `listenComm` should never
be `NULL` if the call succeeds.
`connect`
NCCL will use its bootstrap infrastructure to provide the `handle` to the sender side, then call
`connect` on the sender side on a given device index `dev`, providing the `handle`. `connect`
should not block either, and instead set `sendComm` to `NULL` and return `ncclSuccess`. In that
case, NCCL will call `accept` again until it succeeds.
`accept`
To finalize the connection, the receiver side will call `accept` on the `listenComm` returned by
the `listen` call previously. If the sender did not connect yet, `accept` should not block. It
should return `ncclSuccess`, setting `recvComm` to `NULL`. NCCL will call `accept` again until it
succeeds.
The `connect` API takes a `ncclNetCommConfig_t`, which contains a trafficClass field.
This field can be used by the network plugin to specify the QoS level of the connection. By default,
`trafficClass` is set to -1 but can be configured by the application during communicator initialization
to select a plugin-supported QoS level.
`closeListen`/`closeSend`/`closeRecv`
Once a `listenComm`/`sendComm`/`recvComm` is no longer needed, NCCL will call
`closeListen`/`closeSend`/`closeRecv` to free the associated resources.
### Communication
Communication is done using asynchronous send and receive operations: `isend`, `irecv` and `test`.
To support RDMA capabilities, buffer registration and flush functions are provided.
To keep track of asynchronous send, receive and flush operations, requests are returned to NCCL,
then queried with `test`. Each `sendComm` or `recvComm` must be able to handle
`NCCL_NET_MAX_REQUESTS` requests in parallel.
Note: That value should be multiplied by the multi-receive capability of the plugin for the sender
side, so that we can effectively have `NCCL_NET_MAX_REQUESTS` multi-receive operations happening
in parallel. So, if we have a `maxRecvs`value of 8 and `NCCL_NET_MAX_REQUESTS` is 8, then each
`sendComm` must be able to handle up to 8x8=64 concurrent `isend` operations.
`regMr`
Prior to sending or receiving data, NCCL will call `regMr` with any buffers later used for
communication. It will provide a `sendComm` or `recvComm` as `comm` argument, then the buffer
pointer `data`, `size`, and `type` being either `NCCL_PTR_HOST`, or `NCCL_PTR_CUDA` if the network
supports CUDA pointers.
The network plugin can use the output argument `mhandle` to keep any reference to that memory
registration, as this `mhandle` will be passed back for all `isend`, `irecv`, `iflush` and
`deregMr` calls.
`regMrDmaBuf`
If the plugin has set the `NCCL_PTR_DMABUF` property in `ptrSupport`, NCCL will use `regMrDmaBuf`
instead of `regMr`. If the property was not set, `regMrDmaBuf` can be set to `NULL`.
`deregMr`
When buffers will no longer be used for communication, NCCL will call `deregMr` to let the plugin
free resources. This function is used to deregister handles returned by both `regMr` and
`regMrDmaBuf`.
`isend`
Data will be sent through the connection using `isend`, passing the `sendComm` previously
created by `connect`, and the buffer described by `data`, `size`, and `mhandle`. A `tag` must be
used if the network supports multi-receive operations (see `irecv`) to distinguish between
different sends matching the same multi-receive. Otherwise it can be set to 0.
The `isend` operation returns a handle in the `request` argument for further calls to `test`. If
the `isend` operation cannot be initiated, `request` can be set to `NULL` and NCCL will call
`isend` again later.
The `pHandle` argument allows NCCL to pass an opaque handle that can be used by the network plugin
to support network defined events.
`irecv`
To receive data, NCCL will call `irecv` with the `recvComm` returned by `accept`. The argument
`n` will allow NCCL to perform a multi-receive, to allow grouping of multiple sends through a
single network connection. Each buffer will be described by the `data`, `sizes`, and `mhandles`
arrays. `tags` will specify a tag for each receive so that each of the `n` independent `isend`
operations is received into the right buffer.
If all receive operations can be initiated, `irecv` will return a handle in the `request` pointer,
otherwise it will set it to `NULL`. In the case of multi-receive, all `n` receive operations are
handled by a single request handle.
The sizes provided to `irecv` can (and will) be larger than the size of the `isend` operation.
The contrary (receive size being lower than the send size) is an error, however.
NCCL sets request pointer in `irecv` to `NCCL_NET_OPTIONAL_RECV_COMPLETION` when it is using
LL or LL128 protocols. In these cases, NCCL polls on flag embedded in data to detect completion
of irecv and is resilient to redundant network writes. This allows the plugin to optimize request
completions on such irecvs (for example, complete the request immediately). The plugin is still
expected to set a valid request pointer on return which NCCL can poll to check for completion.
The `pHandle` argument allows NCCL to pass an array of opaque handles that can be used by the
network plugin to support network defined events.
Note: for a given connection, send/receive operations should always match in the order they were
posted. Tags provided for receive operations are only used to assign a given send operation to one
of the buffers of the first (multi-)receive in the queue, not to allow for out-of-order tag
matching on any receive operation posted.
`test`
After an `isend` or `irecv` operation is initiated, NCCL will call `test` on the request handles
until they complete. When that happens, `done` will be set to 1 and `sizes` will be set to the
real size sent or received, the latter being potentially lower than the size passed to `irecv`.
In the case of a multi-receive, all receives will be considered as done as a single operation (the
goal being to allow aggregation), hence they share a single request and a single `done` status.
However, they can have different sizes, so when `done` is non-zero, the `sizes` array should
contain the `n` sizes corresponding to the buffers passed to `irecv`.
Once `test` returns 1 in `done`, the request handle can be freed, meaning that NCCL will never
call `test` again on that request (until it is reallocated by another call to `isend` or `irecv`).
`iflush`
After a receive operation completes, if the operation was targeting GPU memory and received a
non-zero number of bytes, NCCL will call `iflush` to let the network flush any buffer and ensure
the GPU can read it right after without seeing stale data. This flush operation is decoupled from
the `test` code to improve latency of `LL*` protocols, as those are capable of determining when
data is valid or not.
`iflush` returns a request which needs to be queried with `test` until it completes.

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@ -1,22 +0,0 @@
#
# Copyright (c) 2015-2019, NVIDIA CORPORATION. All rights reserved.
#
# See LICENSE.txt for license information
#
.DEFAULT_GOAL: build
include ../../makefiles/common.mk
SRCDIR ?= $(abspath ../..)
BUILDDIR ?= .
NCCLDIR := $(BUILDDIR)
SRC_FILES := $(wildcard *.c)
build: ${BUILDDIR}/libnccl-net-example.so
${BUILDDIR}/libnccl-net-example.so: ${SRC_FILES}
@printf "Compiling %-35s > %s\n" $< $@
@mkdir -p ${BUILDDIR}
$(CC) -Inccl -fPIC -shared -o $@ $^
clean:
rm -f ${BUILDDIR}/libnccl-net-example.so

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@ -1,21 +0,0 @@
/*************************************************************************
* Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#ifndef COMMON_H_
#define COMMON_H_
#include <stdint.h>
typedef enum {NCCL_LOG_NONE=0, NCCL_LOG_VERSION=1, NCCL_LOG_WARN=2, NCCL_LOG_INFO=3, NCCL_LOG_ABORT=4, NCCL_LOG_TRACE=5} ncclDebugLogLevel;
typedef enum {NCCL_INIT=1, NCCL_COLL=2, NCCL_P2P=4, NCCL_SHM=8, NCCL_NET=16, NCCL_GRAPH=32, NCCL_TUNING=64, NCCL_ENV=128, NCCL_ALLOC=256, NCCL_CALL=512, NCCL_PROXY=1024, NCCL_NVLS=2048, NCCL_BOOTSTRAP=4096, NCCL_REG=8192, NCCL_ALL=~0} ncclDebugLogSubSys;
typedef void (*ncclDebugLogger_t)(ncclDebugLogLevel level, unsigned long flags, const char *file, int line, const char *fmt, ...);
enum { ncclProfilerNetEventStart = 0, ncclProfilerNetEventStop, ncclProfilerNetEventUpdate, ncclProfilerNetEventUpdateAndStop };
typedef ncclResult_t (*ncclProfilerCallback_t)(void** eHandle, int type, void* phandle, int64_t pluginId, void* extData);
#endif

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@ -1,17 +0,0 @@
/*
* Copyright (c) 2017-2022, NVIDIA CORPORATION. All rights reserved.
*/
#ifndef NCCL_ERR_H_
#define NCCL_ERR_H_
/* Error type for plugins */
typedef enum { ncclSuccess = 0,
ncclUnhandledCudaError = 1,
ncclSystemError = 2,
ncclInternalError = 3,
ncclInvalidArgument = 4,
ncclInvalidUsage = 5,
ncclRemoteError = 6 } ncclResult_t;
#endif

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@ -1,41 +0,0 @@
/*
* Copyright (c) 2017-2022, NVIDIA CORPORATION. All rights reserved.
*/
#ifndef NET_H_
#define NET_H_
#include <stdint.h>
#include <stdlib.h>
#include "err.h"
#include "net_device.h"
#include "common.h"
#define NCCL_NET_HANDLE_MAXSIZE 128
#define NCCL_MAX_NET_SIZE_BYTES (1*1024*1024*1024*1024L) //1TB
#define NCCL_NET_OPTIONAL_RECV_COMPLETION 0x1
#define NCCL_PTR_HOST 0x1
#define NCCL_PTR_CUDA 0x2
#define NCCL_PTR_DMABUF 0x4
// Maximum number of requests per comm object
#define NCCL_NET_MAX_REQUESTS 32
#include "net_v10.h"
#include "net_v9.h"
#include "net_v8.h"
#include "net_v7.h"
#include "net_v6.h"
#include "net_v5.h"
#include "net_v4.h"
#include "net_v3.h"
#include "net_v2.h"
typedef ncclNet_v10_t ncclNet_t;
typedef ncclNetProperties_v10_t ncclNetProperties_t;
typedef ncclNetVDeviceProps_v10_t ncclNetVDeviceProps_t;
typedef ncclNetCommConfig_v10_t ncclNetCommConfig_t;
#endif // end include guard

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/*************************************************************************
* Copyright (c) 2023-2023, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#ifndef NET_DEVICE_H_
#define NET_DEVICE_H_
#define NCCL_NET_DEVICE_INVALID_VERSION 0x0
#define NCCL_NET_MTU_SIZE 4096
// Arbitrary version number - A given NCCL build will only be compatible with a single device networking plugin
// version. NCCL will check the supplied version number from net->getProperties() and compare to its internal version.
#define NCCL_NET_DEVICE_UNPACK_VERSION 0x7
typedef enum {NCCL_NET_DEVICE_HOST=0, NCCL_NET_DEVICE_UNPACK=1} ncclNetDeviceType;
typedef struct {
ncclNetDeviceType netDeviceType; // Network offload type
int netDeviceVersion; // Version number for network offload
void* handle;
size_t size;
int needsProxyProgress;
} ncclNetDeviceHandle_v7_t;
typedef ncclNetDeviceHandle_v7_t ncclNetDeviceHandle_v8_t;
typedef ncclNetDeviceHandle_v8_t ncclNetDeviceHandle_v9_t;
typedef ncclNetDeviceHandle_v9_t ncclNetDeviceHandle_v10_t;
typedef ncclNetDeviceHandle_v10_t ncclNetDeviceHandle_t;
#endif

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/*
* Copyright (c) 2017-2022, NVIDIA CORPORATION. All rights reserved.
*/
#ifndef NET_V10_H_
#define NET_V10_H_
#define NCCL_NET_MAX_DEVS_PER_NIC_V10 4
typedef struct {
int ndevs;
int devs[NCCL_NET_MAX_DEVS_PER_NIC_V10];
} ncclNetVDeviceProps_v10_t;
#define NCCL_NET_TRAFFIC_CLASS_UNDEF -1
typedef struct {
// Plugin-specific TC value
int trafficClass;
} ncclNetCommConfig_v10_t;
typedef struct {
char* name; // Used mostly for logging.
char* pciPath; // Path to the PCI device in /sys.
uint64_t guid; // Unique identifier for the NIC chip. Important for
// cards with multiple PCI functions (Physical or virtual).
int ptrSupport; // [NCCL_PTR_HOST|NCCL_PTR_CUDA|NCCL_PTR_DMABUF]
int regIsGlobal; // regMr is not tied to a particular comm
int forceFlush; // Force a flush on receives
int speed; // Port speed in Mbps.
int port; // Port number.
float latency; // Network latency
int maxComms; // Maximum number of comms we can create
int maxRecvs; // Maximum number of grouped receives.
ncclNetDeviceType netDeviceType; // Network offload type
int netDeviceVersion; // Version number for network offload
ncclNetVDeviceProps_v10_t vProps;
size_t maxP2pBytes; // Max transfer size for point-to-point operations
size_t maxCollBytes; // Max transfer size for collective operations
} ncclNetProperties_v10_t;
typedef struct {
// Name of the network (mainly for logs)
const char* name;
// Initialize the network.
ncclResult_t (*init)(ncclDebugLogger_t logFunction, ncclProfilerCallback_t profFunction);
// Return the number of adapters.
ncclResult_t (*devices)(int* ndev);
// Get various device properties.
ncclResult_t (*getProperties)(int dev, ncclNetProperties_v10_t* props);
// Create a receiving object and provide a handle to connect to it. The
// handle can be up to NCCL_NET_HANDLE_MAXSIZE bytes and will be exchanged
// between ranks to create a connection.
ncclResult_t (*listen)(int dev, void* handle, void** listenComm);
// Connect to a handle and return a sending comm object for that peer.
// This call must not block for the connection to be established, and instead
// should return successfully with sendComm == NULL with the expectation that
// it will be called again until sendComm != NULL.
// If *sendDevComm points to a valid object, then NCCL is requesting device offload for this connection
ncclResult_t (*connect)(int dev, ncclNetCommConfig_v10_t* config, void* handle, void** sendComm, ncclNetDeviceHandle_v10_t** sendDevComm);
// Finalize connection establishment after remote peer has called connect.
// This call must not block for the connection to be established, and instead
// should return successfully with recvComm == NULL with the expectation that
// it will be called again until recvComm != NULL.
// If *recvDevComm points to a valid object, then NCCL is requesting device offload for this connection
ncclResult_t (*accept)(void* listenComm, void** recvComm, ncclNetDeviceHandle_v10_t** recvDevComm);
// Register/Deregister memory. Comm can be either a sendComm or a recvComm.
// Type is either NCCL_PTR_HOST or NCCL_PTR_CUDA.
ncclResult_t (*regMr)(void* comm, void* data, size_t size, int type, void** mhandle);
/* DMA-BUF support */
ncclResult_t (*regMrDmaBuf)(void* comm, void* data, size_t size, int type, uint64_t offset, int fd, void** mhandle);
ncclResult_t (*deregMr)(void* comm, void* mhandle);
// Asynchronous send to a peer.
// May return request == NULL if the call cannot be performed (or would block)
ncclResult_t (*isend)(void* sendComm, void* data, size_t size, int tag, void* mhandle, void* phandle, void** request);
// Asynchronous recv from a peer.
// May return request == NULL if the call cannot be performed (or would block)
ncclResult_t (*irecv)(void* recvComm, int n, void** data, size_t* sizes, int* tags, void** mhandles, void** phandles, void** request);
// Perform a flush/fence to make sure all data received with NCCL_PTR_CUDA is
// visible to the GPU
ncclResult_t (*iflush)(void* recvComm, int n, void** data, int* sizes, void** mhandles, void** request);
// Test whether a request is complete. If size is not NULL, it returns the
// number of bytes sent/received.
ncclResult_t (*test)(void* request, int* done, int* sizes);
// Close and free send/recv comm objects
ncclResult_t (*closeSend)(void* sendComm);
ncclResult_t (*closeRecv)(void* recvComm);
ncclResult_t (*closeListen)(void* listenComm);
// Copy the given mhandle to a dptr in a format usable by this plugin's device code
ncclResult_t (*getDeviceMr)(void* comm, void* mhandle, void** dptr_mhandle);
// Notify the plugin that a recv has completed by the device
ncclResult_t (*irecvConsumed)(void* recvComm, int n, void* request);
// Virtual NIC APIs. makeVDevice will create a virtual NIC given the specified properties, and tell the caller
// what index this new vNIC exists at
ncclResult_t (*makeVDevice)(int* d, ncclNetVDeviceProps_v10_t* props);
} ncclNet_v10_t;
#endif // end include guard

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/*
* Copyright (c) 2017-2022, NVIDIA CORPORATION. All rights reserved.
*/
#ifndef NET_V2_H_
#define NET_V2_H_
typedef struct {
// Name of the network (mainly for logs)
const char* name;
// Initialize the network.
ncclResult_t (*init)(ncclDebugLogger_t logFunction);
// Return the number of adapters.
ncclResult_t (*devices)(int* ndev);
// Return the device path in /sys. NCCL will call free on this path.
ncclResult_t (*pciPath)(int dev, char** path);
// Return whether this device supports host pointers and/or CUDA pointers
// as data from the current GPU. Supported types should be composed with
// NCCL_PTR_HOST and NCCL_PTR_CUDA.
ncclResult_t (*ptrSupport)(int dev, int* supportedTypes);
// Create a receiving object and provide a handle to connect to it. The
// handle can be up to NCCL_NET_HANDLE_MAXSIZE bytes and will be exchanged
// between ranks to create a connection.
ncclResult_t (*listen)(int dev, void* handle, void** listenComm);
// Connect to a handle and return a sending comm object for that peer.
ncclResult_t (*connect)(int dev, void* handle, void** sendComm);
// Finalize connection establishment after remote peer has called connectHandle
ncclResult_t (*accept)(void* listenComm, void** recvComm);
// Register/Deregister memory. Comm can be either a sendComm or a recvComm.
ncclResult_t (*regMr)(void* comm, void* data, int size, int type, void** mhandle);
ncclResult_t (*deregMr)(void* comm, void* mhandle);
// Asynchronous send to a peer. Type is either NCCL_PTR_HOST or NCCL_PTR_CUDA.
// May return request == NULL if the call cannot be performed (or would block)
ncclResult_t (*isend)(void* sendComm, void* data, int size, void* mhandle, void** request);
// Asynchronous recv from a peer. Type is either NCCL_PTR_HOST or NCCL_PTR_CUDA.
// May return request == NULL if the call cannot be performed (or would block)
ncclResult_t (*irecv)(void* recvComm, void* data, int size, void* mhandle, void** request);
// Perform a flush/fence to make sure all data received with NCCL_PTR_CUDA is
// visible to the GPU
ncclResult_t (*flush)(void* recvComm, void* data, int size, void* mhandle);
// Test whether a request is complete. If size is not NULL, it returns the
// number of bytes sent/received.
ncclResult_t (*test)(void* request, int* done, int* size);
// Close and free send/recv comm objects
ncclResult_t (*closeSend)(void* sendComm);
ncclResult_t (*closeRecv)(void* recvComm);
ncclResult_t (*closeListen)(void* listenComm);
} ncclNet_v2_t;
#endif // end include guard

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/*
* Copyright (c) 2017-2022, NVIDIA CORPORATION. All rights reserved.
*/
#ifndef NET_V3_H_
#define NET_V3_H_
#define NCCL_NET_MAX_REQUESTS_V3 16
typedef ncclNetProperties_v4_t ncclNetProperties_v3_t;
typedef struct {
// Name of the network (mainly for logs)
const char* name;
// Initialize the network.
ncclResult_t (*init)(ncclDebugLogger_t logFunction);
// Return the number of adapters.
ncclResult_t (*devices)(int* ndev);
// Get various device properties.
ncclResult_t (*getProperties)(int dev, ncclNetProperties_v3_t* props);
// Create a receiving object and provide a handle to connect to it. The
// handle can be up to NCCL_NET_HANDLE_MAXSIZE bytes and will be exchanged
// between ranks to create a connection.
ncclResult_t (*listen)(int dev, void* handle, void** listenComm);
// Connect to a handle and return a sending comm object for that peer.
ncclResult_t (*connect)(int dev, void* handle, void** sendComm);
// Finalize connection establishment after remote peer has called connectHandle
ncclResult_t (*accept)(void* listenComm, void** recvComm);
// Register/Deregister memory. Comm can be either a sendComm or a recvComm.
// Type is either NCCL_PTR_HOST or NCCL_PTR_CUDA.
ncclResult_t (*regMr)(void* comm, void* data, int size, int type, void** mhandle);
ncclResult_t (*deregMr)(void* comm, void* mhandle);
// Asynchronous send to a peer.
// May return request == NULL if the call cannot be performed (or would block)
ncclResult_t (*isend)(void* sendComm, void* data, int size, void* mhandle, void** request);
// Asynchronous recv from a peer.
// May return request == NULL if the call cannot be performed (or would block)
ncclResult_t (*irecv)(void* recvComm, void* data, int size, void* mhandle, void** request);
// Perform a flush/fence to make sure all data received with NCCL_PTR_CUDA is
// visible to the GPU
ncclResult_t (*flush)(void* recvComm, void* data, int size, void* mhandle);
// Test whether a request is complete. If size is not NULL, it returns the
// number of bytes sent/received.
ncclResult_t (*test)(void* request, int* done, int* size);
// Close and free send/recv comm objects
ncclResult_t (*closeSend)(void* sendComm);
ncclResult_t (*closeRecv)(void* recvComm);
ncclResult_t (*closeListen)(void* listenComm);
} ncclNet_v3_t;
#endif // end include guard

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/*
* Copyright (c) 2017-2022, NVIDIA CORPORATION. All rights reserved.
*/
#ifndef NET_V4_H_
#define NET_V4_H_
#define NCCL_NET_HANDLE_MAXSIZE_V4 64
typedef struct {
char* name; // Used mostly for logging.
char* pciPath; // Path to the PCI device in /sys.
uint64_t guid; // Unique identifier for the NIC chip. Important for
// cards with multiple PCI functions (Physical or virtual).
int ptrSupport; // NCCL_PTR_HOST or NCCL_PTR_HOST|NCCL_PTR_CUDA
int speed; // Port speed in Mbps.
int port; // Port number.
int maxComms; // Maximum number of comms we can create
} ncclNetProperties_v4_t;
// v4 struct for backwards compatibility
typedef struct {
// Name of the network (mainly for logs)
const char* name;
// Initialize the network.
ncclResult_t (*init)(ncclDebugLogger_t logFunction);
// Return the number of adapters.
ncclResult_t (*devices)(int* ndev);
// Get various device properties.
ncclResult_t (*getProperties)(int dev, ncclNetProperties_v4_t* props);
// Create a receiving object and provide a handle to connect to it. The
// handle can be up to NCCL_NET_HANDLE_MAXSIZE bytes and will be exchanged
// between ranks to create a connection.
ncclResult_t (*listen)(int dev, void* handle, void** listenComm);
// Connect to a handle and return a sending comm object for that peer.
ncclResult_t (*connect)(int dev, void* handle, void** sendComm);
// Finalize connection establishment after remote peer has called connectHandle
ncclResult_t (*accept)(void* listenComm, void** recvComm);
// Register/Deregister memory. Comm can be either a sendComm or a recvComm.
// Type is either NCCL_PTR_HOST or NCCL_PTR_CUDA.
ncclResult_t (*regMr)(void* comm, void* data, int size, int type, void** mhandle);
ncclResult_t (*deregMr)(void* comm, void* mhandle);
// Asynchronous send to a peer.
// May return request == NULL if the call cannot be performed (or would block)
ncclResult_t (*isend)(void* sendComm, void* data, int size, void* mhandle, void** request);
// Asynchronous recv from a peer.
// May return request == NULL if the call cannot be performed (or would block)
ncclResult_t (*irecv)(void* recvComm, void* data, int size, void* mhandle, void** request);
// Perform a flush/fence to make sure all data received with NCCL_PTR_CUDA is
// visible to the GPU
ncclResult_t (*iflush)(void* recvComm, void* data, int size, void* mhandle, void** request);
// Test whether a request is complete. If size is not NULL, it returns the
// number of bytes sent/received.
ncclResult_t (*test)(void* request, int* done, int* size);
// Close and free send/recv comm objects
ncclResult_t (*closeSend)(void* sendComm);
ncclResult_t (*closeRecv)(void* recvComm);
ncclResult_t (*closeListen)(void* listenComm);
} ncclNet_v4_t;
#endif // end include guard

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/*
* Copyright (c) 2017-2022, NVIDIA CORPORATION. All rights reserved.
*/
#ifndef NET_V5_H_
#define NET_V5_H_
typedef ncclNetProperties_v6_t ncclNetProperties_v5_t;
typedef struct {
// Name of the network (mainly for logs)
const char* name;
// Initialize the network.
ncclResult_t (*init)(ncclDebugLogger_t logFunction);
// Return the number of adapters.
ncclResult_t (*devices)(int* ndev);
// Get various device properties.
ncclResult_t (*getProperties)(int dev, ncclNetProperties_v5_t* props);
// Create a receiving object and provide a handle to connect to it. The
// handle can be up to NCCL_NET_HANDLE_MAXSIZE bytes and will be exchanged
// between ranks to create a connection.
ncclResult_t (*listen)(int dev, void* handle, void** listenComm);
// Connect to a handle and return a sending comm object for that peer.
// This call must not block for the connection to be established, and instead
// should return successfully with sendComm == NULL with the expectation that
// it will be called again until sendComm != NULL.
ncclResult_t (*connect)(int dev, void* handle, void** sendComm);
// Finalize connection establishment after remote peer has called connect.
// This call must not block for the connection to be established, and instead
// should return successfully with recvComm == NULL with the expectation that
// it will be called again until recvComm != NULL.
ncclResult_t (*accept)(void* listenComm, void** recvComm);
// Register/Deregister memory. Comm can be either a sendComm or a recvComm.
// Type is either NCCL_PTR_HOST or NCCL_PTR_CUDA.
ncclResult_t (*regMr)(void* comm, void* data, int size, int type, void** mhandle);
ncclResult_t (*deregMr)(void* comm, void* mhandle);
// Asynchronous send to a peer.
// May return request == NULL if the call cannot be performed (or would block)
ncclResult_t (*isend)(void* sendComm, void* data, int size, int tag, void* mhandle, void** request);
// Asynchronous recv from a peer.
// May return request == NULL if the call cannot be performed (or would block)
ncclResult_t (*irecv)(void* recvComm, int n, void** data, int* sizes, int* tags, void** mhandles, void** request);
// Perform a flush/fence to make sure all data received with NCCL_PTR_CUDA is
// visible to the GPU
ncclResult_t (*iflush)(void* recvComm, int n, void** data, int* sizes, void** mhandles, void** request);
// Test whether a request is complete. If size is not NULL, it returns the
// number of bytes sent/received.
ncclResult_t (*test)(void* request, int* done, int* sizes);
// Close and free send/recv comm objects
ncclResult_t (*closeSend)(void* sendComm);
ncclResult_t (*closeRecv)(void* recvComm);
ncclResult_t (*closeListen)(void* listenComm);
} ncclNet_v5_t;
#endif // end include guard

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/*
* Copyright (c) 2017-2022, NVIDIA CORPORATION. All rights reserved.
*/
#ifndef NET_V6_H_
#define NET_V6_H_
typedef struct {
char* name; // Used mostly for logging.
char* pciPath; // Path to the PCI device in /sys.
uint64_t guid; // Unique identifier for the NIC chip. Important for
// cards with multiple PCI functions (Physical or virtual).
int ptrSupport; // [NCCL_PTR_HOST|NCCL_PTR_CUDA|NCCL_PTR_DMABUF]
int speed; // Port speed in Mbps.
int port; // Port number.
float latency; // Network latency
int maxComms; // Maximum number of comms we can create
int maxRecvs; // Maximum number of grouped receives.
}ncclNetProperties_v6_t;
typedef struct {
// Name of the network (mainly for logs)
const char* name;
// Initialize the network.
ncclResult_t (*init)(ncclDebugLogger_t logFunction);
// Return the number of adapters.
ncclResult_t (*devices)(int* ndev);
// Get various device properties.
ncclResult_t (*getProperties)(int dev, ncclNetProperties_v6_t* props);
// Create a receiving object and provide a handle to connect to it. The
// handle can be up to NCCL_NET_HANDLE_MAXSIZE bytes and will be exchanged
// between ranks to create a connection.
ncclResult_t (*listen)(int dev, void* handle, void** listenComm);
// Connect to a handle and return a sending comm object for that peer.
// This call must not block for the connection to be established, and instead
// should return successfully with sendComm == NULL with the expectation that
// it will be called again until sendComm != NULL.
ncclResult_t (*connect)(int dev, void* handle, void** sendComm);
// Finalize connection establishment after remote peer has called connect.
// This call must not block for the connection to be established, and instead
// should return successfully with recvComm == NULL with the expectation that
// it will be called again until recvComm != NULL.
ncclResult_t (*accept)(void* listenComm, void** recvComm);
// Register/Deregister memory. Comm can be either a sendComm or a recvComm.
// Type is either NCCL_PTR_HOST or NCCL_PTR_CUDA.
ncclResult_t (*regMr)(void* comm, void* data, int size, int type, void** mhandle);
/* DMA-BUF support */
ncclResult_t (*regMrDmaBuf)(void* comm, void* data, size_t size, int type, uint64_t offset, int fd, void** mhandle);
ncclResult_t (*deregMr)(void* comm, void* mhandle);
// Asynchronous send to a peer.
// May return request == NULL if the call cannot be performed (or would block)
ncclResult_t (*isend)(void* sendComm, void* data, int size, int tag, void* mhandle, void** request);
// Asynchronous recv from a peer.
// May return request == NULL if the call cannot be performed (or would block)
ncclResult_t (*irecv)(void* recvComm, int n, void** data, int* sizes, int* tags, void** mhandles, void** request);
// Perform a flush/fence to make sure all data received with NCCL_PTR_CUDA is
// visible to the GPU
ncclResult_t (*iflush)(void* recvComm, int n, void** data, int* sizes, void** mhandles, void** request);
// Test whether a request is complete. If size is not NULL, it returns the
// number of bytes sent/received.
ncclResult_t (*test)(void* request, int* done, int* sizes);
// Close and free send/recv comm objects
ncclResult_t (*closeSend)(void* sendComm);
ncclResult_t (*closeRecv)(void* recvComm);
ncclResult_t (*closeListen)(void* listenComm);
} ncclNet_v6_t;
#endif // end include guard

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/*
* Copyright (c) 2017-2022, NVIDIA CORPORATION. All rights reserved.
*/
#ifndef NET_V7_H_
#define NET_V7_H_
typedef struct {
char* name; // Used mostly for logging.
char* pciPath; // Path to the PCI device in /sys.
uint64_t guid; // Unique identifier for the NIC chip. Important for
// cards with multiple PCI functions (Physical or virtual).
int ptrSupport; // [NCCL_PTR_HOST|NCCL_PTR_CUDA|NCCL_PTR_DMABUF]
int speed; // Port speed in Mbps.
int port; // Port number.
float latency; // Network latency
int maxComms; // Maximum number of comms we can create
int maxRecvs; // Maximum number of grouped receives.
ncclNetDeviceType netDeviceType; // Network offload type
int netDeviceVersion; // Version number for network offload
} ncclNetProperties_v7_t;
typedef struct {
// Name of the network (mainly for logs)
const char* name;
// Initialize the network.
ncclResult_t (*init)(ncclDebugLogger_t logFunction);
// Return the number of adapters.
ncclResult_t (*devices)(int* ndev);
// Get various device properties.
ncclResult_t (*getProperties)(int dev, ncclNetProperties_v7_t* props);
// Create a receiving object and provide a handle to connect to it. The
// handle can be up to NCCL_NET_HANDLE_MAXSIZE bytes and will be exchanged
// between ranks to create a connection.
ncclResult_t (*listen)(int dev, void* handle, void** listenComm);
// Connect to a handle and return a sending comm object for that peer.
// This call must not block for the connection to be established, and instead
// should return successfully with sendComm == NULL with the expectation that
// it will be called again until sendComm != NULL.
ncclResult_t (*connect)(int dev, void* handle, void** sendComm, ncclNetDeviceHandle_v7_t** sendDevComm);
// Finalize connection establishment after remote peer has called connect.
// This call must not block for the connection to be established, and instead
// should return successfully with recvComm == NULL with the expectation that
// it will be called again until recvComm != NULL.
ncclResult_t (*accept)(void* listenComm, void** recvComm, ncclNetDeviceHandle_v7_t** recvDevComm);
// Register/Deregister memory. Comm can be either a sendComm or a recvComm.
// Type is either NCCL_PTR_HOST or NCCL_PTR_CUDA.
ncclResult_t (*regMr)(void* comm, void* data, int size, int type, void** mhandle);
/* DMA-BUF support */
ncclResult_t (*regMrDmaBuf)(void* comm, void* data, size_t size, int type, uint64_t offset, int fd, void** mhandle);
ncclResult_t (*deregMr)(void* comm, void* mhandle);
// Asynchronous send to a peer.
// May return request == NULL if the call cannot be performed (or would block)
ncclResult_t (*isend)(void* sendComm, void* data, int size, int tag, void* mhandle, void** request);
// Asynchronous recv from a peer.
// May return request == NULL if the call cannot be performed (or would block)
ncclResult_t (*irecv)(void* recvComm, int n, void** data, int* sizes, int* tags, void** mhandles, void** request);
// Perform a flush/fence to make sure all data received with NCCL_PTR_CUDA is
// visible to the GPU
ncclResult_t (*iflush)(void* recvComm, int n, void** data, int* sizes, void** mhandles, void** request);
// Test whether a request is complete. If size is not NULL, it returns the
// number of bytes sent/received.
ncclResult_t (*test)(void* request, int* done, int* sizes);
// Close and free send/recv comm objects
ncclResult_t (*closeSend)(void* sendComm);
ncclResult_t (*closeRecv)(void* recvComm);
ncclResult_t (*closeListen)(void* listenComm);
// Copy the given mhandle to a dptr in a format usable by this plugin's device code
ncclResult_t (*getDeviceMr)(void* comm, void* mhandle, void** dptr_mhandle);
// Notify the plugin that a recv has completed by the device
ncclResult_t (*irecvConsumed)(void* recvComm, int n, void* request);
} ncclNet_v7_t;
#endif // end include guard

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/*
* Copyright (c) 2017-2022, NVIDIA CORPORATION. All rights reserved.
*/
#ifndef NET_V8_H_
#define NET_V8_H_
typedef struct {
char* name; // Used mostly for logging.
char* pciPath; // Path to the PCI device in /sys.
uint64_t guid; // Unique identifier for the NIC chip. Important for
// cards with multiple PCI functions (Physical or virtual).
int ptrSupport; // [NCCL_PTR_HOST|NCCL_PTR_CUDA|NCCL_PTR_DMABUF]
int regIsGlobal; // regMr is not tied to a particular comm
int speed; // Port speed in Mbps.
int port; // Port number.
float latency; // Network latency
int maxComms; // Maximum number of comms we can create
int maxRecvs; // Maximum number of grouped receives.
ncclNetDeviceType netDeviceType; // Network offload type
int netDeviceVersion; // Version number for network offload
} ncclNetProperties_v8_t;
typedef struct {
// Name of the network (mainly for logs)
const char* name;
// Initialize the network.
ncclResult_t (*init)(ncclDebugLogger_t logFunction);
// Return the number of adapters.
ncclResult_t (*devices)(int* ndev);
// Get various device properties.
ncclResult_t (*getProperties)(int dev, ncclNetProperties_v8_t* props);
// Create a receiving object and provide a handle to connect to it. The
// handle can be up to NCCL_NET_HANDLE_MAXSIZE bytes and will be exchanged
// between ranks to create a connection.
ncclResult_t (*listen)(int dev, void* handle, void** listenComm);
// Connect to a handle and return a sending comm object for that peer.
// This call must not block for the connection to be established, and instead
// should return successfully with sendComm == NULL with the expectation that
// it will be called again until sendComm != NULL.
// If *sendDevComm points to a valid object, then NCCL is requesting device offload for this connection
ncclResult_t (*connect)(int dev, void* handle, void** sendComm, ncclNetDeviceHandle_v8_t** sendDevComm);
// Finalize connection establishment after remote peer has called connect.
// This call must not block for the connection to be established, and instead
// should return successfully with recvComm == NULL with the expectation that
// it will be called again until recvComm != NULL.
// If *recvDevComm points to a valid object, then NCCL is requesting device offload for this connection
ncclResult_t (*accept)(void* listenComm, void** recvComm, ncclNetDeviceHandle_v8_t** recvDevComm);
// Register/Deregister memory. Comm can be either a sendComm or a recvComm.
// Type is either NCCL_PTR_HOST or NCCL_PTR_CUDA.
ncclResult_t (*regMr)(void* comm, void* data, size_t size, int type, void** mhandle);
/* DMA-BUF support */
ncclResult_t (*regMrDmaBuf)(void* comm, void* data, size_t size, int type, uint64_t offset, int fd, void** mhandle);
ncclResult_t (*deregMr)(void* comm, void* mhandle);
// Asynchronous send to a peer.
// May return request == NULL if the call cannot be performed (or would block)
ncclResult_t (*isend)(void* sendComm, void* data, int size, int tag, void* mhandle, void** request);
// Asynchronous recv from a peer.
// May return request == NULL if the call cannot be performed (or would block)
ncclResult_t (*irecv)(void* recvComm, int n, void** data, int* sizes, int* tags, void** mhandles, void** request);
// Perform a flush/fence to make sure all data received with NCCL_PTR_CUDA is
// visible to the GPU
ncclResult_t (*iflush)(void* recvComm, int n, void** data, int* sizes, void** mhandles, void** request);
// Test whether a request is complete. If size is not NULL, it returns the
// number of bytes sent/received.
ncclResult_t (*test)(void* request, int* done, int* sizes);
// Close and free send/recv comm objects
ncclResult_t (*closeSend)(void* sendComm);
ncclResult_t (*closeRecv)(void* recvComm);
ncclResult_t (*closeListen)(void* listenComm);
// Copy the given mhandle to a dptr in a format usable by this plugin's device code
ncclResult_t (*getDeviceMr)(void* comm, void* mhandle, void** dptr_mhandle);
// Notify the plugin that a recv has completed by the device
ncclResult_t (*irecvConsumed)(void* recvComm, int n, void* request);
} ncclNet_v8_t;
#endif // end include guard

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/*
* Copyright (c) 2017-2022, NVIDIA CORPORATION. All rights reserved.
*/
#ifndef NET_V9_H_
#define NET_V9_H_
#define NCCL_NET_MAX_DEVS_PER_NIC_V9 4
typedef struct {
int ndevs;
int devs[NCCL_NET_MAX_DEVS_PER_NIC_V9];
} ncclNetVDeviceProps_v9_t;
typedef struct {
char* name; // Used mostly for logging.
char* pciPath; // Path to the PCI device in /sys.
uint64_t guid; // Unique identifier for the NIC chip. Important for
// cards with multiple PCI functions (Physical or virtual).
int ptrSupport; // [NCCL_PTR_HOST|NCCL_PTR_CUDA|NCCL_PTR_DMABUF]
int regIsGlobal; // regMr is not tied to a particular comm
int forceFlush; // Force a flush on receives
int speed; // Port speed in Mbps.
int port; // Port number.
float latency; // Network latency
int maxComms; // Maximum number of comms we can create
int maxRecvs; // Maximum number of grouped receives.
ncclNetDeviceType netDeviceType; // Network offload type
int netDeviceVersion; // Version number for network offload
ncclNetVDeviceProps_v9_t vProps;
size_t maxP2pBytes; // Max transfer size for point-to-point operations
size_t maxCollBytes; // Max transfer size for collective operations
} ncclNetProperties_v9_t;
typedef struct {
// Name of the network (mainly for logs)
const char* name;
// Initialize the network.
ncclResult_t (*init)(ncclDebugLogger_t logFunction);
// Return the number of adapters.
ncclResult_t (*devices)(int* ndev);
// Get various device properties.
ncclResult_t (*getProperties)(int dev, ncclNetProperties_v9_t* props);
// Create a receiving object and provide a handle to connect to it. The
// handle can be up to NCCL_NET_HANDLE_MAXSIZE bytes and will be exchanged
// between ranks to create a connection.
ncclResult_t (*listen)(int dev, void* handle, void** listenComm);
// Connect to a handle and return a sending comm object for that peer.
// This call must not block for the connection to be established, and instead
// should return successfully with sendComm == NULL with the expectation that
// it will be called again until sendComm != NULL.
// If *sendDevComm points to a valid object, then NCCL is requesting device offload for this connection
ncclResult_t (*connect)(int dev, void* handle, void** sendComm, ncclNetDeviceHandle_v9_t** sendDevComm);
// Finalize connection establishment after remote peer has called connect.
// This call must not block for the connection to be established, and instead
// should return successfully with recvComm == NULL with the expectation that
// it will be called again until recvComm != NULL.
// If *recvDevComm points to a valid object, then NCCL is requesting device offload for this connection
ncclResult_t (*accept)(void* listenComm, void** recvComm, ncclNetDeviceHandle_v9_t** recvDevComm);
// Register/Deregister memory. Comm can be either a sendComm or a recvComm.
// Type is either NCCL_PTR_HOST or NCCL_PTR_CUDA.
ncclResult_t (*regMr)(void* comm, void* data, size_t size, int type, void** mhandle);
/* DMA-BUF support */
ncclResult_t (*regMrDmaBuf)(void* comm, void* data, size_t size, int type, uint64_t offset, int fd, void** mhandle);
ncclResult_t (*deregMr)(void* comm, void* mhandle);
// Asynchronous send to a peer.
// May return request == NULL if the call cannot be performed (or would block)
ncclResult_t (*isend)(void* sendComm, void* data, size_t size, int tag, void* mhandle, void** request);
// Asynchronous recv from a peer.
// May return request == NULL if the call cannot be performed (or would block)
ncclResult_t (*irecv)(void* recvComm, int n, void** data, size_t* sizes, int* tags, void** mhandles, void** request);
// Perform a flush/fence to make sure all data received with NCCL_PTR_CUDA is
// visible to the GPU
ncclResult_t (*iflush)(void* recvComm, int n, void** data, int* sizes, void** mhandles, void** request);
// Test whether a request is complete. If size is not NULL, it returns the
// number of bytes sent/received.
ncclResult_t (*test)(void* request, int* done, int* sizes);
// Close and free send/recv comm objects
ncclResult_t (*closeSend)(void* sendComm);
ncclResult_t (*closeRecv)(void* recvComm);
ncclResult_t (*closeListen)(void* listenComm);
// Copy the given mhandle to a dptr in a format usable by this plugin's device code
ncclResult_t (*getDeviceMr)(void* comm, void* mhandle, void** dptr_mhandle);
// Notify the plugin that a recv has completed by the device
ncclResult_t (*irecvConsumed)(void* recvComm, int n, void* request);
// Virtual NIC APIs. makeVDevice will create a virtual NIC given the specified properties, and tell the caller
// what index this new vNIC exists at
ncclResult_t (*makeVDevice)(int* d, ncclNetVDeviceProps_v9_t* props);
} ncclNet_v9_t;
#endif // end include guard

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/*
* Copyright (c) 2017-2022, NVIDIA CORPORATION. All rights reserved.
*/
#ifndef NCCL_TYPES_H_
#define NCCL_TYPES_H_
/* Data types */
typedef enum { ncclInt8 = 0, ncclChar = 0,
ncclUint8 = 1,
ncclInt32 = 2, ncclInt = 2,
ncclUint32 = 3,
ncclInt64 = 4,
ncclUint64 = 5,
ncclFloat16 = 6, ncclHalf = 6,
ncclFloat32 = 7, ncclFloat = 7,
ncclFloat64 = 8, ncclDouble = 8,
ncclBfloat16 = 9,
} ncclDataType_t;
#endif

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/*************************************************************************
* Copyright (c) 2015-2024, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#include "net.h"
#define __hidden __attribute__ ((visibility("hidden")))
#define NCCL_PLUGIN_MAX_RECVS 1
int max_requests = NCCL_NET_MAX_REQUESTS;
__hidden ncclResult_t pluginInit(ncclDebugLogger_t logFunction, ncclProfilerCallback_t profFunction) { return ncclSuccess; }
__hidden ncclResult_t pluginDevices(int* ndev) { *ndev = 0; return ncclSuccess; }
__hidden ncclResult_t pluginPciPath(int dev, char** path) { return ncclInternalError; }
__hidden ncclResult_t pluginPtrSupport(int dev, int* supportedTypes) { return ncclInternalError; }
__hidden ncclResult_t pluginGetProperties(int dev, ncclNetProperties_t* props) {
// Below are default values, if unsure don't change.
props->name = "Example";
// Fill for proper topology detection, e.g. /sys/devices/pci0000:00/0000:00:10.0/0000:0b:00.0
props->pciPath = NULL;
// Only used to detect NICs with multiple PCI attachments.
props->guid = 0;
// Add NCCL_PTR_CUDA if GPU Direct RDMA is supported and regMr can take CUDA pointers.
props->ptrSupport = NCCL_PTR_HOST;
// If you regMr has a fast registration cache, set to 1. If set to 0, user buffer registration may be disabled.
props->regIsGlobal = 0;
// Force flush after receive. Needed if the control path and data path use a different path to the GPU
props->forceFlush = 0;
// Speed in *Mbps*. 100000 means 100G
props->speed = 100000;
// Port number, used in conjunction with guid
props->port = 0;
// Custom latency (used to help tuning if latency is high. If set to 0, use default NCCL values.
props->latency = 0;
// Maximum number of comm objects we can create.
props->maxComms = 1024*1024;
// Maximum number of receive operations taken by irecv().
props->maxRecvs = NCCL_PLUGIN_MAX_RECVS;
// Coupling with NCCL network device-side code.
props->netDeviceType = NCCL_NET_DEVICE_HOST;
props->netDeviceVersion = NCCL_NET_DEVICE_INVALID_VERSION;
// Used to tell NCCL core whether this is a virtual device fusing multiple physical devices.
props->vProps.ndevs = 1;
props->vProps.devs[0] = dev;
// maximum transfer sizes the plugin can handle
props->maxP2pBytes = NCCL_MAX_NET_SIZE_BYTES;
props->maxCollBytes = NCCL_MAX_NET_SIZE_BYTES;
return ncclSuccess;
}
__hidden ncclResult_t pluginListen(int dev, void* handle, void** listenComm) { return ncclInternalError; }
__hidden ncclResult_t pluginConnect(int dev, ncclNetCommConfig_t* config, void* handle, void** sendComm, ncclNetDeviceHandle_t** sendDevComm) { return ncclInternalError; }
__hidden ncclResult_t pluginAccept(void* listenComm, void** recvComm, ncclNetDeviceHandle_t** recvDevComm) { return ncclInternalError; }
__hidden ncclResult_t pluginRegMr(void* collComm, void* data, size_t size, int type, void** mhandle) { return ncclInternalError; }
__hidden ncclResult_t pluginRegMrDmaBuf(void* collComm, void* data, size_t size, int type, uint64_t offset, int fd, void** mhandle) { return ncclInternalError; }
__hidden ncclResult_t pluginDeregMr(void* collComm, void* mhandle) { return ncclInternalError;}
__hidden ncclResult_t pluginIsend(void* sendComm, void* data, size_t size, int tag, void* mhandle, void* phandle, void** request) { return ncclInternalError; }
__hidden ncclResult_t pluginIrecv(void* recvComm, int n, void** data, size_t* sizes, int* tags, void** mhandles, void** phandles, void** request) { return ncclInternalError; }
__hidden ncclResult_t pluginIflush(void* recvComm, int n, void** data, int* sizes, void** mhandles, void** request) { return ncclInternalError; }
__hidden ncclResult_t pluginTest(void* request, int* done, int* size) { return ncclInternalError; }
__hidden ncclResult_t pluginCloseSend(void* sendComm) { return ncclInternalError; }
__hidden ncclResult_t pluginCloseRecv(void* recvComm) { return ncclInternalError; }
__hidden ncclResult_t pluginCloseListen(void* listenComm) { return ncclInternalError; }
__hidden ncclResult_t pluginIrecvConsumed(void* recvComm, int n, void* request) { return ncclInternalError; }
__hidden ncclResult_t pluginGetDeviceMr(void* comm, void* mhandle, void** dptr_mhandle) { return ncclInternalError; }
__hidden ncclResult_t pluginMakeVDevice(int* d, ncclNetVDeviceProps_t* props) { return ncclInternalError; }
#define PLUGIN_NAME "Plugin"
const ncclNet_v10_t ncclNetPlugin_v10 = {
.name = PLUGIN_NAME,
.init = pluginInit,
.devices = pluginDevices,
.getProperties = pluginGetProperties,
.listen = pluginListen,
.connect = pluginConnect,
.accept = pluginAccept,
.regMr = pluginRegMr,
.regMrDmaBuf = pluginRegMrDmaBuf,
.deregMr = pluginDeregMr,
.isend = pluginIsend,
.irecv = pluginIrecv,
.iflush = pluginIflush,
.test = pluginTest,
.closeSend = pluginCloseSend,
.closeRecv = pluginCloseRecv,
.closeListen = pluginCloseListen,
.getDeviceMr = pluginGetDeviceMr,
.irecvConsumed = pluginIrecvConsumed,
.makeVDevice = pluginMakeVDevice,
};
__hidden ncclResult_t pluginInit_v9(ncclDebugLogger_t logFunction) {
return pluginInit(logFunction, NULL);
}
__hidden ncclResult_t pluginGetProperties_v9(int dev, ncclNetProperties_v9_t* props) {
return pluginGetProperties(dev, (ncclNetProperties_t*)props);
}
__hidden ncclResult_t pluginConnect_v9(int dev, void* handle, void** sendComm, ncclNetDeviceHandle_t** sendDevComm){
return pluginConnect(dev, NULL, handle, sendComm, sendDevComm);
}
__hidden ncclResult_t pluginIsend_v9(void* sendComm, void* data, size_t size, int tag, void* mhandle, void** request) {
return pluginIsend(sendComm, data, size, tag, mhandle, NULL, request);
}
__hidden ncclResult_t pluginIrecv_v9(void* recvComm, int n, void** data, size_t* sizes, int* tags, void** mhandles, void** request) {
return pluginIrecv(recvComm, n, data, sizes, tags, mhandles, NULL, request);
}
__hidden ncclResult_t pluginMakeVDevice_v9(int* d, ncclNetVDeviceProps_v9_t* props) { return ncclInternalError; }
const ncclNet_v9_t ncclNetPlugin_v9 = {
.name = PLUGIN_NAME,
.init = pluginInit_v9,
.devices = pluginDevices,
.getProperties = pluginGetProperties_v9,
.listen = pluginListen,
.connect = pluginConnect_v9,
.accept = pluginAccept,
.regMr = pluginRegMr,
.regMrDmaBuf = pluginRegMrDmaBuf,
.deregMr = pluginDeregMr,
.isend = pluginIsend_v9,
.irecv = pluginIrecv_v9,
.iflush = pluginIflush,
.test = pluginTest,
.closeSend = pluginCloseSend,
.closeRecv = pluginCloseRecv,
.closeListen = pluginCloseListen,
.getDeviceMr = pluginGetDeviceMr,
.irecvConsumed = pluginIrecvConsumed,
.makeVDevice = pluginMakeVDevice_v9,
};
__hidden ncclResult_t pluginGetProperties_v8(int dev, ncclNetProperties_v8_t* props_v8) {
ncclNetProperties_t props;
ncclResult_t ret = pluginGetProperties(dev, &props);
if (ret != ncclSuccess) return ret;
props_v8->name = props.name;
props_v8->pciPath = props.pciPath;
props_v8->guid = props.guid;
props_v8->ptrSupport = props.ptrSupport;
props_v8->regIsGlobal = props.regIsGlobal;
props_v8->speed = props.speed;
props_v8->latency = props.latency;
props_v8->port = props.port;
props_v8->maxComms = props.maxComms;
props_v8->maxRecvs = props.maxRecvs;
props_v8->netDeviceType = props.netDeviceType;
props_v8->netDeviceVersion = props.netDeviceVersion;
return ncclSuccess;
}
__hidden ncclResult_t pluginIsend_v8(void* sendComm, void* data, int size, int tag, void* mhandle, void** request) {
return pluginIsend(sendComm, data, (int)size, tag, mhandle, NULL, request);
}
__hidden ncclResult_t pluginIrecv_v8(void* recvComm, int n, void** data, int* sizes, int* tags, void** mhandles, void** request) {
size_t sizesOut[NCCL_PLUGIN_MAX_RECVS];
for (int i=0; i<n; i++) sizesOut[i] = sizes[i];
return pluginIrecv(recvComm, 1, data, sizesOut, tags, mhandles, NULL, request);
}
const ncclNet_v8_t ncclNetPlugin_v8 = {
.name = PLUGIN_NAME,
.init = pluginInit_v9,
.devices = pluginDevices,
.getProperties = pluginGetProperties_v8,
.listen = pluginListen,
.connect = pluginConnect_v9,
.accept = pluginAccept,
.regMr = pluginRegMr,
.regMrDmaBuf = pluginRegMrDmaBuf,
.deregMr = pluginDeregMr,
.isend = pluginIsend_v8,
.irecv = pluginIrecv_v8,
.iflush = pluginIflush,
.test = pluginTest,
.closeSend = pluginCloseSend,
.closeRecv = pluginCloseRecv,
.closeListen = pluginCloseListen,
.getDeviceMr = pluginGetDeviceMr,
.irecvConsumed = pluginIrecvConsumed,
};
__hidden ncclResult_t pluginGetProperties_v7(int dev, ncclNetProperties_v7_t* props_v7) {
ncclNetProperties_t props;
ncclResult_t ret = pluginGetProperties(dev, &props);
if (ret != ncclSuccess) return ret;
props_v7->name = props.name;
props_v7->pciPath = props.pciPath;
props_v7->guid = props.guid;
props_v7->ptrSupport = props.ptrSupport;
props_v7->speed = props.speed;
props_v7->latency = props.latency;
props_v7->port = props.port;
props_v7->maxComms = props.maxComms;
props_v7->maxRecvs = props.maxRecvs;
props_v7->netDeviceType = props.netDeviceType;
props_v7->netDeviceVersion = props.netDeviceVersion;
return ncclSuccess;
}
__hidden ncclResult_t pluginRegMr_v7(void* collComm, void* data, int size, int type, void** mhandle) {
return pluginRegMr(collComm, data, size, type, mhandle);
}
const ncclNet_v7_t ncclNetPlugin_v7 = {
.name = PLUGIN_NAME,
.init = pluginInit_v9,
.devices = pluginDevices,
.getProperties = pluginGetProperties_v7,
.listen = pluginListen,
.connect = pluginConnect_v9,
.accept = pluginAccept,
.regMr = pluginRegMr_v7,
.regMrDmaBuf = pluginRegMrDmaBuf,
.deregMr = pluginDeregMr,
.isend = pluginIsend_v8,
.irecv = pluginIrecv_v8,
.iflush = pluginIflush,
.test = pluginTest,
.closeSend = pluginCloseSend,
.closeRecv = pluginCloseRecv,
.closeListen = pluginCloseListen,
.getDeviceMr = pluginGetDeviceMr,
.irecvConsumed = pluginIrecvConsumed,
};
__hidden ncclResult_t pluginGetProperties_v6(int dev, ncclNetProperties_v6_t* props_v6) {
ncclNetProperties_t props;
ncclResult_t ret = pluginGetProperties(dev, &props);
if (ret != ncclSuccess) return ret;
props_v6->name = props.name;
props_v6->pciPath = props.pciPath;
props_v6->guid = props.guid;
props_v6->ptrSupport = props.ptrSupport;
props_v6->speed = props.speed;
props_v6->latency = props.latency;
props_v6->port = props.port;
props_v6->maxComms = props.maxComms;
props_v6->maxRecvs = props.maxRecvs;
return ncclSuccess;
}
__hidden ncclResult_t pluginConnect_v6(int dev, void* handle, void** sendComm) { return ncclInternalError; }
__hidden ncclResult_t pluginAccept_v6(void* listenComm, void** recvComm) { return ncclInternalError; }
const ncclNet_v6_t ncclNetPlugin_v6 = {
.name = PLUGIN_NAME,
.init = pluginInit_v9,
.devices = pluginDevices,
.getProperties = pluginGetProperties_v6,
.listen = pluginListen,
.connect = pluginConnect_v6,
.accept = pluginAccept_v6,
.regMr = pluginRegMr_v7,
.regMrDmaBuf = pluginRegMrDmaBuf,
.deregMr = pluginDeregMr,
.isend = pluginIsend_v8,
.irecv = pluginIrecv_v8,
.iflush = pluginIflush,
.test = pluginTest,
.closeSend = pluginCloseSend,
.closeRecv = pluginCloseRecv,
.closeListen = pluginCloseListen
};
/* v5 Compat */
const ncclNet_v5_t ncclNetPlugin_v5 = {
.name = PLUGIN_NAME,
.init = pluginInit_v9,
.devices = pluginDevices,
.getProperties = pluginGetProperties_v6,
.listen = pluginListen,
.connect = pluginConnect_v6,
.accept = pluginAccept_v6,
.regMr = pluginRegMr_v7,
.deregMr = pluginDeregMr,
.isend = pluginIsend_v8,
.irecv = pluginIrecv_v8,
.iflush = pluginIflush,
.test = pluginTest,
.closeSend = pluginCloseSend,
.closeRecv = pluginCloseRecv,
.closeListen = pluginCloseListen,
};
/* v4 Compat */
static ncclResult_t pluginGetProperties_v4(int dev, ncclNetProperties_v4_t* props_v4) {
ncclNetProperties_t props;
ncclResult_t ret = pluginGetProperties(dev, &props);
if (ret != ncclSuccess) return ret;
props_v4->name = props.name;
props_v4->pciPath = props.pciPath;
props_v4->guid = props.guid;
props_v4->ptrSupport = props.ptrSupport;
props_v4->speed = props.speed;
props_v4->port = props.port;
props_v4->maxComms = props.maxComms;
return ncclSuccess;
}
static ncclResult_t pluginIsend_v4(void *sendComm, void* data, int size, void *mhandle, void** request) {
return pluginIsend_v8(sendComm, data, size, 0, mhandle, request);
}
static ncclResult_t pluginIrecv_v4(void* recvComm, void* data, int size, void* mhandle, void** request) {
int tag = 0;
return pluginIrecv_v8(recvComm, 1, &data, &size, &tag, &mhandle, request);
}
static ncclResult_t pluginIflush_v4(void* recvComm, void* data, int size, void* mhandle, void** request) {
return pluginIflush(recvComm, 1, &data, &size, &mhandle, request);
}
static ncclResult_t pluginConnect_v4(int dev, void* handle, void** sendComm) {
ncclResult_t ret;
do {
ncclNetDeviceHandle_v7_t* handle = NULL;
ret = pluginConnect(dev, NULL, handle, sendComm, &handle);
} while (ret == ncclSuccess && *sendComm == NULL);
return ret;
}
static ncclResult_t pluginAccept_v4(void* listenComm, void** recvComm) {
ncclResult_t ret;
do {
ncclNetDeviceHandle_v7_t* handle = NULL;
ret = pluginAccept(listenComm, recvComm, &handle);
} while (ret == ncclSuccess && *recvComm == NULL);
return ret;
}
const ncclNet_v4_t ncclNetPlugin_v4 = {
.name = PLUGIN_NAME,
.init = pluginInit_v9,
.devices = pluginDevices,
.getProperties = pluginGetProperties_v4,
.listen = pluginListen,
.connect = pluginConnect_v4,
.accept = pluginAccept_v4,
.regMr = pluginRegMr_v7,
.deregMr = pluginDeregMr,
.isend = pluginIsend_v4,
.irecv = pluginIrecv_v4,
.iflush = pluginIflush_v4,
.test = pluginTest,
.closeSend = pluginCloseSend,
.closeRecv = pluginCloseRecv,
.closeListen = pluginCloseListen,
};
/* v3 Compat */
static ncclResult_t pluginFlush(void* recvComm, void* data, int size, void* mhandle) {
void* req;
ncclResult_t ret = pluginIflush_v4(recvComm, data, size, mhandle, &req);
int done = 0;
while (ret == ncclSuccess && done == 0) {
ret = pluginTest(req, &done, NULL);
}
return ret;
}
static ncclResult_t pluginInit_v3(ncclDebugLogger_t logFunction) {
max_requests = NCCL_NET_MAX_REQUESTS_V3;
return pluginInit(logFunction, NULL);
}
#include <string.h>
static ncclResult_t pluginListen_v3(int dev, void* handle, void** listenComm) {
char pluginHandle[NCCL_NET_HANDLE_MAXSIZE];
ncclResult_t ret = pluginListen(dev, &pluginHandle, listenComm);
memcpy(handle, &pluginHandle, NCCL_NET_HANDLE_MAXSIZE_V4);
return ret;
}
static ncclResult_t pluginConnect_v3(int dev, void* handle, void** sendComm) {
char pluginHandle[NCCL_NET_HANDLE_MAXSIZE];
memcpy(&pluginHandle, handle, NCCL_NET_HANDLE_MAXSIZE_V4);
return pluginConnect_v4(dev, &pluginHandle, sendComm);
}
const ncclNet_v3_t ncclNetPlugin_v3 = {
.name = PLUGIN_NAME,
.init = pluginInit_v3,
.devices = pluginDevices,
.getProperties = pluginGetProperties_v4,
.listen = pluginListen_v3,
.connect = pluginConnect_v3,
.accept = pluginAccept_v4,
.regMr = pluginRegMr_v7,
.deregMr = pluginDeregMr,
.isend = pluginIsend_v4,
.irecv = pluginIrecv_v4,
.flush = pluginFlush,
.test = pluginTest,
.closeSend = pluginCloseSend,
.closeRecv = pluginCloseRecv,
.closeListen = pluginCloseListen,
};
/* v2 Compat */
const ncclNet_v2_t ncclNetPlugin_v2 = {
.name = PLUGIN_NAME,
.init = pluginInit_v3,
.devices = pluginDevices,
.pciPath = pluginPciPath,
.ptrSupport = pluginPtrSupport,
.listen = pluginListen,
.connect = pluginConnect_v4,
.accept = pluginAccept_v4,
.regMr = pluginRegMr_v7,
.deregMr = pluginDeregMr,
.isend = pluginIsend_v4,
.irecv = pluginIrecv_v4,
.flush = pluginFlush,
.test = pluginTest,
.closeSend = pluginCloseSend,
.closeRecv = pluginCloseRecv,
.closeListen = pluginCloseListen,
};

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@ -1,22 +0,0 @@
CUDA_HOME?=/usr/local/cuda
INC:=-I$(CUDA_HOME)/include
PLUGIN_SO:=libnccl-net.so
default: $(PLUGIN_SO)
$(PLUGIN_SO): nccl-fastsocket/*.cc
$(CC) $(INC) -fPIC -shared -o $@ -Wl,-soname,$(PLUGIN_SO) $^
nccl-fastsocket/*.cc:
git clone https://github.com/google/nccl-fastsocket.git
install: $(BUILDDIR)/lib/$(PLUGIN_SO)
$(BUILDDIR)/lib/$(PLUGIN_SO): $(PLUGIN_SO)
@printf "Grabbing %-35s > %s\n" $< $@
mkdir -p $(BUILDDIR)/lib
install -m 644 $< $@
clean:
rm -f $(PLUGIN_SO)
rm -Rf nccl-fastsocket

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@ -1,461 +0,0 @@
# NCCL Profiler Plugin Documentation
This page describes the NCCL Profiler plugin API and how to implement a profiler plugin for NCCL.
# Overview
To allow NCCL to better integrate with DL frameworks, NCCL v2.23 introduced a profiler plugin
interface. Any NCCL user can write profiler plugins to extract performance data from NCCL and
use it for debugging and analysis.
Similarly to other plugins (e.g., network plugin), the profiler plugins come as a shared library
called `libnccl-profiler.so`. That shared library contains one or more implementations of the
NCCL PROFILER API, in the form of versioned structs, filled with pointers to all required
functions.
# Plugin architecture
## Plugin name and supporting multiple profiler plugins
When NCCL is initialized, it will look for a `libnccl-profiler.so` library and dynamically load
it, then look for symbols inside the library.
The `NCCL_PROFILER_PLUGIN` environment variable allows multiple plugins to coexist. If set, NCCL
will look for a library with a name of `libnccl-profiler-${NCCL_PROFILER_PLUGIN}.so`. It is therefore
advised to name the library following that pattern, with a symlink pointing `libnccl-profiler.so`
to `libnccl-profiler-${NCCL_PROFILER_PLUGIN}.so`. That way, if there are multiple plugins in the
path, setting `NCCL_PROFILER_PLUGIN` will allow users to select the right plugin. Alternatively,
the user can also set `NCCL_PROFILER_PLUGIN` to the pathname of the `libnccl-profiler.so` library.
## Struct versioning
Once a library is found, NCCL will look for a symbol named `ncclProfiler_vX`, with `X` increasing
over time. The versioning ensures that the plugin and the NCCL core are compatible.
Plugins are encouraged to provide multiple of those symbols, implementing multiple versions of the
NCCL PROFILER API, so that the same plugin can be compiled and support a wide range of NCCL versions.
Conversely, and to ease transition, NCCL can choose to support different plugin versions, looking
for the latest ncclProfiler struct version, but also looking for older ones so that older plugins
would still work.
## Headers management
To help users build plugins effortlessly, plugins should copy the `ncclProfiler_vX` definitions
they support to their internal includes. An example is shown in `ext-profiler/example` where we
keep all headers in the `nccl/` directory and provide thin layers to implement old version on top
of newer ones.
The `nccl/` directory is populated with `profiler_vX.h` files extracting all relevant definitions
from old API versions. It also provides error codes in `err.h`.
# API (v4)
Below is the main `ncclProfiler_v4` struct. Each function is explained in later sections.
```
typedef struct {
const char* name;
// init - initialize the profiler plugin
// Input
// - context : opaque profiler context object for separating profiler behavior across comms
// - commName : user assigned communicator name
// - commHash : communicator id
// - nNodes : number of nodes in communicator
// - nranks : number of ranks in communicator
// - rank : rank identifier in communicator
// - logfn : logger function
// Output
// - eActivationMask: bitmask of active events set by the plugin
ncclResult_t (*init)(void** context, int* eActivationMask, const char* commName, uint64_t commHash, int nNodes, int nranks, int rank, ncclDebugLogger_t logfn);
// startEvent - initialize and start a new event for the supplied event descriptor inside the eventset
// Input
// - context: opaque profiler context object
// - eDescr : pointer to ncclProfilerEventDescr_t object
// Output
// - eHandle: return event handle for supplied event descriptor object
ncclResult_t (*startEvent)(void* context, void** eHandle, ncclProfilerEventDescr_v4_t* eDescr);
// stopEvent - stop/finalize an event inside and event set
// Input
// - eHandle: handle to event object
ncclResult_t (*stopEvent)(void* eHandle);
// recordEventState - record event state transitions and event attribute updates
// Input
// - eHandle : handle to event object created through startEvent
// - eStateArgs: optional argument used to capture event attribute updates associated with the state transition
// - eState : event state transition
ncclResult_t (*recordEventState)(void* eHandle, ncclProfilerEventState_v4_t eState, ncclProfilerEventStateArgs_v4_t* eStateArgs);
// finalize - finalize the profiler plugin
// Input
// - context: opaque profiler context object
ncclResult_t (*finalize)(void* context);
} ncclProfiler_v4_t;
```
## Error codes
As rule of thumb, profiler generated errors should not be propagated to NCCL and alter its normal
functioning. Nevertheless, the profiler interface returns NCCL error codes, in case any need for
them arises in the future. For now, any profiler interface call should only return `ncclSuccess`.
The only exception is `init` that can return an error so that NCCL can disable the plugin.
## Operation overview
NCCL will call the `init` function first for every new communicator that is initialized. The profiler
returns an opaque context handle that is used to isolate profiler instances across communicators.
Similarly, NCCL will call `finalize` to destroy the profiler context, thus freeing resources.
The NCCL core code is instrumented with calls to `startEvent`, `stopEvent` and `recordEventState`.
These are used to start, stop and update events in the profiler, respectively.
## API Functions
### Initialization
#### name
The `name` field should point to a character string with the name of the profiler plugin. This will
be used for all logging, especially when `NCCL_DEBUG=INFO` is set.
#### init
As soon as NCCL finds the plugin and the correct ncclProfiler symbol, it calls its `init` function.
This allows the plugin to initialize its internal context, used during profiling of NCCL events.
If the `init` function does not return `ncclSuccess`, NCCL disables the plugin.
#### finalize
When the profiler is no longer needed, a call to `finalize` destroys the profiler context and frees
up resources.
### Profiling
#### startEvent
When NCCL needs to start profiling a new event it calls `startEvent`. `startEvent` takes the profiler
context, previously created by `init`, an event descriptor of type `ncclProfilerEventDescr_t` and
returns an opaque profiler event handle that can be passed to other profiler functions, as discussed
later in the document.
The event descriptor contains all the event metadata. Every event type has its own descriptor. Below
is the `ncclProfilerEventDescr_t` struct.
```
typedef struct {
uint8_t type; // event type (e.g., ncclProfileGroup, ncclProfileColl, ...)
void* parentObj; // pointer to parent event used to expose the event hierarchy to the profiler
int rank; // rank that generated the event
union {
struct { // collective events metadata
uint64_t seqNumber; // sequence number of this collective operation in the communicator
const char* func; // string containing name of the collective
void const* sendBuff; // address of send buffer
void* recvBuff; // address of recv buffer
size_t count; // data count
int root; // root rank
const char* datatype; // string containing the name of the datatype
uint8_t nChannels; // number of channels for this collective
uint8_t nWarps; // number of GPU warps for this collective
const char* algo; // string containing name of the algorithm for this collective
const char* proto; // string containing name of the protocol for this collective
} coll;
struct { // point-to-point events metadata
const char* func;
void* buff;
const char* datatype;
size_t count;
int peer; // peer rank for this point-to-point
uint8_t nChannels; // number of channels for this p2p
} p2p;
struct { // proxyOp events metadata
pid_t pid; // process id that generated the associated `ncclProxyOp` object
uint8_t channelId; // id of the channel used by the associated `ncclProxyOp` object
int peer; // peer rank
int nSteps; // number of network transfers/steps required by the `ncclProxyOp`
int chunkSize; // chunk size for this `ncclProxyOp`
int isSend; // type of network operation
} proxyOp;
struct { // proxyStep events metadata
int step; // individual step in `ncclProxyOp`
} proxyStep;
struct {
uint8_t channelId; // id of the channel used by the kernel
uint64_t ptimer; // kernel supplied timestamp
} kernelCh;
struct {
int64_t id; // net plugin id (used by net and profiler plugins to agree on event definitions)
void* data; // pointer to network plugin defined event
} netPlugin;
};
} ncclProfilerEventDescr_v4_t;
```
NCCL defines the following events: `ncclProfileGroup`, `ncclProfileColl`, `ncclProfileP2p`,
`ncclProfileProxyOp`, `ncclProfileProxyStep`, `ncclProfileProxyCtrl`, `ncclProfileKernelCh` and
`ncclProfileNetPlugin`.
#### stopEvent
`stopEvent` takes the event handle returned by `startEvent` to stop the event. After the event
has been stopped the handle can no longer be used with other profiler calls. Using the event
handle after `eventStop` is undefined behavior.
#### recordEventState
Some events can only be started and stopped. For example, `ncclProfileGroup`, `ncclProfileColl`,
`ncclProfileP2p`, cannot be updated through calls to `recordEventState`.
`ncclProfileProxyOp`, `ncclProfileProxyStep`, `ncclProfileNetPlugin`, `ncclProfileKernelCh`, and
`ncclProfileProxyCtrl` can be updated through calls to `recordEventState`.
The state of these events can be updated, along with event attributes, using `recordEventState`.
These events can go through several states during their lifecycle.
The list of supported states for the updatable events is reported below.
```
typedef enum {
// ncclProfileProxyOp event states
ncclProfilerProxyOpSendPosted = 0, // deprecated in v4
ncclProfilerProxyOpSendRemFifoWait = 1, // deprecated in v4
ncclProfilerProxyOpSendTransmitted = 2, // deprecated in v4
ncclProfilerProxyOpSendDone = 3, // deprecated in v4
ncclProfilerProxyOpRecvPosted = 4, // deprecated in v4
ncclProfilerProxyOpRecvReceived = 5, // deprecated in v4
ncclProfilerProxyOpRecvTransmitted = 6, // deprecated in v4
ncclProfilerProxyOpRecvDone = 7, // deprecated in v4
ncclProfilerProxyOpInProgress_v4 = 19,// state marks transition of proxy op to progress
// ncclProfileProxyStep event states
ncclProfilerProxyStepSendGPUWait = 8, // state marks the waiting of send data from GPU for given network transfer/step
ncclProfilerProxyStepSendPeerWait_v4 = 20,// state marks the waiting of recv clear to send credits for given network transfer/step
ncclProfilerProxyStepSendWait = 9, // state marks the waiting of send data from network for given network transfer/step
ncclProfilerProxyStepRecvWait = 10,// state marks the waiting of recv data from network for given network transfer/step
ncclProfilerProxyStepRecvFlushWait = 11,// state marks the waiting of recv data flush to GPU for given network transfer/step
ncclProfilerProxyStepRecvGPUWait = 12,// state marks the waiting of recv data consumption from GPU for given network transfer/step
// ncclProfileProxyCtrl event states
ncclProfilerProxyCtrlIdle = 13,// state marks proxy progress thread idle
ncclProfilerProxyCtrlActive = 14,// state marks proxy progress thread active
ncclProfilerProxyCtrlSleep = 15,// state marks proxy progress thread sleeping
ncclProfilerProxyCtrlWakeup = 16,// state marks proxy progress thread waking up
ncclProfilerProxyCtrlAppend = 17,// state marks append of new network work item begin
ncclProfilerProxyCtrlAppendEnd = 18,// state marks append of new network work item end
// ncclProfileNetPlugin event states
ncclProfilerNetPluginUpdate = 21,// state marks update of network defined event
// ncclProfileKernelCh event states
ncclProfilerKernelChStop = 22,// state marks stop of kernelCh event and timestamp update
} ncclProfilerEventState_v4_t;
```
`ncclProfileProxyOp` events are generated by the proxy progress thread while it is processing
network requests for the GPU kernel. ProxyOp events are generated for every active channel and
provide a summary of the activity of the proxy progress thread for that channel. Most of the
states for this event were duplicated with `ncclProfileProxyStep` events. Therefore, starting
with version 4 of the profiler interface these states have been deprecated. The same level of
information can still be obtained through the `ncclProfileProxyStep` events.
`ncclProfileProxyStep` events are generated by the proxy progress thread while it is processing
network requests for the GPU kernel. ProxyStep events describe individual network transfer in
the channel. Thus, they provide a more fine-grained view w.r.t. ProxyOp events.
`ncclProfileProxyCtrl` events are generated by the proxy progress thread while it is not processing
network requests for the GPU kernel. This includes everything else that the proxy thread might be
doing, including appending new `ncclProxyOp` objects to the list of work elements to process.
`ncclProfileKernelCh` events are generated by the profiler proxy progress function while the kernel
processes work items for the enqueued NCCL operations.
`ncclProfileNetPlugin` events are generated by the network plugin. Network plugins are free to define
their own set of events and communicate them to the profiler plugin using `ncclProfileNetPlugin` and
the `ncclProfilerCallback\_t` NCCL core callback. The network and profiler plugin can agree on the
network defined event definition using the plugin id in the event descriptor. The plugin identifier
is a 64-bit integer that has two parts: the 16 LSB are assigned to the plugin event version, the next
16 bits are assigned to the plugin type (NCCL\_PROFILER\_NET\_TYPE\_IB, ...). The rest of the bits are
unused and available for future extensions.
A network IB plugin can use this infrastructure to define a QP event as:
```C
#define NCCL_PROFILER_NET_IB_VER 1
enum {
ncclProfileQp = (1 << 0),
};
// The data structure version is encoded in the plugin identifier bitmask and
// passed to NCCL core through the profiler callback. NCCL copies the plugin
// identifier in the event descriptor before calling the profiler startEvent
// function. The profiler should inspect the plugin id to find out the source
// plugin as well as the version of the event struct
typedef struct {
uint8_t type; // event type (plugin defined)
union {
struct {
int device; // network device id
uint64_t wr_id; // work request id
int opcode; // ibv opcode
int qpNum; // QP number
size_t length; // work request data length
} qp;
};
} ncclProfilerNetIbDescr_v1_t;
```
The network event infrastructure is network agnostic. A different network socket plugin can
use it to define a socket event as:
```C
#define NCCL_PROFILER_NET_SOCKET_VER 1
enum {
ncclProfileSocket = (1 << 0),
};
// The data structure version is encoded in the plugin identifier bitmask and
// passed to NCCL core through the profiler callback. NCCL copies the plugin
// identifier in the event descriptor before calling the profiler startEvent
// function. The profiler should inspect the plugin id to find out the source
// plugin as well as the version of the event struct
typedef struct {
uint8_t type; // event type (plugin defined)
union {
struct {
int fd;
int op;
size_t length;
} sock;
};
} ncclProfilerNetSockDescr_v1_t;
```
The network plugin creates an event (descriptor) and passes it to the profiler callback,
along with the network type and version (plugin id). NCCL then creates a `ncclProfileNetPlugin`
event descriptor, attaches the network plugin defined event as external data, and calls
the profiler `startEvent` function.
```C
ncclResult_t isend(..., void* phandle, ...) {
...
int pluginId = NCCL_PROFILER_NET_TYPE_IB | NCCL_PROFILER_NET_IB_VER;
ncclProfilerNetIbDescr_v1_t eDescr = { };
eDescr.type = ncclProfileQp;
eDescr.qp = { ... };
ncclProfilerCallback(&eHandle, 0 /* start net event */, phandle, pluginId, &eDescr);
...
}
```
State transitions for the events described can also come with event attribute updates. For this
reason the profiler defines the `ncclProfilerEventStateArgs_t` struct, reported below.
```
typedef union {
struct { // attributes for update for ncclProfileProxyStep events
size_t transSize; // transfer size field for this proxy step
} proxyStep;
struct { // attributes to update for ncclProfileProxyCtrl events
int appendedProxyOps; // number of appended proxy ops thus far
} proxyCtrl;
struct { // attributes to update for ncclProfileNetPlugin events
void* data; // network plugin opaque update data field
} netPlugin;
struct { // attribute to update for ncclProfileKernelCh events
uint64_t pTimer; // timestamp provided by the NCCL kernel
} kernelCh;
} ncclProfilerEventStateArgs_v4_t;
```
The example profiler in `ext-profiler/example` contains details on how to capture and use the events above.
### Event hierarchy
NCCL core events (reported above) are organized into a hierarchy as reported below:
```
Group event
|
+- Collective event
| |
| +- ProxyOp event
| | |
| | +- ProxyStep event
| | |
| | +- NetPlugin event
| |
| +- KernelCh event
|
+- Point-to-point event
|
+- ProxyOp event
| |
| +- ProxyStep event
| |
| +- NetPlugin event
|
+- KernelCh event
ProxyCtrl event
```
# Profiler instrumentation and logging
## Profiling of collective and p2p operations
The NCCL code is instrumented with profiler callbacks at different levels to capture start/stop of groups,
collective and point-to-point operations, as well as proxy, kernel and network activity. Due to the asynchronous nature
of NCCL operations, events associated to collective and point-to-point operations are not easy to delimit
precisely. For example, without both proxy and/or kernel activity it is impossible for the profiler to
figure out when a collective operation completes. Therefore, `stopEvent` for collectives simply indicates to
the profiler that the collective has been enqueued. The profiler can leverage proxy and/or kernel event information, if
these are enabled, to estimate when the collective ends. For example, the profiler can look at the `stopEvent`
call of the last `ncclProfileProxyOp` event to mark the completion of the associated collective event. This
can be achieved by reference counting the collective event and letting calls to `startEvent` and `stopEvent`
increment and decrement the reference counter, respectively.
## PXN
PXN causes some proxy operations to be processed in a remote proxy thread that differs from the one that
generated the operation. When this happens, the event hierarchy reported above breaks. Because the
profiler can use the hierarchy information, provided by NCCL in the event descriptor, to dereference the
parent event during `startEvent`, the remote proxy thread must be in the same address space of the proxy
thread originating the operation. To avoid the profiler instance in the remote proxy address space to
dereference a pointer from another address space the event descriptor includes the PID of the originator.
The profiler plugin needs to check that the originator PID matches the local PID before dereferencing the
parent event.
# Known Limitations
In intra-node communication, or whenever a rank does not have any network activity for which proxy events
are unavailable, the profiler will only report the enqueue events (e.g., ncclAllReduce). The events from
enqueue can be time stamped by the profiler (at start and stop) to reconstruct the execution time of the
collective. However, this time only represents the launch time of the collective and not the actual
execution time. To reconstruct the execution time more accurately proxy and kernel events are provided.
With version 3 of the profiler interface network activity is no longer required to do intra-node profiling.
Kernel events instrumentation leverages counters exposed by the kernel to the host and the proxy progress
thread. Thus, the proxy progress thread infrastructure is shared between the network and the profiler. If
the proxy is serving network requests the kernel profiling probing can be delayed, causing loss of
accuracy. Similarly, if the CPU is under heavy load and the scheduling of the proxy progress thread is
delayed, a similar loss of accuracy can be encountered.
To mitigate this effect, with version 4 of the profiler NCCL uses a per-channel ring buffer of 64 elements.
Every counter is complemented by two timestamps (ptimers) supplied by the NCCL kernel (one for start and one
for stop of the operation in the kernel). NCCL propagates these timestamps to the profiler plugin that it can
convert them to CPU time domain.

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@ -1,22 +0,0 @@
#
# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
#
# See LICENSE.txt for license information
#
.DEFAULT_GOAL: build
include ../../makefiles/common.mk
SRCDIR ?= $(abspath ../..)
BUILDDIR ?= .
NCCLDIR := $(BUILDDIR)
SRC_FILES := $(wildcard *.c)
build: ${BUILDDIR}/libnccl-profiler-example.so
${BUILDDIR}/libnccl-profiler-example.so: ${SRC_FILES}
@printf "Compiling %-35s > %s\n" $< $@
@mkdir -p ${BUILDDIR}
$(CC) -Inccl -fPIC -shared -o $@ $^
clean:
rm -f ${BUILDDIR}/libnccl-profiler-example.so

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# NCCL Example Profiler Plugin Usage
This page describes how to use the NCCL example profiler plugin
# Overview
The example profiler plugin implements the NCCL profiler plugin API introduced in NCCL v2.23. The API
defines a set of events and data structures that NCCL uses to share event information with profiler
plugins. The user can control what events are instrumented by NCCL and when traces collected by the
profiler should be dumped through environment variables, as described in the rest of the document.
The user can also control other profiler parameters that alter its behavior. For example, users can
change the size of the event window the profiler keeps track of.
## Building the profiler plugin
To use the example plugin, just type `make`. You will need a NCCL build's include directory present.
You can override `NCCL_HOME` to where the NCCL installation is on your system.
## Using the profiler plugin
1. Add the directory of this profiler plugin to your `LD_LIBRARY_PATH` or set the `NCCL_PROFILER_PLUGIN`,
as documented in `ext-profiler/README.md`.
2. Set `NCCL_PROFILE_EVENT_MASK` bitmask to specify the NCCL events you want to instrument. By
default, all collectives and send/recv operations will be traced. For more details about the event
representation used by the profiler refer to `ext-profiler/README.md`.
As an example, setting:
`NCCL_PROFILE_EVENT_MASK` to 1 (`ncclProfileGroup`) | 2 (`ncclProfileColl`) | 8 (`ncclProfileProxyOp`)
enables the profiling of the group, the collective and the proxy op events. The same events can be
expressed more concisely by setting `NCCL_PROFILE_EVENT_MASK` to 8 (`ncclProfileProxyOp`). Indeed,
in NCCL all the events above (in the event hierarchy) the one requested are also captured. The advantage
is that the profiler can easily correlate events that belong to the same NCCL operation and present
them accordingly.
3. Set `NCCL_PROFILE_DUMP_FILE` to the name of the dump file for the collected traces. A file named
${NCCL_PROFILE_DUMP_FILE}-hostname-tid.txt is created. Profiler traces are saved using the chrome
event format (more precisely, using asynchronous events).
4. If you set the dump file variable, type chrome://tracing on your chromium browser search bar and
open the created dump file to visualize the traces.
# Changing the profiler memory pool sizes
The example profiler uses separate memory pools for different types of events. The size of these memory
pools (i.e., the # events) determines the number of events that the profiler can keep track of at the
same time. When NCCL requests a new event (e.g., collective event) to profile a `ncclAllReduce`
operation, by calling `startEvent`, the profiler searches in the collective pool for a free event. If it
finds one, it marks it as in use and returns the handle to NCCL. If the pool is completely used the
profiler returns `NULL` to NCCL and ignores all the following NCCL profiler calls for the `NULL` event
handle. When the `ncclAllReduce` has been processed, NCCL calls `stopEvent` with the previosly returned
event handle. The profiler has a total of 5 memory pools.
The group, collective and p2p pools contain objects for the corresponding events. The `ProxyCtrl` pool
contains objects for `ProxyCtrl` events and the `ProxyDetach` pool contains objects for `ProxyOp` events
generated by remote proxies. A list of pools and their size is reported below:
- `NCCL_PROFILE_GROUP_POOL_SIZE` (16)
- `NCCL_PROFILE_COLL_POOL_SIZE` (16)
- `NCCL_PROFILE_P2P_POOL_SIZE` (1024)
- `NCCL_PROFILE_PROXY_CTRL_POOL_SIZE` (16)
- `NCCL_PROFILE_PROXY_DETACH_POOL_SIZE` (128)
Remote proxy operations are generated when PXN is in use. Refer to this article for more information
about PXN and how it works:
https://developer.nvidia.com/blog/doubling-all2all-performance-with-nvidia-collective-communication-library-2-12/
# Reported events
The example profiler generates traces using the json format. An example of trace is reported below:
```
[
{"name": "Group", "cat": "GROUP", "ph": "b", "id": 0, "pid": 4157654, "tid": 1, "ts": 764234.611328, "args": {"groupId": 0}},
{"name": "AllReduce", "cat": "COLL", "ph": "b", "id": 0, "pid": 4157654, "tid": 1, "ts": 764237.294922, "args": {"SeqNum": 0, "CommHash": 673864846479792718, "Rank": 1, "Count": 32768, "Datatype": "ncclFloat32", "Algorithm": "RING", "Protocol": "LL", "nMaxChannels": 2}},
{"name": "Recv", "cat": "PROXY", "ph": "b", "id": 0, "pid": 4157654, "tid": 1, "ts": 768464.936523, "args": {"Channel": 0, "Peer": 0, "Steps": 14, "ChunkSize": 32768, "transSize": 229376, "POSTED": {"step": 14, "ts": 772020.300781}, "RECEIVED": {"step": 14, "ts": 772196.049805}, "TRANSMITTED": {"step": 14, "ts": 772197.326172}, "DONE": {"step": 14, "ts": 772201.538086}}},
{"name": "RecvBufferWait", "cat": "NET", "ph": "b", "id": 0, "pid": 4157654, "tid": 1, "ts": 768465.158203, "args": {"Step": 0}},
{"name": "RecvBufferWait", "cat": "NET", "ph": "e", "id": 0, "pid": 4157654, "tid": 1, "ts": 768477.924805},
{"name": "RecvWait", "cat": "NET", "ph": "b", "id": 0, "pid": 4157654, "tid": 1, "ts": 768477.924805, "args": {"Step": 0}},
{"name": "RecvWait", "cat": "NET", "ph": "e", "id": 0, "pid": 4157654, "tid": 1, "ts": 768547.197266},
{"name": "RecvFlushWait", "cat": "NET", "ph": "b", "id": 0, "pid": 4157654, "tid": 1, "ts": 768547.197266, "args": {"Step": 0}},
{"name": "RecvFlushWait", "cat": "NET", "ph": "e", "id": 0, "pid": 4157654, "tid": 1, "ts": 768564.174805},
{"name": "RecvGpuWait", "cat": "NET", "ph": "b", "id": 0, "pid": 4157654, "tid": 1, "ts": 768564.174805, "args": {"Step": 0}},
{"name": "RecvGpuWait", "cat": "NET", "ph": "e", "id": 0, "pid": 4157654, "tid": 1, "ts": 768568.276367},
{"name": "RecvBufferWait", "cat": "NET", "ph": "b", "id": 1, "pid": 4157654, "tid": 1, "ts": 768503.604492, "args": {"Step": 1}},
{"name": "RecvBufferWait", "cat": "NET", "ph": "e", "id": 1, "pid": 4157654, "tid": 1, "ts": 768504.549805},
{"name": "RecvWait", "cat": "NET", "ph": "b", "id": 1, "pid": 4157654, "tid": 1, "ts": 768504.549805, "args": {"Step": 1}},
{"name": "RecvWait", "cat": "NET", "ph": "e", "id": 1, "pid": 4157654, "tid": 1, "ts": 769994.490234},
{"name": "RecvFlushWait", "cat": "NET", "ph": "b", "id": 1, "pid": 4157654, "tid": 1, "ts": 769994.490234, "args": {"Step": 1}},
{"name": "RecvFlushWait", "cat": "NET", "ph": "e", "id": 1, "pid": 4157654, "tid": 1, "ts": 769995.012695},
{"name": "RecvGpuWait", "cat": "NET", "ph": "b", "id": 1, "pid": 4157654, "tid": 1, "ts": 769995.012695, "args": {"Step": 1}},
{"name": "RecvGpuWait", "cat": "NET", "ph": "e", "id": 1, "pid": 4157654, "tid": 1, "ts": 770006.914062},
{"name": "RecvBufferWait", "cat": "NET", "ph": "b", "id": 2, "pid": 4157654, "tid": 1, "ts": 768506.941406, "args": {"Step": 2}},
{"name": "RecvBufferWait", "cat": "NET", "ph": "e", "id": 2, "pid": 4157654, "tid": 1, "ts": 768507.435547},
{"name": "RecvWait", "cat": "NET", "ph": "b", "id": 2, "pid": 4157654, "tid": 1, "ts": 768507.435547, "args": {"Step": 2}},
{"name": "RecvWait", "cat": "NET", "ph": "e", "id": 2, "pid": 4157654, "tid": 1, "ts": 771452.536133},
{"name": "RecvFlushWait", "cat": "NET", "ph": "b", "id": 2, "pid": 4157654, "tid": 1, "ts": 771452.536133, "args": {"Step": 2}},
{"name": "RecvFlushWait", "cat": "NET", "ph": "e", "id": 2, "pid": 4157654, "tid": 1, "ts": 771453.060547},
{"name": "RecvGpuWait", "cat": "NET", "ph": "b", "id": 2, "pid": 4157654, "tid": 1, "ts": 771453.060547, "args": {"Step": 2}},
{"name": "RecvGpuWait", "cat": "NET", "ph": "e", "id": 2, "pid": 4157654, "tid": 1, "ts": 771468.458008},
{"name": "RecvBufferWait", "cat": "NET", "ph": "b", "id": 3, "pid": 4157654, "tid": 1, "ts": 768509.484375, "args": {"Step": 3}},
{"name": "RecvBufferWait", "cat": "NET", "ph": "e", "id": 3, "pid": 4157654, "tid": 1, "ts": 768510.250000},
{"name": "RecvWait", "cat": "NET", "ph": "b", "id": 3, "pid": 4157654, "tid": 1, "ts": 768510.250000, "args": {"Step": 3}},
{"name": "RecvWait", "cat": "NET", "ph": "e", "id": 3, "pid": 4157654, "tid": 1, "ts": 771904.499023},
{"name": "RecvFlushWait", "cat": "NET", "ph": "b", "id": 3, "pid": 4157654, "tid": 1, "ts": 771904.499023, "args": {"Step": 3}},
{"name": "RecvFlushWait", "cat": "NET", "ph": "e", "id": 3, "pid": 4157654, "tid": 1, "ts": 771904.991211},
{"name": "RecvGpuWait", "cat": "NET", "ph": "b", "id": 3, "pid": 4157654, "tid": 1, "ts": 771904.991211, "args": {"Step": 3}},
{"name": "RecvGpuWait", "cat": "NET", "ph": "e", "id": 3, "pid": 4157654, "tid": 1, "ts": 771910.500000},
{"name": "Send", "cat": "PROXY", "ph": "b", "id": 1, "pid": 4157654, "tid": 1, "ts": 768482.878906, "args": {"Channel": 0, "Peer": 2, "Steps": 14, "ChunkSize": 32768, "transSize": 229376, "POSTED": {"step": 14, "ts": 771995.675781}, "REM_FIFO_WAIT": {"step": 14, "ts": 772190.692383}, "TRANSMITTED": {"step": 14, "ts": 772191.516602}, "DONE": {"step": 14, "ts": 772208.473633}}},
{"name": "SendBufferWait", "cat": "NET", "ph": "b", "id": 14, "pid": 4157654, "tid": 1, "ts": 768483.019531, "args": {"Step": 0}},
{"name": "SendBufferWait", "cat": "NET", "ph": "e", "id": 14, "pid": 4157654, "tid": 1, "ts": 768483.300781},
{"name": "SendGpuWait", "cat": "NET", "ph": "b", "id": 14, "pid": 4157654, "tid": 1, "ts": 768483.300781, "args": {"Step": 0}},
{"name": "SendGpuWait", "cat": "NET", "ph": "e", "id": 14, "pid": 4157654, "tid": 1, "ts": 769594.615234},
{"name": "SendWait", "cat": "NET", "ph": "b", "id": 14, "pid": 4157654, "tid": 1, "ts": 769594.615234, "args": {"Step": 0}},
{"name": "SendWait", "cat": "NET", "ph": "e", "id": 14, "pid": 4157654, "tid": 1, "ts": 769618.889648},
{"name": "SendBufferWait", "cat": "NET", "ph": "b", "id": 15, "pid": 4157654, "tid": 1, "ts": 768505.083008, "args": {"Step": 1}},
{"name": "SendBufferWait", "cat": "NET", "ph": "e", "id": 15, "pid": 4157654, "tid": 1, "ts": 768505.163086},
{"name": "SendGpuWait", "cat": "NET", "ph": "b", "id": 15, "pid": 4157654, "tid": 1, "ts": 768505.163086, "args": {"Step": 1}},
{"name": "SendGpuWait", "cat": "NET", "ph": "e", "id": 15, "pid": 4157654, "tid": 1, "ts": 769610.555664},
{"name": "SendWait", "cat": "NET", "ph": "b", "id": 15, "pid": 4157654, "tid": 1, "ts": 769610.555664, "args": {"Step": 1}},
{"name": "SendWait", "cat": "NET", "ph": "e", "id": 15, "pid": 4157654, "tid": 1, "ts": 769622.517578},
{"name": "SendBufferWait", "cat": "NET", "ph": "b", "id": 16, "pid": 4157654, "tid": 1, "ts": 768507.937500, "args": {"Step": 2}},
{"name": "SendBufferWait", "cat": "NET", "ph": "e", "id": 16, "pid": 4157654, "tid": 1, "ts": 768508.017578},
{"name": "SendGpuWait", "cat": "NET", "ph": "b", "id": 16, "pid": 4157654, "tid": 1, "ts": 768508.017578, "args": {"Step": 2}},
{"name": "SendGpuWait", "cat": "NET", "ph": "e", "id": 16, "pid": 4157654, "tid": 1, "ts": 770002.129883},
{"name": "SendWait", "cat": "NET", "ph": "b", "id": 16, "pid": 4157654, "tid": 1, "ts": 770002.129883, "args": {"Step": 2}},
{"name": "SendWait", "cat": "NET", "ph": "e", "id": 16, "pid": 4157654, "tid": 1, "ts": 770013.848633},
{"name": "SendBufferWait", "cat": "NET", "ph": "b", "id": 17, "pid": 4157654, "tid": 1, "ts": 768510.742188, "args": {"Step": 3}},
{"name": "SendBufferWait", "cat": "NET", "ph": "e", "id": 17, "pid": 4157654, "tid": 1, "ts": 768510.822266},
{"name": "SendGpuWait", "cat": "NET", "ph": "b", "id": 17, "pid": 4157654, "tid": 1, "ts": 768510.822266, "args": {"Step": 3}},
{"name": "SendGpuWait", "cat": "NET", "ph": "e", "id": 17, "pid": 4157654, "tid": 1, "ts": 771461.563477},
{"name": "SendWait", "cat": "NET", "ph": "b", "id": 17, "pid": 4157654, "tid": 1, "ts": 771461.563477, "args": {"Step": 3}},
{"name": "SendWait", "cat": "NET", "ph": "e", "id": 17, "pid": 4157654, "tid": 1, "ts": 771469.171875},
... [ trace truncated for brevity ]
{"name": "AllReduce", "cat": "COLL", "ph": "e", "id": 0, "pid": 4157654, "tid": 1, "ts": 772209.317383},
{"name": "Group", "cat": "GROUP", "ph": "e", "id": 0, "pid": 4157654, "tid": 1, "ts": 772209.418945},
{}]
```
Details about the fields used in the trace can be found at this link:
https://docs.google.com/document/d/1CvAClvFfyA5R-PhYUmn5OOQtYMH4h6I0nSsKchNAySU/preview?tab=t.0#heading=h.yr4qxyxotyw
The trace above is obtained by running a `ncclAllReduce` operation on 8 GPUs, communicating with each other through
the network interface. The `Group` event encloses all traces that are related to the single `ncclAllReduce` call.
(Note that for single collective invocations, where there are no explicit group calls, NCCL creates a group with only
one collective and this is what is presented in the traces above).
The `AllReduce` event encloses traces for the proxy operation associated to the `ncclAllReduce` operation. The `args`
field in the traces contains NCCL specific information (aside from the chrome trace event format).
## AllReduce trace
The `AllReduce` entry presents information about the `ncclAllReduce` operation. It contains the following info in the args field:
- seqNum : sequential number of the collective in the communicator (every collective type has its own sequence number in the communicator)
- commHash : communicator unique identifier
- rank : NCCL rank for the ncclAllReduce
- datatype : NCCL datatype
- algorithm : algorithm used to process the ncclAllReduce
- protocol : protocol used to process the ncclAllReduce
- nMaxChannels: max number of channels used to process the ncclAllReduce
If the proxy events are not active (e.g., the `ncclAllReduce` is intranode) the end timestamp will match the time
consumed by the CPU to launch the collective. For more details refer to `ext-profiler/README.md`, section `Profiling
of collective and p2p operations`.
### Proxy Send
The `Send` entry presents information about the `ProxyOp` processing in the progress thread. It contains the following
info in the args field:
- Channel : id of the channel used by this proxy operation to send data to the peer
- Peer : peer rank
- Steps : number of network steps required to transfer transSize bytes to the peer
- ChunkSize : chunk size used by NCCL to pipeline data through the proxy thread
- transSize : bytes transferred across the channel by this proxy operation
- POSTED : struct containing the number of buffer posts to the GPU and the time stamp for the last post
- REM_FIFO_WAIT: struct containing the number of remote buffer waits and the time stamp for the last wait
- TRANSMITTED : struct containing the number of network sends and the time stamp of the last send
- DONE : struct containing the number of network sends completed and the time stamp of the last send completed
In case of a network problem the POSTED, REM_FIFO_WAIT, TRANSMITTED and DONE might all have partially updated steps,
which could help identify at which point the network problem occurred.
The Proxy send trace gives a summary of the proxy progress thread activity for the channel. If more details are
needed, these can be obtained by enabling the proxy step event (`ncclProfileProxyStep`). In which case the trace
entries below are also reported by the profiler.
#### Proxy SendBufferWait
Presents, for every network step, the time the CPU proxy spends waiting for the channel staging buffer to become available.
#### Proxy SendGPUWait
Presents, for every network step, the time the CPU proxy spends waiting for the GPU to provide the data in the staging
buffer.
#### Proxy SendWait
Presents, for every network step, the time the CPU proxy spends waiting for the `isend` to complete
### Proxy Recv
The `Recv` entry presents information about the `ProxyOp` processing in the progress thread. It contains the following
info in the args field:
- Channel : id of the channel used by this proxy operation to recv data from the peer
- Peer : peer rank
- Steps : number of network steps required to transfer transSize bytes from the peer
- ChunkSize : chunk size used by NCCL to pipeline data through the proxy thread
- transSize : bytes transferred across the channel by this proxy operation
- POSTED : struct containing the number of recvs posted and the time stamp for the last recv posted
- RECEIVED : struct containing the number of recvs completed and the time stamp for the last recv completed
- TRANSMITTED: struct containing the number of recvs flushed to the GPU memory and the time stamp for the last recv flushed
- DONE : struct containing the number of flush completed and the time stamp for the last flush completed
The Proxy Recv trace gives a summary of the proxy progress thread activity for the channel. If more details are
needed, these can be obtained by enabling the proxy step event (`ncclProfileProxyStep`). In which case the trace
entries below are also reported by the profiler.
#### Proxy RecvBufferWait
Presents, for every network step, the time the CPU proxy spends waiting for the staging buffer for the channel to
become available.
#### Proxy RecvWait
Presents, for every network step, the time the CPU proxy spends waiting for a posted `irecv` to complete
#### Proxy RecvFlushWait
Presents, for every network step, the time the CPU proxy spends waitng for the recv data to be flushed to the GPU
#### Proxy RecvGPUWait
Presents, for every network step, the time the CPU proxy spends waiting for the GPU to consume the recv data

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/*************************************************************************
* Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#include <stdio.h>
#include "event.h"
int taskEventQueueEmpty(struct group* g) {
return g->eventHead == NULL;
}
void taskEventQueueEnqueue(struct group* g, struct taskEventBase* event) {
event->next = NULL;
if (g->eventHead) g->eventTail->next = event;
else g->eventHead = event;
g->eventTail = event;
}
struct taskEventBase* taskEventQueueHead(struct group* g) {
return g->eventHead;
}
struct taskEventBase* taskEventQueueDequeue(struct group* g) {
struct taskEventBase* tmp = g->eventHead;
g->eventHead = g->eventHead->next;
if (g->eventHead == NULL) g->eventTail = NULL;
return tmp;
}

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/*************************************************************************
* Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#ifndef EVENT_H_
#define EVENT_H_
#include <sys/types.h>
#include <stdint.h>
#include <unistd.h>
#include "profiler.h"
#define MAX_CHANNELS 32
#define MAX_STEPS 16
#define MAX_OPS 16 // Up to 64K ranks for PAT
#define MAX_EVENTS_PER_REQ (8)
struct proxyOp;
struct proxyStep;
struct netPlugin {
uint8_t type;
int pluginType;
int pluginVer;
uint8_t pluginEvent;
union {
struct {
int device;
int qpNum;
int opcode;
uint64_t wr_id;
size_t length;
} qp;
struct {
int fd;
int op;
size_t length;
} sock;
};
double startTs;
double stopTs;
struct proxyStep* parent;
};
struct kernelCh {
uint8_t type;
uint8_t channelId;
struct taskEventBase* parent;
double startTs;
double stopTs;
uint64_t startGpuClk;
uint64_t stopGpuClk;
};
#define PROXY_STEP_SEND_GPU_WAIT 0
#define PROXY_STEP_SEND_PEER_WAIT 1
#define PROXY_STEP_SEND_WAIT 2
#define PROXY_STEP_RECV_WAIT 0
#define PROXY_STEP_RECV_FLUSH_WAIT 1
#define PROXY_STEP_RECV_GPU_WAIT 2
#define PROXY_STEP_MAX_STATES 3
struct proxyStep {
uint8_t type; // type of event: network transfer
int state;
int step; // network transfer id in given channel
int isSend; // send/recv channel operation
double timestamp[PROXY_STEP_MAX_STATES];
double startTs;
double stopTs;
struct proxyOp* parent;
struct netPlugin net[MAX_EVENTS_PER_REQ];
int nNetEvents;
};
struct proxyOp {
uint8_t type; // type of event: proxy operation
uint8_t channelId; // channel id for this proxy operation
pid_t pid;
int rank;
int peer; // peer rank for this proxy operation
int nSteps; // total number of network transfers for this proxy operation
int chunkSize; // chunk size for this proxy operation
int isSend; // send/recv channel operation
size_t transSize; // transfer data size for this proxy operation
double startTs;
double progrTs; // In progress state transition
double stopTs;
int stepCount; // last processed network operation for this proxy operation
struct proxyStep step[MAX_STEPS]; // array of network transfer events
struct taskEventBase* parent; // parent event p2p/collective
};
struct group;
struct context;
struct proxyCtrl {
uint8_t type;
struct context* ctx; // profiler context
double startTs;
double stopTs;
int state;
int appended; // appended proxy operations
};
// task level event base structure
struct taskEventBase {
uint8_t type; // event type: collective/p2p
int rank; // rank of the operation in NCCL communicator
const char* func; // ncclFunc*
int refCount; // number of references for this operation
struct group* parent; // parent event group
struct taskEventBase* next; // next top level event in group
double startTs;
double stopTs;
};
struct collective {
struct taskEventBase base; // base structure for this event
uint64_t seqNumber; // sequence number for this collective in communicator
void const* sendBuff;
void* recvBuff;
size_t count;
int root;
const char* datatype;
uint8_t nChannels;
const char* algo;
const char* proto;
int nWarps;
struct proxyOp op[MAX_CHANNELS][2*MAX_OPS];
int nProxyOps[MAX_CHANNELS];
struct kernelCh kernel[MAX_CHANNELS];
};
struct p2p {
struct taskEventBase base; // base structure for this event
uint8_t func;
void const* buff;
size_t count;
const char* datatype;
int peer;
uint8_t nChannels;
struct proxyOp op[MAX_CHANNELS];
struct kernelCh kernel[MAX_CHANNELS];
};
struct group {
uint8_t type;
struct context* ctx; // profiler context
int groupId;
int refCount;
struct taskEventBase* eventHead; // queue head for task events
struct taskEventBase* eventTail; // queue tail for task events
double startTs;
double stopTs;
struct group* next; // next group event in queue
};
// arrays for different event objects
struct context {
const char* commName;
uint64_t commHash;
int nranks;
int rank;
int groupPoolSize;
int groupPoolBase;
int groupPoolIndex;
struct group* groupPool;
int collPoolSize;
int collPoolBase;
int collPoolIndex;
struct collective* collPool;
int p2pPoolSize;
int p2pPoolBase;
int p2pPoolIndex;
struct p2p* p2pPool;
int proxyCtrlPoolSize;
int proxyCtrlPoolBase;
int proxyCtrlPoolIndex;
struct proxyCtrl* proxyCtrlPool;
};
int taskEventQueueEmpty(struct group* g);
void taskEventQueueEnqueue(struct group* g, struct taskEventBase* event);
struct taskEventBase* taskEventQueueHead(struct group* g);
struct taskEventBase* taskEventQueueDequeue(struct group* g);
#endif

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/*************************************************************************
* Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#ifndef COMMON_H_
#define COMMON_H_
typedef enum {NCCL_LOG_NONE=0, NCCL_LOG_VERSION=1, NCCL_LOG_WARN=2, NCCL_LOG_INFO=3, NCCL_LOG_ABORT=4, NCCL_LOG_TRACE=5} ncclDebugLogLevel;
typedef enum {NCCL_INIT=1, NCCL_COLL=2, NCCL_P2P=4, NCCL_SHM=8, NCCL_NET=16, NCCL_GRAPH=32, NCCL_TUNING=64, NCCL_ENV=128, NCCL_ALLOC=256, NCCL_CALL=512, NCCL_PROXY=1024, NCCL_NVLS=2048, NCCL_BOOTSTRAP=4096, NCCL_REG=8192, NCCL_ALL=~0} ncclDebugLogSubSys;
typedef void (*ncclDebugLogger_t)(ncclDebugLogLevel level, unsigned long flags, const char *file, int line, const char *fmt, ...);
#endif

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/*************************************************************************
* Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#ifndef NCCL_ERR_H_
#define NCCL_ERR_H_
/* Error type for plugins */
typedef enum { ncclSuccess = 0,
ncclUnhandledCudaError = 1,
ncclSystemError = 2,
ncclInternalError = 3,
ncclInvalidArgument = 4,
ncclInvalidUsage = 5,
ncclRemoteError = 6 } ncclResult_t;
#endif

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/*************************************************************************
* Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#ifndef NET_IB_V1_H_
#define NET_IB_V1_H_
#define NCCL_PROFILER_NET_IB_VER 1
enum {
ncclProfileQp = (1 << 0),
};
// The data structure version is encoded in the plugin identifier bitmask and
// passed to NCCL core through the profiler callback. NCCL copies the plugin
// identifier in the event descriptor before calling the profiler startEvent
// function. The profiler should inspect the plugin id to find out the source
// plugin as well as the version of the event struct
typedef struct {
uint8_t type; // event type (plugin defined)
union {
struct {
int device; // network device id
uint64_t wr_id; // work request id
int opcode; // ibv opcode
int qpNum; // QP number
size_t length; // work request data length
} qp;
};
} ncclProfilerNetIbDescr_v1_t;
#endif

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/*************************************************************************
* Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#ifndef NET_SOCKET_V1_H_
#define NET_SOCKET_V1_H_
#define NCCL_PROFILER_NET_SOCKET_VER 1
enum {
ncclProfileSocket = (1 << 0),
};
// The data structure version is encoded in the plugin identifier bitmask and
// passed to NCCL core through the profiler callback. NCCL copies the plugin
// identifier in the event descriptor before calling the profiler startEvent
// function. The profiler should inspect the plugin id to find out the source
// plugin as well as the version of the event struct
typedef struct {
uint8_t type; // event type (plugin defined)
union {
struct {
int fd;
int op;
size_t length;
} sock;
};
} ncclProfilerNetSockDescr_v1_t;
#endif

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/*************************************************************************
* Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#ifndef PROFILER_H_
#define PROFILER_H_
#include <stdint.h>
#include <stdlib.h>
#include "common.h"
#include "err.h"
enum {
ncclProfileGroup = (1 << 0), // group event type
ncclProfileColl = (1 << 1), // host collective call event type
ncclProfileP2p = (1 << 2), // host point-to-point call event type
ncclProfileProxyOp = (1 << 3), // proxy operation event type
ncclProfileProxyStep = (1 << 4), // proxy step event type
ncclProfileProxyCtrl = (1 << 5), // proxy control event type
ncclProfileKernelCh = (1 << 6), // kernel channel event type
ncclProfileNetPlugin = (1 << 7), // network plugin-defined, events
};
typedef enum {
ncclProfilerProxyOpSendPosted = 0, // deprecated in v4
ncclProfilerProxyOpSendRemFifoWait = 1, // deprecated in v4
ncclProfilerProxyOpSendTransmitted = 2, // deprecated in v4
ncclProfilerProxyOpSendDone = 3, // deprecated in v4
ncclProfilerProxyOpRecvPosted = 4, // deprecated in v4
ncclProfilerProxyOpRecvReceived = 5, // deprecated in v4
ncclProfilerProxyOpRecvTransmitted = 6, // deprecated in v4
ncclProfilerProxyOpRecvDone = 7, // deprecated in v4
ncclProfilerProxyOpInProgress_v4 = 19,
/* Legacy proxy profiler states */
ncclProfilerProxyStepSendGPUWait = 8,
ncclProfilerProxyStepSendPeerWait_v4 = 20,
ncclProfilerProxyStepSendWait = 9,
ncclProfilerProxyStepRecvWait = 10,
ncclProfilerProxyStepRecvFlushWait = 11,
ncclProfilerProxyStepRecvGPUWait = 12,
/* Legacy proxy control states */
ncclProfilerProxyCtrlIdle = 13,
ncclProfilerProxyCtrlActive = 14,
ncclProfilerProxyCtrlSleep = 15,
ncclProfilerProxyCtrlWakeup = 16,
ncclProfilerProxyCtrlAppend = 17,
ncclProfilerProxyCtrlAppendEnd = 18,
/* Network defined events states */
ncclProfilerNetPluginUpdate = 21,
/* Kernel event states */
ncclProfilerKernelChStop = 22,
} ncclProfilerEventState_t;
typedef ncclProfilerEventState_t ncclProfilerEventState_v1_t;
typedef ncclProfilerEventState_t ncclProfilerEventState_v2_t;
typedef ncclProfilerEventState_t ncclProfilerEventState_v3_t;
typedef ncclProfilerEventState_t ncclProfilerEventState_v4_t;
#include "profiler_v4.h"
#include "profiler_v3.h"
#include "profiler_v2.h"
#include "profiler_v1.h"
#include "profiler_net.h"
typedef ncclProfiler_v4_t ncclProfiler_t;
typedef ncclProfilerEventDescr_v4_t ncclProfilerEventDescr_t;
typedef ncclProfilerEventStateArgs_v4_t ncclProfilerEventStateArgs_t;
#endif // end include guard

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/*************************************************************************
* Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#ifndef PROFILER_NET_H_
#define PROFILER_NET_H_
#define NCCL_PROFILER_NET_VER_BITS (16)
#define NCCL_PROFILER_NET_VER_MASK (~0U >> NCCL_PROFILER_NET_VER_BITS)
#define NCCL_PROFILER_NET_TYPE_MASK (~0U << NCCL_PROFILER_NET_VER_BITS)
typedef enum {
NCCL_PROFILER_NET_TYPE_IB = (1U << NCCL_PROFILER_NET_VER_BITS),
NCCL_PROFILER_NET_TYPE_SOCK = (2U << NCCL_PROFILER_NET_VER_BITS),
} ncclProfilerNetType;
#include "net_ib_v1.h"
#include "net_socket_v1.h"
#endif

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/*************************************************************************
* Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#ifndef PROFILER_V1_H_
#define PROFILER_V1_H_
#include <stdint.h>
typedef struct {
uint8_t type; // event type descriptor: ncclProfileColl, ...
void* parentObj; // pointer to the profiler parent object (for coll is the group)
int rank; // originating rank
union {
struct {
const char* name;
uint64_t commHash;
uint64_t seqNumber;
uint8_t func;
void const* sendBuff;
void* recvBuff;
size_t count;
int root;
uint8_t datatype;
uint32_t op;
size_t trafficBytes;
uint8_t nMaxChannels;
uint8_t nWarps;
uint8_t algo;
uint8_t proto;
int isCollnet;
int isNvls;
} coll;
struct {
const char* name;
uint64_t commHash;
uint8_t func;
void* buff;
uint8_t datatype;
size_t count;
int peer;
} p2p;
struct {
pid_t pid; // pid of the originating process
uint8_t channelId; // channel id for this proxy operation
int peer; // remote rank for send/recv
int nSteps; // number of steps for this proxy operation
int chunkSize; // amount of data transferred by this proxy operation
int isSend;
} proxyOp;
struct {
int step;
} proxyStep;
};
} ncclProfilerEventDescr_v1_t;
typedef union {
struct {
size_t transSize;
int steps;
} proxyOp;
struct {
int appendedProxyOps;
} proxyCtrl;
} ncclProfilerEventStateArgs_v1_t;
typedef struct {
const char* name;
// init - initialize the profiler plugin
// Input
// - context : opaque profiler context object for separating profiler behavior across comms
// Output
// - eActivationMask: bitmask of active events set by the plugin
ncclResult_t (*init)(void** context, int* eActivationMask);
// startEvent - initialize and start a new event for the supplied event descriptor inside the eventset
// Input
// - context: opaque profiler context object
// - eDescr : pointer to ncclProfilerEventDescr_t object
// Output
// - eHandle: return event handle for supplied event descriptor object
ncclResult_t (*startEvent)(void* context, void** eHandle, ncclProfilerEventDescr_v1_t* eDescr);
// stopEvent - stop/finalize an event inside and event set
// Input
// - eHandle: handle to event object
ncclResult_t (*stopEvent)(void* eHandle);
// recordEventState - record event state transitions and event attribute updates
// Input
// - eHandle : handle to event object created through startEvent
// - eStateArgs: optional argument used to capture event attribute updates associated with the state transition
// - eState : event state transition
ncclResult_t (*recordEventState)(void* eHandle, ncclProfilerEventState_v1_t eState, ncclProfilerEventStateArgs_v1_t* eStateArgs);
// finalize - finalize the profiler plugin
// Input
// - context: opaque profiler context object
ncclResult_t (*finalize)(void* context);
} ncclProfiler_v1_t;
#endif

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/*************************************************************************
* Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#ifndef PROFILER_V2_H_
#define PROFILER_V2_H_
#include <stdint.h>
typedef struct {
uint8_t type; // event type descriptor: ncclProfileColl, ...
void* parentObj; // pointer to the profiler parent object (for coll is the group)
int rank; // originating rank
union {
struct {
const char* name;
uint64_t commHash;
uint64_t seqNumber;
const char* func;
void const* sendBuff;
void* recvBuff;
size_t count;
int root;
const char* datatype;
size_t trafficBytes;
uint8_t nMaxChannels;
uint8_t nWarps;
const char* algo;
const char* proto;
} coll;
struct {
const char* name;
uint64_t commHash;
const char* func;
void* buff;
const char* datatype;
size_t count;
int peer;
} p2p;
struct {
pid_t pid; // pid of the originating process
uint8_t channelId; // channel id for this proxy operation
int peer; // remote rank for send/recv
int nSteps; // number of steps for this proxy operation
int chunkSize; // amount of data transferred by this proxy operation
int isSend;
} proxyOp;
struct {
int step;
} proxyStep;
};
} ncclProfilerEventDescr_v2_t;
typedef union {
struct {
size_t transSize;
int steps;
} proxyOp;
struct {
int appendedProxyOps;
} proxyCtrl;
} ncclProfilerEventStateArgs_v2_t;
typedef struct {
const char* name;
// init - initialize the profiler plugin
// Input
// - context : opaque profiler context object for separating profiler behavior across comms
// Output
// - eActivationMask: bitmask of active events set by the plugin
ncclResult_t (*init)(void** context, int* eActivationMask);
// startEvent - initialize and start a new event for the supplied event descriptor inside the eventset
// Input
// - context: opaque profiler context object
// - eDescr : pointer to ncclProfilerEventDescr_t object
// Output
// - eHandle: return event handle for supplied event descriptor object
ncclResult_t (*startEvent)(void* context, void** eHandle, ncclProfilerEventDescr_v2_t* eDescr);
// stopEvent - stop/finalize an event inside and event set
// Input
// - eHandle: handle to event object
ncclResult_t (*stopEvent)(void* eHandle);
// recordEventState - record event state transitions and event attribute updates
// Input
// - eHandle : handle to event object created through startEvent
// - eStateArgs: optional argument used to capture event attribute updates associated with the state transition
// - eState : event state transition
ncclResult_t (*recordEventState)(void* eHandle, ncclProfilerEventState_v2_t eState, ncclProfilerEventStateArgs_v2_t* eStateArgs);
// finalize - finalize the profiler plugin
// Input
// - context: opaque profiler context object
ncclResult_t (*finalize)(void* context);
} ncclProfiler_v2_t;
#endif

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/*************************************************************************
* Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#ifndef PROFILER_V3_H_
#define PROFILER_V3_H_
#include <stdint.h>
typedef struct {
uint8_t type; // event type descriptor: ncclProfileColl, ...
void* parentObj; // pointer to the profiler parent object (for coll is the group)
int rank; // originating rank
union {
struct {
const char* name;
uint64_t commHash;
uint64_t seqNumber;
const char* func;
void const* sendBuff;
void* recvBuff;
size_t count;
int root;
const char* datatype;
uint8_t nMaxChannels;
uint8_t nWarps;
const char* algo;
const char* proto;
} coll;
struct {
const char* name;
uint64_t commHash;
const char* func;
void* buff;
const char* datatype;
size_t count;
int peer;
} p2p;
struct {
pid_t pid; // pid of the originating process
uint8_t channelId; // channel id for this proxy operation
int peer; // remote rank for send/recv
int nSteps; // number of steps for this proxy operation
int chunkSize; // amount of data transferred by this proxy operation
int isSend;
} proxyOp;
struct {
int step;
} proxyStep;
struct {
uint8_t channelId;
} kernelCh;
struct {
int64_t id;
void* data;
} netPlugin;
};
} ncclProfilerEventDescr_v3_t;
typedef union {
struct {
size_t transSize;
int steps;
} proxyOp;
struct {
int appendedProxyOps;
} proxyCtrl;
} ncclProfilerEventStateArgs_v3_t;
typedef struct {
const char* name;
// init - initialize the profiler plugin
// Input
// - context : opaque profiler context object for separating profiler behavior across comms
// Output
// - eActivationMask: bitmask of active events set by the plugin
ncclResult_t (*init)(void** context, int* eActivationMask);
// startEvent - initialize and start a new event for the supplied event descriptor inside the eventset
// Input
// - context: opaque profiler context object
// - eDescr : pointer to ncclProfilerEventDescr_t object
// Output
// - eHandle: return event handle for supplied event descriptor object
ncclResult_t (*startEvent)(void* context, void** eHandle, ncclProfilerEventDescr_v3_t* eDescr);
// stopEvent - stop/finalize an event inside and event set
// Input
// - eHandle: handle to event object
ncclResult_t (*stopEvent)(void* eHandle);
// recordEventState - record event state transitions and event attribute updates
// Input
// - eHandle : handle to event object created through startEvent
// - eStateArgs: optional argument used to capture event attribute updates associated with the state transition
// - eState : event state transition
ncclResult_t (*recordEventState)(void* eHandle, ncclProfilerEventState_v3_t eState, ncclProfilerEventStateArgs_v3_t* eStateArgs);
// finalize - finalize the profiler plugin
// Input
// - context: opaque profiler context object
ncclResult_t (*finalize)(void* context);
} ncclProfiler_v3_t;
#endif

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/*************************************************************************
* Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#ifndef PROFILER_V4_H_
#define PROFILER_V4_H_
typedef struct {
uint8_t type; // event type descriptor: ncclProfileColl, ...
void* parentObj; // pointer to the profiler parent object (for coll is the group)
int rank; // originating rank
union {
struct {
uint64_t seqNumber;
const char* func;
void const* sendBuff;
void* recvBuff;
size_t count;
int root;
const char* datatype;
uint8_t nChannels;
uint8_t nWarps;
const char* algo;
const char* proto;
} coll;
struct {
const char* func;
void* buff;
const char* datatype;
size_t count;
int peer;
uint8_t nChannels;
} p2p;
struct {
pid_t pid; // pid of the originating process
uint8_t channelId; // channel id for this proxy operation
int peer; // remote rank for send/recv
int nSteps; // number of steps for this proxy operation
int chunkSize; // amount of data transferred by this proxy operation
int isSend;
} proxyOp;
struct {
int step;
} proxyStep;
struct {
uint8_t channelId;
uint64_t pTimer; // start timestamp from GPU globaltimer
} kernelCh;
struct {
int64_t id;
void* data;
} netPlugin;
};
} ncclProfilerEventDescr_v4_t;
typedef union {
struct {
size_t transSize;
} proxyStep;
struct {
int appendedProxyOps;
} proxyCtrl;
struct {
void* data;
} netPlugin;
struct {
uint64_t pTimer;
} kernelCh;
} ncclProfilerEventStateArgs_v4_t;
typedef struct {
const char* name;
// init - initialize the profiler plugin
// Input
// - context : opaque profiler context object for separating profiler behavior across comms
// - commName : user assigned communicator name
// - commHash : communicator id
// - nNodes : number of nodes in communicator
// - nranks : number of ranks in communciator
// - rank : rank identifier in communicator
// - logfn : logger function
// Output
// - eActivationMask: bitmask of active events set by the plugin
ncclResult_t (*init)(void** context, int* eActivationMask, const char* commName, uint64_t commHash, int nNodes, int nranks, int rank, ncclDebugLogger_t logfn);
// startEvent - initialize and start a new event for the supplied event descriptor inside the eventset
// Input
// - context: opaque profiler context object
// - eDescr : pointer to ncclProfilerEventDescr_t object
// Output
// - eHandle: return event handle for supplied event descriptor object
ncclResult_t (*startEvent)(void* context, void** eHandle, ncclProfilerEventDescr_v4_t* eDescr);
// stopEvent - stop/finalize an event inside and event set
// Input
// - eHandle: handle to event object
ncclResult_t (*stopEvent)(void* eHandle);
// recordEventState - record event state transitions and event attribute updates
// Input
// - eHandle : handle to event object created through startEvent
// - eStateArgs: optional argument used to capture event attribute updates associated with the state transition
// - eState : event state transition
ncclResult_t (*recordEventState)(void* eHandle, ncclProfilerEventState_v4_t eState, ncclProfilerEventStateArgs_v4_t* eStateArgs);
// finalize - finalize the profiler plugin
// Input
// - context: opaque profiler context object
ncclResult_t (*finalize)(void* context);
} ncclProfiler_v4_t;
#endif

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/*
* Copyright (c) 2017-2022, NVIDIA CORPORATION. All rights reserved.
*/
#ifndef NCCL_TYPES_H_
#define NCCL_TYPES_H_
/* Data types */
typedef enum { ncclInt8 = 0, ncclChar = 0,
ncclUint8 = 1,
ncclInt32 = 2, ncclInt = 2,
ncclUint32 = 3,
ncclInt64 = 4,
ncclUint64 = 5,
ncclFloat16 = 6, ncclHalf = 6,
ncclFloat32 = 7, ncclFloat = 7,
ncclFloat64 = 8, ncclDouble = 8,
ncclBfloat16 = 9,
} ncclDataType_t;
#endif

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/*************************************************************************
* Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#include <stdio.h>
#include <pthread.h>
#include <string.h>
#include <linux/limits.h>
#include <sys/time.h>
#include <sys/types.h>
#include <sys/syscall.h>
#include <unistd.h>
#include <time.h>
#include "event.h"
#include "print_event.h"
#define __hidden __attribute__ ((visibility("hidden")))
static int initialized; // initialization counter for profiler
static double startTime; // profiler start time
static const int defaultEActivationMask = ncclProfileColl | ncclProfileP2p;
static const int defaultGroupPoolSize = 16;
static const int defaultCollPoolSize = 16;
static const int defaultP2pPoolSize = 1024;
static const int defaultProxyCtrlPoolSize = 16;
static const int defaultDetachPoolSize = 128;
static int groupPoolSize;
static int collPoolSize;
static int p2pPoolSize;
static int proxyCtrlPoolSize;
static int detachPoolSize;
static int detachPoolBase;
static int detachPoolIndex;
static int detachPoolDone;
static struct proxyOp* detachPool;
ncclDebugLogger_t logFn;
#define INFO(FLAGS, ...) logFn(NCCL_LOG_INFO, (FLAGS), __func__, __LINE__, __VA_ARGS__)
__hidden double gettime(void) {
struct timespec t;
clock_gettime(CLOCK_MONOTONIC, &t);
return (t.tv_sec*1e6 + (t.tv_nsec*1e-3));
}
static pthread_mutex_t lock = PTHREAD_MUTEX_INITIALIZER;
static pid_t pid;
static int* eActivationMaskPtr;
__hidden ncclResult_t exampleProfilerInit(void** context, int* eActivationMask, const char* commName, uint64_t commHash, int nNodes, int nranks, int rank, ncclDebugLogger_t logfn) {
pthread_mutex_lock(&lock);
if (__atomic_fetch_add(&initialized, 1, __ATOMIC_RELAXED) == 0) {
// first thread initializes event mask, environment and detach pool
const char* str;
str = getenv("NCCL_PROFILE_EVENT_MASK");
__atomic_store_n(eActivationMask, str ? atoi(str) : 0, __ATOMIC_RELAXED);
str = getenv("NCCL_PROFILE_GROUP_POOL_SIZE");
groupPoolSize = str ? atoi(str) : defaultGroupPoolSize;
str = getenv("NCCL_PROFILE_COLL_POOL_SIZE");
collPoolSize = str ? atoi(str) : defaultCollPoolSize;
str = getenv("NCCL_PROFILE_P2P_POOL_SIZE");
p2pPoolSize = str ? atoi(str) : defaultP2pPoolSize;
str = getenv("NCCL_PROFILE_PROXY_CTRL_POOL_SIZE");
proxyCtrlPoolSize = str ? atoi(str) : defaultProxyCtrlPoolSize;
str = getenv("NCCL_PROFILE_PROXY_DETACH_POOL_SIZE");
detachPoolSize = str ? atoi(str) : defaultDetachPoolSize;
// detach pool is used to store PXN proxyOps and is shared among threads
detachPool = (struct proxyOp *)calloc(detachPoolSize, sizeof(*detachPool));
if (detachPool == NULL) {
pthread_mutex_unlock(&lock);
return ncclSystemError;
}
// Pid of the process initializing the profiler first.
// This is compared against the pid of proxyOp events
// to figure out if they have a parent event in this
// process address space.
pid = getpid();
startTime = gettime();
}
pthread_mutex_unlock(&lock);
// store pointer to activation mask globally
eActivationMaskPtr = eActivationMask;
// pre-allocate memory for event object pools in dedicated profiler context
struct context* ctx = (struct context *)calloc(1, sizeof(*ctx));
ctx->commName = commName;
ctx->commHash = commHash;
ctx->nranks = nranks;
ctx->rank = rank;
logFn = logfn;
INFO(NCCL_INIT, "PROFILER/Plugin: init commName: %s commHash: %lu nranks: %d rank: %d", commName ? commName : "", commHash, nranks, rank);
ctx->groupPool = (struct group *)calloc(groupPoolSize, sizeof(*ctx->groupPool));
if (ctx->groupPool == NULL) goto fail;
ctx->collPool = (struct collective *)calloc(collPoolSize, sizeof(*ctx->collPool));
if (ctx->collPool == NULL) goto fail;
ctx->p2pPool = (struct p2p *)calloc(p2pPoolSize, sizeof(*ctx->p2pPool));
if (ctx->p2pPool == NULL) goto fail;
ctx->proxyCtrlPool = (struct proxyCtrl *)calloc(proxyCtrlPoolSize, sizeof(*ctx->proxyCtrlPool));
if (ctx->proxyCtrlPool == NULL) goto fail;
// Print event pool sizes for debugging
//fprintf(stdout, "Profiler: Group pool size (bytes): %lu\n", sizeof(struct group)*groupPoolSize);
//fprintf(stdout, "Profiler: Coll pool size (bytes): %lu\n", sizeof(struct collective)*collPoolSize);
//fprintf(stdout, "Profiler: P2p pool size (bytes): %lu\n", sizeof(struct p2p)*p2pPoolSize);
//fprintf(stdout, "Profiler: Proxy pool size (bytes): %lu\n", sizeof(struct proxyCtrl)*proxyCtrlPoolSize);
//fprintf(stdout, "Profiler: PXN pool size (bytes): %lu\n", sizeof(struct proxyOp)*detachPoolSize);
*context = ctx;
return ncclSuccess;
fail:
// cleanup resources
if (ctx->proxyCtrlPool) free(ctx->proxyCtrlPool);
if (ctx->p2pPool) free(ctx->p2pPool);
if (ctx->collPool) free(ctx->collPool);
if (ctx->groupPool) free(ctx->groupPool);
free(ctx);
if (detachPool) free(detachPool);
return ncclSystemError;
}
__hidden ncclResult_t exampleProfilerFinalize(void* context) {
FILE* fh = NULL;
char filename[PATH_MAX] = { 0 };
struct context* ctx = (struct context *)context;
const char* dump = getenv("NCCL_PROFILE_DUMP_FILE");
if (dump) {
sprintf(filename, "%s_%lu_%d.json", dump, ctx->commHash, ctx->rank);
fh = fopen(filename, "w");
fprintf(fh, "[\n");
}
INFO(NCCL_INIT, "PROFILER/Plugin: finalize commName: %s commHash: %lu nranks: %d rank: %d", ctx->commName ? ctx->commName : "", ctx->commHash, ctx->nranks, ctx->rank);
// print last N groups/collectives/p2ps
int start = (ctx->groupPoolIndex - groupPoolSize >= 0) ? ctx->groupPoolIndex - groupPoolSize : 0;
int end = ctx->groupPoolIndex;
for (int i = start; i < end; i++) {
printEvent(fh, &ctx->groupPool[i%groupPoolSize]);
}
start = (ctx->proxyCtrlPoolIndex - proxyCtrlPoolSize >= 0) ? ctx->proxyCtrlPoolIndex - proxyCtrlPoolSize : 0;
end = ctx->proxyCtrlPoolIndex;
for (int i = start; i < end; i++) {
printEvent(fh, &ctx->proxyCtrlPool[i%proxyCtrlPoolSize]);
}
free(ctx->groupPool);
free(ctx->collPool);
free(ctx->p2pPool);
free(ctx->proxyCtrlPool);
free(ctx);
// last thread cleans up shared detach pool
if (__atomic_sub_fetch(&initialized, 1, __ATOMIC_RELAXED) == 0) {
start = (detachPoolIndex - detachPoolSize >= 0) ? detachPoolIndex - detachPoolSize : 0;
end = detachPoolIndex;
for (int i = start; i < end; i++) {
printEvent(fh, &detachPool[i%detachPoolSize]);
}
free(detachPool);
}
if (fh) fprintf(fh, "{}]\n");
if (fh) fclose(fh);
return ncclSuccess;
}
__hidden void updateEvent(void* handle);
__hidden ncclResult_t exampleProfilerStartEvent(void* context, void** eHandle, ncclProfilerEventDescr_t* eDescr) {
*eHandle = NULL;
struct context* ctx = (struct context *)context;
if (eDescr->type == ncclProfileGroup) {
struct group* event;
int groupId = __atomic_fetch_add(&ctx->groupPoolIndex, 1, __ATOMIC_RELAXED);
if ((groupId - __atomic_load_n(&ctx->groupPoolBase, __ATOMIC_RELAXED)) < groupPoolSize) {
// if there are available group events grab one
event = &ctx->groupPool[groupId%groupPoolSize];
while (!taskEventQueueEmpty(event)) {
struct taskEventBase* base = taskEventQueueDequeue(event);
if (base->type == ncclProfileColl) {
struct collective* c = (struct collective *)base;
// reset event proxyOps & proxySteps
memset(c->nProxyOps, 0, sizeof(int)*MAX_CHANNELS);
// release collective events in the group and return them to the collective pool
__atomic_fetch_add(&ctx->collPoolBase, 1, __ATOMIC_RELAXED);
} else if (base->type == ncclProfileP2p) {
struct p2p* p = (struct p2p *)base;
// reset event proxyOp and proxySteps
memset(&p->op, 0, sizeof(struct proxyOp)*MAX_CHANNELS);
// release p2p events in the group and return them to the p2p pool
__atomic_fetch_add(&ctx->p2pPoolBase, 1, __ATOMIC_RELAXED);
}
}
} else {
// else drop this event
__atomic_fetch_sub(&ctx->groupPoolIndex, 1, __ATOMIC_RELAXED);
return ncclSuccess;
}
event->type = ncclProfileGroup;
event->ctx = ctx;
event->groupId = groupId;
event->startTs = gettime() - startTime;
*eHandle = event;
debugEvent(event, "GroupStart");
} else if (eDescr->type == ncclProfileColl) {
// the parent might be null if we run out of events
struct group* parent = (struct group *)eDescr->parentObj;
if (parent == NULL) return ncclSuccess;
struct collective* event;
int collId = __atomic_fetch_add(&ctx->collPoolIndex, 1, __ATOMIC_RELAXED);
if ((collId - __atomic_load_n(&ctx->collPoolBase, __ATOMIC_RELAXED)) < collPoolSize) {
// if there are available collective events grab one
event = &ctx->collPool[collId%collPoolSize];
} else {
// else drop this event
__atomic_fetch_sub(&ctx->collPoolIndex, 1, __ATOMIC_RELAXED);
return ncclSuccess;
}
event->base.type = ncclProfileColl;
event->base.rank = eDescr->rank;
event->base.func = eDescr->coll.func;
event->base.startTs = gettime() - startTime;
event->base.parent = parent;
event->seqNumber = eDescr->coll.seqNumber;
event->sendBuff = eDescr->coll.sendBuff;
event->recvBuff = eDescr->coll.recvBuff;
event->count = eDescr->coll.count;
event->root = eDescr->coll.root;
event->datatype = eDescr->coll.datatype;
event->nChannels = eDescr->coll.nChannels;
event->nWarps = eDescr->coll.nWarps;
event->algo = eDescr->coll.algo;
event->proto = eDescr->coll.proto;
*eHandle = event;
taskEventQueueEnqueue(parent, (struct taskEventBase *)event);
// increment the group ref counter so the event will staty open
__atomic_fetch_add(&parent->refCount, 1, __ATOMIC_RELAXED);
debugEvent(event, "CollStart");
} else if (eDescr->type == ncclProfileP2p) {
// the parent might be null if we run out of events
struct group* parent = (struct group *)eDescr->parentObj;
if (parent == NULL) return ncclSuccess;
struct p2p* event;
int p2pId = __atomic_fetch_add(&ctx->p2pPoolIndex, 1, __ATOMIC_RELAXED);
if ((p2pId - __atomic_load_n(&ctx->p2pPoolBase, __ATOMIC_RELAXED)) < p2pPoolSize) {
// if there are available p2p events grab one
event = &ctx->p2pPool[p2pId%p2pPoolSize];
} else {
// else drop this event
__atomic_fetch_sub(&ctx->p2pPoolIndex, 1, __ATOMIC_RELAXED);
return ncclSuccess;
}
event->base.type = ncclProfileP2p;
event->base.rank = eDescr->rank;
event->base.func = eDescr->p2p.func;
event->base.next = parent->eventHead;
event->base.startTs = gettime() - startTime;
event->base.parent = parent;
event->buff = eDescr->p2p.buff;
event->count = eDescr->p2p.count;
event->datatype = eDescr->p2p.datatype;
event->peer = eDescr->p2p.peer;
event->nChannels = eDescr->p2p.nChannels;
*eHandle = event;
// increment the group ref counter so the event will staty open
taskEventQueueEnqueue(parent, (struct taskEventBase *)event);
__atomic_fetch_add(&parent->refCount, 1, __ATOMIC_RELAXED);
debugEvent(event, "P2pStart");
} else if (eDescr->type == ncclProfileProxyCtrl) {
int proxyCtrlId = __atomic_fetch_add(&ctx->proxyCtrlPoolIndex, 1, __ATOMIC_RELAXED);
struct proxyCtrl* event = &ctx->proxyCtrlPool[proxyCtrlId%proxyCtrlPoolSize];
event->type = ncclProfileProxyCtrl;
event->ctx = ctx;
event->startTs = gettime() - startTime;
*eHandle = event;
} else if (eDescr->type == ncclProfileProxyOp) {
// the eventBase might be null if we run out of events
struct taskEventBase* eventBase = (struct taskEventBase *)eDescr->parentObj;
if (eventBase == NULL) return ncclSuccess;
if (eDescr->proxyOp.pid != pid) {
// PXN captured proxyOp events
struct proxyOp* event;
int detachId = __atomic_fetch_add(&detachPoolIndex, 1, __ATOMIC_RELAXED);
if ((detachId - detachPoolBase) < detachPoolSize) {
// if there are available detached proxyOp events grab one
event = &detachPool[detachId%detachPoolSize];
} else {
// else drop this event
__atomic_fetch_sub(&detachPoolIndex, 1, __ATOMIC_RELAXED);
return ncclSuccess;
}
event->type = ncclProfileProxyOp;
event->channelId = eDescr->proxyOp.channelId;
event->pid = eDescr->proxyOp.pid;
event->rank = eDescr->rank;
event->peer = eDescr->proxyOp.peer;
event->nSteps = eDescr->proxyOp.nSteps;
event->chunkSize = eDescr->proxyOp.chunkSize;
event->isSend = eDescr->proxyOp.isSend;
event->startTs = gettime() - startTime;
event->parent = NULL;
event->stepCount = 0;
*eHandle = event;
debugEvent(event, "PxnProxyOpStart");
return ncclSuccess;
}
if (eventBase->type == ncclProfileColl) {
struct collective* parent = (struct collective *)eDescr->parentObj;
int channelId = eDescr->proxyOp.channelId;
struct proxyOp* event = &parent->op[channelId][parent->nProxyOps[channelId]++];
event->type = ncclProfileProxyOp;
event->channelId = channelId;
event->pid = eDescr->proxyOp.pid;
event->rank = eDescr->rank;
event->peer = eDescr->proxyOp.peer;
event->nSteps = eDescr->proxyOp.nSteps;
event->chunkSize = eDescr->proxyOp.chunkSize;
event->isSend = eDescr->proxyOp.isSend;
event->parent = eventBase;
event->startTs = gettime() - startTime;
event->stepCount = 0;
*eHandle = event;
__atomic_fetch_add(&parent->base.refCount, 1, __ATOMIC_RELAXED);
debugEvent(event, "ProxyOpStart");
} else { // ncclProfileP2p
struct p2p* parent = (struct p2p *)eDescr->parentObj;
int channelId = eDescr->proxyOp.channelId;
struct proxyOp* event = &parent->op[channelId];
event->type = ncclProfileProxyOp;
event->channelId = channelId;
event->pid = eDescr->proxyOp.pid;
event->rank = eDescr->rank;
event->peer = eDescr->proxyOp.peer;
event->nSteps = eDescr->proxyOp.nSteps;
event->chunkSize = eDescr->proxyOp.chunkSize;
event->isSend = eDescr->proxyOp.isSend;
event->parent = eventBase;
event->startTs = gettime() - startTime;
event->stepCount = 0;
*eHandle = event;
__atomic_fetch_add(&parent->base.refCount, 1, __ATOMIC_RELAXED);
debugEvent(event, "ProxyOpStart");
}
} else if (eDescr->type == ncclProfileProxyStep) {
// the parent might be null if we run out of events
struct proxyOp* parent = (struct proxyOp *)eDescr->parentObj;
if (parent == NULL) return ncclSuccess;
int s = parent->stepCount++ % MAX_STEPS;
struct proxyStep* event = &parent->step[s];
event->type = ncclProfileProxyStep;
event->state = 0;
event->step = eDescr->proxyStep.step;
event->parent = parent;
event->isSend = parent->isSend;
event->startTs = gettime() - startTime;
event->nNetEvents = 0;
*eHandle = event;
debugEvent(event, "ProxyStepStart");
} else if (eDescr->type == ncclProfileKernelCh) {
struct taskEventBase* eventBase = (struct taskEventBase *)eDescr->parentObj;
if (eventBase == NULL) return ncclSuccess;
if (eventBase->type == ncclProfileColl) {
struct collective* parent = (struct collective *)eDescr->parentObj;
struct kernelCh* event = &parent->kernel[eDescr->kernelCh.channelId];
event->type = ncclProfileKernelCh;
event->channelId = eDescr->kernelCh.channelId;
event->startGpuClk = eDescr->kernelCh.pTimer;
event->parent = eventBase;
event->startTs = gettime() - startTime;
*eHandle = event;
__atomic_fetch_add(&parent->base.refCount, 1, __ATOMIC_RELAXED);
debugEvent(event, "KernelChStart");
} else { // ncclProfileP2p
struct p2p* parent = (struct p2p *)eDescr->parentObj;
struct kernelCh* event = &parent->kernel[eDescr->kernelCh.channelId];
event->type = ncclProfileKernelCh;
event->channelId = eDescr->kernelCh.channelId;
event->startGpuClk = eDescr->kernelCh.pTimer;
event->parent = eventBase;
event->startTs = gettime() - startTime;
*eHandle = event;
__atomic_fetch_add(&parent->base.refCount, 1, __ATOMIC_RELAXED);
debugEvent(event, "KernelChStart");
}
} else if (eDescr->type == ncclProfileNetPlugin) {
struct proxyStep* parent = (struct proxyStep *)eDescr->parentObj;
if (parent == NULL) return ncclSuccess;
int64_t pluginId = eDescr->netPlugin.id;
int64_t type = pluginId & NCCL_PROFILER_NET_TYPE_MASK;
int64_t ver = pluginId & NCCL_PROFILER_NET_VER_MASK;
if (type == NCCL_PROFILER_NET_TYPE_IB) {
if (ver == 1) {
ncclProfilerNetIbDescr_v1_t* descr = (ncclProfilerNetIbDescr_v1_t *)eDescr->netPlugin.data;
struct netPlugin* event = parent->net + __atomic_fetch_add(&parent->nNetEvents, 1, __ATOMIC_RELAXED);
event->type = ncclProfileNetPlugin;
event->pluginType = type;
event->pluginVer = ver;
if (descr->type == ncclProfileQp) {
event->pluginEvent = ncclProfileQp;
event->qp.device = descr->qp.device;
event->qp.wr_id = descr->qp.wr_id;
event->qp.opcode = descr->qp.opcode;
event->qp.qpNum = descr->qp.qpNum;
event->qp.length = descr->qp.length;
}
event->startTs = gettime() - startTime;
*eHandle = event;
debugEvent(event, "NetPluginStart");
}
} else if (type == NCCL_PROFILER_NET_TYPE_SOCK) {
if (ver == 1) {
ncclProfilerNetSockDescr_v1_t* descr = (ncclProfilerNetSockDescr_v1_t *)eDescr->netPlugin.data;
struct netPlugin* event = parent->net + __atomic_fetch_add(&parent->nNetEvents, 1, __ATOMIC_RELAXED);
event->type = ncclProfileNetPlugin;
event->pluginType = type;
event->pluginVer = ver;
if (descr->type == ncclProfileSocket) {
event->pluginEvent = ncclProfileSocket;
event->sock.fd = descr->sock.fd;
event->sock.op = descr->sock.op;
event->sock.length = descr->sock.length;
}
event->startTs = gettime() - startTime;
*eHandle = event;
debugEvent(event, "NetPluginStart");
}
}
}
return ncclSuccess;
}
void updateEvent(void* handle) {
uint8_t type = *(uint8_t *)handle;
if (type == ncclProfileGroup) {
struct group* event = (struct group *)handle;
if (__atomic_sub_fetch(&event->refCount, 1, __ATOMIC_RELAXED) == 0) {
event->stopTs = gettime() - startTime;
// return group event to the pool
__atomic_fetch_add(&event->ctx->groupPoolBase, 1, __ATOMIC_RELAXED);
}
debugEvent(event, "GroupStop");
} else if (type == ncclProfileColl) {
struct collective* event = (struct collective *)handle;
if (__atomic_sub_fetch(&event->base.refCount, 1, __ATOMIC_RELAXED) == 0) {
event->base.stopTs = gettime() - startTime;
debugEvent(event, "CollStop");
updateEvent(event->base.parent);
return;
}
debugEvent(event, "CollStop");
} else if (type == ncclProfileP2p) {
struct p2p* event = (struct p2p *)handle;
if (__atomic_sub_fetch(&event->base.refCount, 1, __ATOMIC_RELAXED) == 0) {
event->base.stopTs = gettime() - startTime;
debugEvent(event, "P2pStop");
updateEvent(event->base.parent);
return;
}
debugEvent(event, "P2pStop");
} else if (type == ncclProfileProxyOp) {
struct proxyOp* event = (struct proxyOp *)handle;
event->stopTs = gettime() - startTime;
if (event->pid != pid) {
// only for proxyOps that don't have a parent collective/p2p (i.e., PXN)
int done = __atomic_add_fetch(&detachPoolDone, 1, __ATOMIC_RELAXED);
if (done == detachPoolSize) {
// reset the event completed (done) counter
__atomic_store_n(&detachPoolDone, 0, __ATOMIC_RELAXED);
// update the base pointer to the top of the pool
int index = __atomic_load_n(&detachPoolIndex, __ATOMIC_RELAXED);
__atomic_store_n(&detachPoolBase, index, __ATOMIC_RELAXED);
}
debugEvent(event, "ProxyOpStop");
return;
}
updateEvent(event->parent);
debugEvent(event, "ProxyOpStop");
} else if (type == ncclProfileProxyStep) {
struct proxyStep* event = (struct proxyStep *)handle;
event->stopTs = gettime() - startTime;
debugEvent(event, "ProxyStepStop");
} else if (type == ncclProfileProxyCtrl) {
struct proxyCtrl* event = (struct proxyCtrl *)handle;
event->stopTs = gettime() - startTime;
debugEvent(event, "ProxyCtrlStop");
} else if (type == ncclProfileKernelCh) {
struct kernelCh* event = (struct kernelCh *)handle;
event->stopTs = gettime() - startTime;
updateEvent(event->parent);
debugEvent(event, "KernelChStop");
} else if (type == ncclProfileNetPlugin) {
struct netPlugin* event = (struct netPlugin *)handle;
event->stopTs = gettime() - startTime;
debugEvent(event, "NetPluginStop");
}
}
__hidden ncclResult_t exampleProfilerStopEvent(void* eHandle) {
// the event handle might be null if we run out of events
if (eHandle == NULL) return ncclSuccess;
uint8_t type = *(uint8_t *)eHandle;
if (type == ncclProfileGroup) {
// stopping the group event in NCCL core does not
// mean the group has completed. It means the group
// was submitted/enqueued so we need to keep the event open
struct group* event = (struct group *)eHandle;
event->stopTs = gettime() - startTime;
return ncclSuccess;
} else if (type == ncclProfileColl) {
// stopping the collective event in NCCL core does not
// mean the collective has completed. It means the collective
// was submitted/enqueued so we need to keep the event open
struct collective* event = (struct collective *)eHandle;
event->base.stopTs = gettime() - startTime;
return ncclSuccess;
} else if (type == ncclProfileP2p) {
// stopping the p2p event in NCCL core does not
// mean the p2p has completed. It means the p2p
// was submitted/enqueued so we need to keep the event open
struct p2p* event = (struct p2p *)eHandle;
event->base.stopTs = gettime() - startTime;
return ncclSuccess;
}
updateEvent(eHandle);
return ncclSuccess;
}
__hidden ncclResult_t exampleProfilerRecordEventState(void* eHandle, ncclProfilerEventState_t eState, ncclProfilerEventStateArgs_t* eStateArgs) {
// the event handle might be null if we run out of events
if (eHandle == NULL) return ncclSuccess;
uint8_t type = *(uint8_t *)eHandle;
if (type == ncclProfileProxyOp) {
struct proxyOp* event = (struct proxyOp *)eHandle;
if (eState == ncclProfilerProxyOpInProgress_v4) {
event->progrTs = gettime() - startTime;
}
} else if (type == ncclProfileProxyStep) {
struct proxyStep* event = (struct proxyStep *)eHandle;
struct proxyOp* parent = event->parent;
switch (eState) {
case ncclProfilerProxyStepSendGPUWait:
event->timestamp[PROXY_STEP_SEND_GPU_WAIT] = gettime() - startTime;
break;
case ncclProfilerProxyStepSendPeerWait_v4:
// do not update step event if in SendPeerWait
if (event->state == ncclProfilerProxyStepSendPeerWait_v4) break;
event->timestamp[PROXY_STEP_SEND_PEER_WAIT] = gettime() - startTime;
event->state = ncclProfilerProxyStepSendPeerWait_v4;
break;
case ncclProfilerProxyStepSendWait:
event->timestamp[PROXY_STEP_SEND_WAIT] = gettime() - startTime;
parent->transSize += eStateArgs->proxyStep.transSize;
break;
case ncclProfilerProxyStepRecvWait:
event->timestamp[PROXY_STEP_RECV_WAIT] = gettime() - startTime;
break;
case ncclProfilerProxyStepRecvFlushWait:
event->timestamp[PROXY_STEP_RECV_FLUSH_WAIT] = gettime() - startTime;
parent->transSize += eStateArgs->proxyStep.transSize;
break;
case ncclProfilerProxyStepRecvGPUWait:
event->timestamp[PROXY_STEP_RECV_GPU_WAIT] = gettime() - startTime;
break;
}
} else if (type == ncclProfileProxyCtrl) {
struct proxyCtrl* event = (struct proxyCtrl *)eHandle;
if (eState == ncclProfilerProxyCtrlAppendEnd) {
event->appended = eStateArgs->proxyCtrl.appendedProxyOps;
}
event->state = eState;
} else if (type == ncclProfileKernelCh) {
struct kernelCh* event = (struct kernelCh *)eHandle;
if (eState == ncclProfilerKernelChStop) {
event->stopGpuClk = eStateArgs->kernelCh.pTimer;
}
}
debugEvent(eHandle, "RecordEventState");
return ncclSuccess;
}
ncclProfiler_t ncclProfiler_v4 = {
"Example-profiler",
exampleProfilerInit,
exampleProfilerStartEvent,
exampleProfilerStopEvent,
exampleProfilerRecordEventState,
exampleProfilerFinalize,
};
int exampleProfilerStart(int eActivationMask) {
if (__atomic_load_n(&initialized, __ATOMIC_RELAXED)) {
__atomic_store_n(eActivationMaskPtr, eActivationMask, __ATOMIC_RELAXED);
}
return ncclSuccess;
}
int exampleProfilerStop(void) {
if (__atomic_load_n(&initialized, __ATOMIC_RELAXED)) {
__atomic_store_n(eActivationMaskPtr, 0, __ATOMIC_RELAXED);
}
return ncclSuccess;
}

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@ -1,13 +0,0 @@
/*************************************************************************
* Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#ifndef PLUGIN_H_
#define PLUGIN_H_
int exampleProfilerStart(int eActivationMask);
int exampleProfilerStop(void);
#endif

View File

@ -1,294 +0,0 @@
/*************************************************************************
* Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#include <stdio.h>
#include "profiler.h"
#include "event.h"
#include "print_event.h"
#define __hidden __attribute__ ((visibility("hidden")))
// FIXME: chrome tracing asynchronous events (following used) allow event nesting for events that have same id and category
// It appears that nesting more than three events causes issues. Therefore, every event is given an increasing id and a
// category that matches the type of event (GROUP, COLL, P2P, PROXY, NET)
static __thread int groupId;
__hidden void printGroupEventHeader(FILE* fh, struct group* event) {
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"GROUP\", \"ph\": \"b\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f, \"args\": {\"groupId\": %d}},\n",
"Group", groupId, getpid(), 1, event->startTs, event->groupId);
}
__hidden void printGroupEventTrailer(FILE* fh, struct group* event) {
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"GROUP\", \"ph\": \"e\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f},\n",
"Group", groupId++, getpid(), 1, event->stopTs);
}
static __thread int collId;
__hidden void printCollEventHeader(FILE* fh, struct collective* event) {
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"COLL\", \"ph\": \"b\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f, \"args\": {\"SeqNum\": %lu, \"CommHash\": %lu, \"Rank\": %d, \"Count\": %lu, \"Datatype\": \"%s\", \"Algorithm\": \"%s\", \"Protocol\": \"%s\", \"nChannels\": %d}},\n",
event->base.func, collId, getpid(), 1, event->base.startTs, event->seqNumber, event->base.parent->ctx->commHash, event->base.rank, event->count, event->datatype, event->algo, event->proto, event->nChannels);
}
__hidden void printCollEventTrailer(FILE* fh, struct collective* event) {
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"COLL\", \"ph\": \"e\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f},\n",
event->base.func, collId++, getpid(), 1, event->base.stopTs);
}
static __thread int p2pId;
__hidden void printP2pEventHeader(FILE* fh, struct p2p* event) {
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"P2P\", \"ph\": \"b\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f, \"args\": {\"CommHash\": %lu, \"Rank\": %d, \"Peer\": %d, \"Count\": %lu, \"Datatype\": \"%s\", \"nChannels\": %d}},\n",
event->base.func, p2pId, getpid(), 1, event->base.startTs, event->base.parent->ctx->commHash, event->base.rank, event->peer, event->count, event->datatype, event->nChannels);
}
__hidden void printP2pEventTrailer(FILE* fh, struct p2p* event) {
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"P2P\", \"ph\": \"e\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f},\n",
event->base.func, p2pId++, getpid(), 1, event->base.stopTs);
}
static __thread int proxyOpId;
__hidden void printProxyOpEventHeader(FILE* fh, struct proxyOp* event) {
if (event->isSend) {
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"PROXY\", \"ph\": \"b\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f, \"args\": {\"Channel\": %d, \"Peer\": %d, \"Steps\": %d, \"ChunkSize\": %d, \"transSize\": %lu}},\n",
"ScheduleSend", proxyOpId, getpid(), 1, event->startTs, event->channelId, event->peer, event->nSteps, event->chunkSize, event->transSize);
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"PROXY\", \"ph\": \"e\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f},\n",
"ScheduleSend", proxyOpId, getpid(), 1, event->progrTs);
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"PROXY\", \"ph\": \"b\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f, \"args\": {\"Channel\": %d, \"Peer\": %d, \"Steps\": %d, \"ChunkSize\": %d, \"transSize\": %lu}},\n",
"ProgressSend", proxyOpId, getpid(), 1, event->progrTs, event->channelId, event->peer, event->nSteps, event->chunkSize, event->transSize);
} else {
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"PROXY\", \"ph\": \"b\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f, \"args\": {\"Channel\": %d, \"Peer\": %d, \"Steps\": %d, \"ChunkSize\": %d, \"transSize\": %lu}},\n",
"ScheduleRecv", proxyOpId, getpid(), 1, event->startTs, event->channelId, event->peer, event->nSteps, event->chunkSize, event->transSize);
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"PROXY\", \"ph\": \"e\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f},\n",
"ScheduleRecv", proxyOpId, getpid(), 1, event->progrTs);
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"PROXY\", \"ph\": \"b\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f, \"args\": {\"Channel\": %d, \"Peer\": %d, \"Steps\": %d, \"ChunkSize\": %d, \"transSize\": %lu}},\n",
"ProgressRecv", proxyOpId, getpid(), 1, event->progrTs, event->channelId, event->peer, event->nSteps, event->chunkSize, event->transSize);
}
}
__hidden void printProxyOpEventTrailer(FILE* fh, struct proxyOp* event) {
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"PROXY\", \"ph\": \"e\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f},\n",
event->isSend ? "ProgressSend" : "ProgressRecv", proxyOpId++, getpid(), 1, event->stopTs);
}
static __thread int proxyStepId;
__hidden void printProxyStepEventHeader(FILE* fh, struct proxyStep* event) {
if (event->isSend) {
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"NET\", \"ph\": \"b\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f, \"args\": {\"Step\": %d}},\n",
"SendGpuWait", proxyStepId, getpid(), 1, event->timestamp[PROXY_STEP_SEND_GPU_WAIT], event->step);
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"NET\", \"ph\": \"e\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f},\n",
"SendGpuWait", proxyStepId, getpid(), 1, event->timestamp[PROXY_STEP_SEND_PEER_WAIT]);
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"NET\", \"ph\": \"b\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f, \"args\": {\"Step\": %d}},\n",
"SendPeerWait", proxyStepId, getpid(), 1, event->timestamp[PROXY_STEP_SEND_PEER_WAIT], event->step);
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"NET\", \"ph\": \"e\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f},\n",
"SendPeerWait", proxyStepId, getpid(), 1, event->timestamp[PROXY_STEP_SEND_WAIT]);
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"NET\", \"ph\": \"b\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f, \"args\": {\"Step\": %d}},\n",
"SendWait", proxyStepId, getpid(), 1, event->timestamp[PROXY_STEP_SEND_WAIT], event->step);
} else {
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"NET\", \"ph\": \"b\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f, \"args\": {\"Step\": %d}},\n",
"RecvWait", proxyStepId, getpid(), 1, event->timestamp[PROXY_STEP_RECV_WAIT], event->step);
}
}
__hidden void printProxyStepEventTrailer(FILE* fh, struct proxyStep* event) {
if (event->isSend) {
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"NET\", \"ph\": \"e\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f},\n",
"SendWait", proxyStepId++, getpid(), 1, event->stopTs);
} else {
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"NET\", \"ph\": \"e\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f},\n",
"RecvWait", proxyStepId, getpid(), 1, event->timestamp[PROXY_STEP_RECV_FLUSH_WAIT]);
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"NET\", \"ph\": \"b\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f, \"args\": {\"Step\": %d}},\n",
"RecvFlushWait", proxyStepId, getpid(), 1, event->timestamp[PROXY_STEP_RECV_FLUSH_WAIT], event->step);
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"NET\", \"ph\": \"e\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f},\n",
"RecvFlushWait", proxyStepId, getpid(), 1, event->timestamp[PROXY_STEP_RECV_GPU_WAIT]);
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"NET\", \"ph\": \"b\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f, \"args\": {\"Step\": %d}},\n",
"RecvGpuWait", proxyStepId, getpid(), 1, event->timestamp[PROXY_STEP_RECV_GPU_WAIT], event->step);
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"NET\", \"ph\": \"e\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f},\n",
"RecvGpuWait", proxyStepId++, getpid(), 1, event->stopTs);
}
}
static __thread int kernelId;
__hidden void printKernelChEventHeader(FILE* fh, struct kernelCh* event) {
if (event->type != ncclProfileKernelCh) return;
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"GPU\", \"ph\": \"b\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f, \"args\": {\"Channel\": %d, \"StartGpuClk\": %lu, \"StopGpuClk\": %lu}},\n",
"KernelCh", kernelId, getpid(), 1, event->startTs, event->channelId, event->startGpuClk, event->stopGpuClk);
}
__hidden void printKernelChEventTrailer(FILE* fh, struct kernelCh* event) {
if (event->type != ncclProfileKernelCh) return;
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"GPU\", \"ph\": \"e\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f},\n",
"KernelCh", kernelId, getpid(), 1, event->stopTs);
}
static __thread int proxyCtrlId;
__hidden void printProxyCtrlEvent(FILE* fh, struct proxyCtrl* event) {
const char* str;
if (event->state == ncclProfilerProxyCtrlIdle || event->state == ncclProfilerProxyCtrlActive) {
str = "Idle";
} else if (event->state == ncclProfilerProxyCtrlSleep || event->state == ncclProfilerProxyCtrlWakeup) {
str = "Sleep";
} else if (event->state == ncclProfilerProxyCtrlAppend || event->state == ncclProfilerProxyCtrlAppendEnd) {
str = "Append";
} else {
return;
}
if (event->state == ncclProfilerProxyCtrlAppendEnd) {
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"PROXY\", \"ph\": \"b\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f, \"args\": {\"appended\": %d}},\n",
str, proxyCtrlId, getpid(), 1, event->startTs, event->appended);
} else {
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"PROXY\", \"ph\": \"b\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f},\n",
str, proxyCtrlId, getpid(), 1, event->startTs);
}
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"PROXY\", \"ph\": \"e\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f},\n",
str, proxyCtrlId++, getpid(), 1, event->stopTs);
}
static __thread int ibQpId, sockId;
__hidden void printNetPluginEvent(FILE* fh, struct netPlugin* event) {
if (event->pluginType == NCCL_PROFILER_NET_TYPE_IB) {
if (event->pluginVer == 1) {
if (event->pluginEvent == ncclProfileQp) {
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"NET_IB\", \"ph\": \"b\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f, \"args\": {\"device\": %d, \"qp_num\": %d, \"opcode\": %d, \"wr_id\": %lu, \"size\": %lu}},\n",
"Qp", ibQpId, getpid(), 1, event->startTs, event->qp.device, event->qp.qpNum, event->qp.opcode, event->qp.wr_id, event->qp.length);
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"NET_IB\", \"ph\": \"e\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f},\n",
"Qp", ibQpId++, getpid(), 1, event->stopTs);
}
}
} else if (event->pluginType == NCCL_PROFILER_NET_TYPE_SOCK) {
if (event->pluginVer == 1) {
if (event->pluginEvent == ncclProfileSocket) {
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"NET_SOCK\", \"ph\": \"b\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f, \"args\": {\"sock\": %d, \"op\": %d, \"size\": %lu}},\n",
"Sock", sockId, getpid(), 1, event->startTs, event->sock.fd, event->sock.op, event->sock.length);
fprintf(fh, "{\"name\": \"%s\", \"cat\": \"NET_SOCK\", \"ph\": \"e\", \"id\": %d, \"pid\": %d, \"tid\": %d, \"ts\": %f},\n",
"Sock", sockId++, getpid(), 1, event->stopTs);
}
}
}
}
//#define DEBUG_EVENTS
void debugEvent(void* eHandle, const char* tag) {
#ifdef DEBUG_EVENTS
char filename[64] = { 0 };
sprintf(filename, "EventDebug-%d", getpid());
FILE* fh = fopen(filename, "a+");
uint8_t type = *(uint8_t *)eHandle;
if (type == ncclProfileGroup) {
struct group* event = (struct group *)eHandle;
fprintf(fh, "Group event %p tag = %s {\n", event, tag);
fprintf(fh, " refCount = %d\n", __atomic_load_n(&event->refCount, __ATOMIC_RELAXED));
fprintf(fh, " startTs = %f\n", event->startTs);
fprintf(fh, " stopTs = %f\n", event->stopTs);
fprintf(fh, "}\n");
} else if (type == ncclProfileColl) {
struct collective* event = (struct collective *)eHandle;
fprintf(fh, "Collective event %p tag = %s {\n", event, tag);
fprintf(fh, " refCount = %d\n", __atomic_load_n(&event->base.refCount, __ATOMIC_RELAXED));
fprintf(fh, " parent = %p\n", event->base.parent);
for (int j = 0; j < 2*MAX_OPS; j++) {
for (int i = 0; i < MAX_CHANNELS; i++) if (event->op[i][j].type == ncclProfileProxyOp) fprintf(fh, " op[%d] = %p\n", i, &event->op[i]);
}
fprintf(fh, " startTs = %f\n", event->base.startTs);
fprintf(fh, " stopTs = %f\n", event->base.stopTs);
fprintf(fh, "}\n");
} else if (type == ncclProfileP2p) {
struct p2p* event = (struct p2p *)eHandle;
fprintf(fh, "P2p event %p tag = %s {\n", event, tag);
fprintf(fh, " refCount = %d\n", __atomic_load_n(&event->base.refCount, __ATOMIC_RELAXED));
fprintf(fh, " parent = %p\n", event->base.parent);
fprintf(fh, " op = %p\n", &event->op);
fprintf(fh, " startTs = %f\n", event->base.startTs);
fprintf(fh, " stopTs = %f\n", event->base.stopTs);
fprintf(fh, "}\n");
} else if (type == ncclProfileProxyOp) {
struct proxyOp* event = (struct proxyOp *)eHandle;
fprintf(fh, "ProxyOp event %p tag = %s {\n", event, tag);
fprintf(fh, " type = %s\n", event->isSend < 0 ? "Unknown" : event->isSend ? "Send" : "Recv");
fprintf(fh, " channel = %d\n", event->channelId);
fprintf(fh, " parent = %p\n", event->parent);
fprintf(fh, " rank = %d\n", event->rank);
fprintf(fh, " startTs = %f\n", event->startTs);
fprintf(fh, " progrTs = %f\n", event->progrTs);
fprintf(fh, " stopTs = %f\n", event->stopTs);
fprintf(fh, "}\n");
} else if (type == ncclProfileProxyStep) {
struct proxyStep* event = (struct proxyStep *)eHandle;
fprintf(fh, "ProxyStep event %p tag = %s {\n", event, tag);
fprintf(fh, " type = %s\n", event->isSend < 0 ? "Unknown" : event->isSend ? "Send" : "Recv");
fprintf(fh, " parent = %p\n", event->parent);
fprintf(fh, " startTs = %f\n", event->startTs);
fprintf(fh, " stopTs = %f\n", event->stopTs);
fprintf(fh, "}\n");
} else if (type == ncclProfileKernelCh) {
struct kernelCh* event = (struct kernelCh *)eHandle;
fprintf(fh, "KernelCh event %p tag = %s {\n", event, tag);
fprintf(fh, " parent = %p\n", event->parent);
fprintf(fh, " channel = %d\n", event->channelId);
} else if (type == ncclProfileNetPlugin) {
struct netPlugin* event = (struct netPlugin *)eHandle;
fprintf(fh, "NetPlugin event %p tag = %s {\n", event, tag);
fprintf(fh, " pluginType = %d\n", event->pluginType);
fprintf(fh, " pluginVer = %d\n", event->pluginVer);
fprintf(fh, " pluginEvent = %d\n", event->pluginEvent);
fprintf(fh, " startTs = %f\n", event->startTs);
fprintf(fh, " stopTs = %f\n", event->stopTs);
fprintf(fh, "}\n");
}
fclose(fh);
#endif
}
void printEvent(FILE* fh, void* handle) {
if (handle == NULL || fh == NULL) return;
uint8_t type = *(uint8_t *)handle;
if (type == ncclProfileGroup) {
struct group* g = (struct group *)handle;
printGroupEventHeader(fh, g);
struct taskEventBase* base = taskEventQueueHead(g);
while (base) {
struct taskEventBase* next = base->next;
printEvent(fh, base);
base = next;
}
printGroupEventTrailer(fh, g);
} else if (type == ncclProfileColl) {
struct collective* c = (struct collective *)handle;
printCollEventHeader(fh, c);
for (int i = 0; i < MAX_CHANNELS; i++) {
printKernelChEventHeader(fh, &c->kernel[i]);
for (int j = 0; j < c->nProxyOps[i]; j++) {
printEvent(fh, &c->op[i][j]);
}
printKernelChEventTrailer(fh, &c->kernel[i]);
}
printCollEventTrailer(fh, c);
} else if (type == ncclProfileP2p) {
struct p2p* p = (struct p2p *)handle;
printP2pEventHeader(fh, p);
for (int i = 0; i < MAX_CHANNELS; i++) {
printKernelChEventHeader(fh, &p->kernel[i]);
printEvent(fh, &p->op[i]);
printKernelChEventTrailer(fh, &p->kernel[i]);
}
printP2pEventTrailer(fh, p);
} else if (type == ncclProfileProxyOp) {
struct proxyOp* p = (struct proxyOp *)handle;
printProxyOpEventHeader(fh, p);
for (int i = 0; i < MAX_STEPS; i++) {
printEvent(fh, &p->step[i]);
}
printProxyOpEventTrailer(fh, p);
} else if (type == ncclProfileProxyStep) {
struct proxyStep* p = (struct proxyStep *)handle;
printProxyStepEventHeader(fh, p);
for (int q = 0; q < p->nNetEvents; q++) {
printNetPluginEvent(fh, &p->net[q]);
}
printProxyStepEventTrailer(fh, p);
} else if (type == ncclProfileProxyCtrl) {
struct proxyCtrl* p = (struct proxyCtrl *)handle;
printProxyCtrlEvent(fh, p);
}
return;
}

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/*************************************************************************
* Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#ifndef PRINT_EVENT_H_
#define PRINT_EVENT_H_
#include "nccl/common.h"
extern ncclDebugLogger_t logFn;
void debugEvent(void* eHandle, const char* tag);
void printEvent(FILE* fh, void* handle);
#endif

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@ -1,23 +0,0 @@
#
# Copyright (c) 2015-2019, NVIDIA CORPORATION. All rights reserved.
#
# See LICENSE.txt for license information
#
.DEFAULT_GOAL: build
include ../../makefiles/common.mk
SRCDIR ?= $(abspath ../..)
BUILDDIR ?= .
NCCLDIR := $(BUILDDIR)
SRC_FILES := $(wildcard *.c)
DST_DIR := $(BUILDDIR)/test/unit/plugins
build: ${BUILDDIR}/libnccl-tuner-basic.so
${BUILDDIR}/libnccl-tuner-basic.so: ${SRC_FILES}
@printf "Compiling %-35s > %s\n" $< $@
@mkdir -p ${BUILDDIR}
$(CC) -Inccl -fPIC -shared -o $@ $^
clean:
rm -f ${BUILDDIR}/libnccl-tuner-basic.so

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@ -1,15 +0,0 @@
/*************************************************************************
* Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#ifndef COMMON_H_
#define COMMON_H_
typedef enum {NCCL_LOG_NONE=0, NCCL_LOG_VERSION=1, NCCL_LOG_WARN=2, NCCL_LOG_INFO=3, NCCL_LOG_ABORT=4, NCCL_LOG_TRACE=5} ncclDebugLogLevel;
typedef enum {NCCL_INIT=1, NCCL_COLL=2, NCCL_P2P=4, NCCL_SHM=8, NCCL_NET=16, NCCL_GRAPH=32, NCCL_TUNING=64, NCCL_ENV=128, NCCL_ALLOC=256, NCCL_CALL=512, NCCL_PROXY=1024, NCCL_NVLS=2048, NCCL_BOOTSTRAP=4096, NCCL_REG=8192, NCCL_ALL=~0} ncclDebugLogSubSys;
typedef void (*ncclDebugLogger_t)(ncclDebugLogLevel level, unsigned long flags, const char *file, int line, const char *fmt, ...);
#endif

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@ -1,17 +0,0 @@
/*
* Copyright (c) 2017-2022, NVIDIA CORPORATION. All rights reserved.
*/
#ifndef NCCL_ERR_H_
#define NCCL_ERR_H_
/* Error type for plugins */
typedef enum { ncclSuccess = 0,
ncclUnhandledCudaError = 1,
ncclSystemError = 2,
ncclInternalError = 3,
ncclInvalidArgument = 4,
ncclInvalidUsage = 5,
ncclRemoteError = 6 } ncclResult_t;
#endif

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@ -1,97 +0,0 @@
/*************************************************************************
* Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
* Copyright (c) 2023, Meta Platforms, Inc. and affiliates.
*
* See LICENSE.txt for license information
************************************************************************/
#ifndef NCCL_TUNER_H_
#define NCCL_TUNER_H_
#include <stdint.h>
#include <stdlib.h>
#include "common.h"
#include "err.h"
#define NCCL_NUM_FUNCTIONS 5 // Send/Recv not included for now
typedef enum {
ncclFuncBroadcast = 0,
ncclFuncReduce = 1,
ncclFuncAllGather = 2,
ncclFuncReduceScatter = 3,
ncclFuncAllReduce = 4,
ncclFuncSendRecv = 5,
ncclFuncSend = 6,
ncclFuncRecv = 7,
ncclNumFuncs = 8
} ncclFunc_t;
#define NCCL_NUM_ALGORITHMS 7 // Tree/Ring/CollNet*
#define NCCL_ALGO_UNDEF -1
#define NCCL_ALGO_TREE 0
#define NCCL_ALGO_RING 1
#define NCCL_ALGO_COLLNET_DIRECT 2
#define NCCL_ALGO_COLLNET_CHAIN 3
#define NCCL_ALGO_NVLS 4
#define NCCL_ALGO_NVLS_TREE 5
#define NCCL_ALGO_PAT 6
#define NCCL_NUM_PROTOCOLS 3 // Simple/LL/LL128
#define NCCL_PROTO_UNDEF -1
#define NCCL_PROTO_LL 0
#define NCCL_PROTO_LL128 1
#define NCCL_PROTO_SIMPLE 2
#define NCCL_ALGO_PROTO_IGNORE -1.0
// API to be implemented by external tuner
typedef struct {
// Name of the tuner
const char* name;
// Initializes tuner states.
// Inputs:
// - nRanks: number of ranks in current communicator. Each communicator initialize its own tuner.
// - nNodes: number of nodes in current communicator.
// - logFunction: a logFunction can be useful to integrate logging together with NCCL core.
// Outputs:
// - context: tuner context object
ncclResult_t (*init)(size_t nRanks, size_t nNodes, ncclDebugLogger_t logFunction, void **context);
// Gets info (algo, protocol, number of ctas and threads) for a given collective.
// Inputs:
// - context: tuner context object
// - collType: collective type , e.g., allreduce, allgather…
// - nBytes: collective size in bytes
// - numPipeOps: number of operations in the group
// - numAlgo: number of algorithms in collCostTable
// - numProto: number of protocols in collCostTable
// - regBuff: can register user buffer
//
// Outputs:
// - nChannels: number of channels (hence SMs) to be used.
//
// InOut:
// - collCostTable: collective cost table, generated by NCCL core, containing algo|proto|time entries for collType.
// NCCL core sets ignored algo/proto cost table entries to -1.0 (NCCL_ALGO_PROTO_IGNORE).
//
// If getCollInfo() does not return ncclSuccess, NCCL will fall back to the
// default tuning for the given collective.
// Also, the plugin is allowed to not set any output, or set only the
// algorithm and protocol, but not only the algorithm or only the protocol.
// Unset fields will be set automatically by NCCL.
ncclResult_t (*getCollInfo)(void* context, ncclFunc_t collType, size_t nBytes,
int numPipeOps, float** collCostTable, int numAlgo, int numProto,
int regBuff, int* nChannels);
// Terminates the plugin and cleans up any resources that the plugin allocated.
// context: tuner context object
ncclResult_t (*destroy)(void* context);
} ncclTuner_v4_t;
typedef ncclTuner_v4_t ncclTuner_t;
#define NCCL_TUNER_PLUGIN_SYMBOL "ncclTunerPlugin_v4"
#endif

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@ -1,34 +0,0 @@
/*************************************************************************
* Copyright (c) 2015-2019, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#include "tuner.h"
#define __hidden __attribute__ ((visibility("hidden")))
__hidden ncclResult_t pluginInit(size_t nRanks, size_t nNodes, ncclDebugLogger_t logFunction, void **context) { return ncclSuccess; }
__hidden ncclResult_t pluginGetCollInfo(void* context, ncclFunc_t collType, size_t nBytes,
int numPipeOps, float** collCostTable, int numAlgo, int numProto,
int regBuff, int* nChannels) {
// Update NCCL core generated cost table. Updated table will be evaluated by NCCL to pick the best algo/proto combo
float (*table)[NCCL_NUM_PROTOCOLS] = (float (*)[NCCL_NUM_PROTOCOLS])collCostTable;
if (table[NCCL_ALGO_RING][NCCL_PROTO_SIMPLE] != NCCL_ALGO_PROTO_IGNORE) {
table[NCCL_ALGO_RING][NCCL_PROTO_SIMPLE] = 0.0;
}
*nChannels = 1;
return ncclSuccess;
}
__hidden ncclResult_t pluginDestroy(void* context) { return ncclSuccess; }
#define PLUGIN_NAME "Basic"
const ncclTuner_v4_t ncclTunerPlugin_v4 = {
.name = PLUGIN_NAME,
.init = pluginInit,
.getCollInfo = pluginGetCollInfo,
.destroy = pluginDestroy
};

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#
# Copyright (c) 2015-2019, NVIDIA CORPORATION. All rights reserved.
#
# See LICENSE.txt for license information
#
.DEFAULT_GOAL: build
PLUGIN_SO:=libnccl-tuner-example.so
include ../../makefiles/common.mk
SRCDIR ?= $(abspath ../..)
BUILDDIR ?= .
NCCLDIR := $(BUILDDIR)
SRC_FILES := $(wildcard *.c)
DST_DIR := $(BUILDDIR)/test/unit/plugins
default: ${BUILDDIR}/$(PLUGIN_SO)
build: ${BUILDDIR}/$(PLUGIN_SO)
${BUILDDIR}/$(PLUGIN_SO): plugin.c
@printf "Compiling %-35s > %s\n" $< $@
@mkdir -p ${BUILDDIR}
$(CC) -Inccl $(INC) -fPIC -shared -o $@ -Wl,-soname,$(PLUGIN_SO) $^
# Test targets - delegate to test directory
test:
$(MAKE) -C test test TEST_CASE=$(TEST_CASE)
test-verbose:
$(MAKE) -C test test-verbose TEST_CASE=$(TEST_CASE)
# Build tests
test-build:
$(MAKE) -C test all
# Optimize configurations from performance data
optimize-config:
@if [ -z "$(CSV_FILE)" ]; then \
echo "Usage: make optimize-config CSV_FILE=path/to/data.csv [OUTPUT=config.conf] [METRIC=latency_us]"; \
echo "Example: make optimize-config CSV_FILE=scripts/sample_performance_data.csv"; \
exit 1; \
fi
python3 scripts/optimize_config.py $(CSV_FILE) \
$(if $(OUTPUT),-o $(OUTPUT)) \
$(if $(METRIC),-m $(METRIC)) \
$(if $(SIZE_RANGES),--size-ranges $(SIZE_RANGES)) \
$(if $(DRY_RUN),--dry-run) \
$(if $(NO_HEADER),--no-header)
clean:
rm -f ${BUILDDIR}/$(PLUGIN_SO)
$(MAKE) -C test clean
.PHONY: test test-verbose test-build optimize-config clean

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@ -1,164 +0,0 @@
# NCCL Example Tuner Plugin
This example plugin shows a practical example of a CSV file-based tuning approach, allowing selective overrides for tuning parameters based on all tuning inputs without recompiling.
## Features
- **File-based Configuration**: Read tuning parameters from a CSV configuration file
- **Size-based Tuning**: Specify different configurations based on message size ranges
- **Dimension-aware Tuning**: Match configurations based on number of nodes and ranks
- **Optional Channels Configuration**: Set specific channel counts or use -1 to keep NCCL's default
- **Environment Variable Support**: Specify config file location via `NCCL_TUNER_CONFIG_FILE`
- **Fallback Behavior**: Gracefully handles missing config files and invalid entries
## Building
```bash
make
```
This will create `libnccl-tuner-example.so` that can be loaded by NCCL.
## Configuration File Format
The configuration file uses CSV (Comma-Separated Values) format with one configuration per line:
```
collective_type,min_bytes,max_bytes,algorithm,protocol,channels,nNodes,nRanks,numPipeOps,regBuff
```
### Parameters
- **collective_type**: The collective operation type
- `broadcast`, `reduce`, `allgather`, `reducescatter`, `allreduce`
- **min_bytes/max_bytes**: The message size range (in bytes) for which this config applies
- Use `0` for minimum and `4294967295` for maximum (covers all sizes)
- **algorithm**: The NCCL algorithm to use
- `tree`, `ring`, `collnet_direct`, `collnet_chain`, `nvls`, `nvls_tree`, `pat`
- **protocol**: The NCCL protocol to use
- `ll`, `ll128`, `simple`
- **channels**: Number of channels (SMs) to use
- Use a positive integer to specify exact channel count
- Use `-1` to keep NCCL's default channel selection
- **nNodes**: Number of nodes to match
- Use a positive integer to match specific node count
- Use `-1` to match any number of nodes
- **nRanks**: Number of ranks to match
- Use a positive integer to match specific rank count
- Use `-1` to match any number of ranks
- **numPipeOps**: Number of pipeline operations to match (optional)
- Use a positive integer to match specific pipeline operation count
- Use `-1` to match any number of pipeline operations
- If omitted, configuration will match any numPipeOps value
- **regBuff**: Whether user buffer can be registered (optional)
- Use `0` to match only non-registered buffers
- Use `1` to match only registered buffers
- Use `-1` to match either registered or non-registered buffers
- If omitted, configuration will match any regBuff value
### Example Configuration
```csv
# Single-node, small allreduce: use tree algorithm, registered buffers only
allreduce,0,65536,tree,simple,2,1,-1,-1,1
# 4-node, 32-rank setup: medium allreduce, single pipeline op, non-registered buffers
allreduce,65537,1048576,ring,simple,4,4,32,1,0
# Any topology: large allreduce with LL128, multiple pipeline ops, any buffer type
allreduce,1048577,4294967295,ring,ll128,-1,-1,-1,4,-1
# Single-node broadcast: prefer tree, any pipeOps, registered buffers (backward compatible)
broadcast,0,32768,tree,simple,-1,1,-1
# Multi-node broadcast: optimized for non-registered buffers, single pipeline op
broadcast,32769,4294967295,ring,simple,2,-1,-1,1,0
```
Comments start with `#` and empty lines are ignored. The CSV format makes it easy to edit configurations in spreadsheet applications like Excel, Google Sheets, or LibreOffice Calc.
### Backward Compatibility
Configurations without the numPipeOps and/or regBuff parameters are fully supported:
- 8 fields: matches any numPipeOps and regBuff values
- 9 fields: matches any regBuff value
- 10 fields: full parameter specification
This ensures existing configuration files continue to work without modification.
## Usage
### Method 1: Default Config File
Place your configuration in `nccl_tuner.conf` in the current working directory.
### Method 2: Environment Variable
Set the `NCCL_TUNER_CONFIG_FILE` environment variable to specify the config file path:
```bash
export NCCL_TUNER_CONFIG_FILE=/path/to/your/tuner.conf
export LD_LIBRARY_PATH=/path/to/plugin:$LD_LIBRARY_PATH
mpirun -np 4 your_nccl_application
```
## Editing Configuration Files
### Generating Configuration Files from Raw Data
A python script to generate valid CSV configs has been provided. [Using optimize_config.py](scripts/README.md).
### Spreadsheet Tips:
- Use column headers: `collective_type,min_bytes,max_bytes,algorithm,protocol,channels,nNodes,nRanks,numPipeOps,regBuff`
- Save as CSV format (not Excel format) for the plugin to read
- Use data validation to prevent typos in algorithm/protocol names
## Logging
The plugin uses NCCL's logging system. To see tuner-related messages:
```bash
export NCCL_DEBUG=INFO
```
This will show when configurations are loaded and applied, including the topology information.
For detailed debugging output during tuning decisions:
```bash
export NCCL_DEBUG=TRACE
```
This will show verbose information about which configurations are being evaluated and matched.
## Dimension Matching
Configurations are only applied when the topology matches:
- **Exact Match**: Configuration specifies `nNodes=4,nRanks=32`, only applied when communicator has exactly 4 nodes and 32 ranks
- **Wildcard Nodes**: Configuration specifies `nNodes=-1,nRanks=8`, applied to any topology with exactly 8 ranks
- **Wildcard Ranks**: Configuration specifies `nNodes=2,nRanks=-1`, applied to any 2-node topology regardless of ranks per node
- **Wildcard Both**: Configuration specifies `nNodes=-1,nRanks=-1`, applied to any topology
This allows you to create specialized configurations for different cluster setups while maintaining flexibility.
## Default Behavior
If no configuration file is found or no matching configuration exists for a collective operation, the plugin falls back to preferring the ring algorithm with simple protocol. All configured algorithm/protocol combinations are given a low cost (0.0) to make them preferred by NCCL's selection logic.
When channels is set to `-1`, NCCL's default channel selection logic is preserved, allowing the system to automatically determine the optimal number of channels based on hardware and message size.
## Troubleshooting
1. **Config file not found**: Check the file path and permissions
2. **Configurations not applied**: Verify the collective type, size ranges, algorithm/protocol names, and topology parameters
3. **Plugin not loaded**: Ensure `LD_LIBRARY_PATH` includes the plugin directory
4. **No effect on performance**: Check that NCCL is actually using the tuner plugin with `NCCL_DEBUG=INFO`
5. **Topology mismatch**: Verify that nNodes and nRanks match your actual setup, or use -1 for wildcards
6. **CSV parsing errors**: Ensure no spaces after commas, or quote fields containing spaces

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/*************************************************************************
* Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#ifndef COMMON_H_
#define COMMON_H_
typedef enum {NCCL_LOG_NONE=0, NCCL_LOG_VERSION=1, NCCL_LOG_WARN=2, NCCL_LOG_INFO=3, NCCL_LOG_ABORT=4, NCCL_LOG_TRACE=5} ncclDebugLogLevel;
typedef enum {NCCL_INIT=1, NCCL_COLL=2, NCCL_P2P=4, NCCL_SHM=8, NCCL_NET=16, NCCL_GRAPH=32, NCCL_TUNING=64, NCCL_ENV=128, NCCL_ALLOC=256, NCCL_CALL=512, NCCL_PROXY=1024, NCCL_NVLS=2048, NCCL_BOOTSTRAP=4096, NCCL_REG=8192, NCCL_ALL=~0} ncclDebugLogSubSys;
typedef void (*ncclDebugLogger_t)(ncclDebugLogLevel level, unsigned long flags, const char *file, int line, const char *fmt, ...);
#endif

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/*
* Copyright (c) 2017-2022, NVIDIA CORPORATION. All rights reserved.
*/
#ifndef NCCL_ERR_H_
#define NCCL_ERR_H_
/* Error type for plugins */
typedef enum { ncclSuccess = 0,
ncclUnhandledCudaError = 1,
ncclSystemError = 2,
ncclInternalError = 3,
ncclInvalidArgument = 4,
ncclInvalidUsage = 5,
ncclRemoteError = 6 } ncclResult_t;
#endif

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/*************************************************************************
* Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
* Copyright (c) 2023, Meta Platforms, Inc. and affiliates.
*
* See LICENSE.txt for license information
************************************************************************/
#ifndef NCCL_TUNER_H_
#define NCCL_TUNER_H_
#include <stdint.h>
#include <stdlib.h>
#include "common.h"
#include "err.h"
#define NCCL_NUM_FUNCTIONS 5 // Send/Recv not included for now
typedef enum {
ncclFuncBroadcast = 0,
ncclFuncReduce = 1,
ncclFuncAllGather = 2,
ncclFuncReduceScatter = 3,
ncclFuncAllReduce = 4,
ncclFuncSendRecv = 5,
ncclFuncSend = 6,
ncclFuncRecv = 7,
ncclNumFuncs = 8
} ncclFunc_t;
#define NCCL_NUM_ALGORITHMS 7 // Tree/Ring/CollNet*
#define NCCL_ALGO_UNDEF -1
#define NCCL_ALGO_TREE 0
#define NCCL_ALGO_RING 1
#define NCCL_ALGO_COLLNET_DIRECT 2
#define NCCL_ALGO_COLLNET_CHAIN 3
#define NCCL_ALGO_NVLS 4
#define NCCL_ALGO_NVLS_TREE 5
#define NCCL_ALGO_PAT 6
#define NCCL_NUM_PROTOCOLS 3 // Simple/LL/LL128
#define NCCL_PROTO_UNDEF -1
#define NCCL_PROTO_LL 0
#define NCCL_PROTO_LL128 1
#define NCCL_PROTO_SIMPLE 2
#define NCCL_ALGO_PROTO_IGNORE -1.0
// API to be implemented by external tuner
typedef struct {
// Name of the tuner
const char* name;
// Initializes tuner states.
// Inputs:
// - nRanks: number of ranks in current communicator. Each communicator initialize its own tuner.
// - nNodes: number of nodes in current communicator.
// - logFunction: a logFunction can be useful to integrate logging together with NCCL core.
// Outputs:
// - context: tuner context object
ncclResult_t (*init)(size_t nRanks, size_t nNodes, ncclDebugLogger_t logFunction, void **context);
// Gets info (algo, protocol, number of ctas and threads) for a given collective.
// Inputs:
// - context: tuner context object
// - collType: collective type , e.g., allreduce, allgather…
// - nBytes: collective size in bytes
// - numPipeOps: number of operations in the group
// - numAlgo: number of algorithms in collCostTable
// - numProto: number of protocols in collCostTable
// - regBuff: can register user buffer
//
// Outputs:
// - nChannels: number of channels (hence SMs) to be used.
//
// InOut:
// - collCostTable: collective cost table, generated by NCCL core, containing algo|proto|time entries for collType.
// NCCL core sets ignored algo/proto cost table entries to -1.0 (NCCL_ALGO_PROTO_IGNORE).
//
// If getCollInfo() does not return ncclSuccess, NCCL will fall back to the
// default tuning for the given collective.
// Also, the plugin is allowed to not set any output, or set only the
// algorithm and protocol, but not only the algorithm or only the protocol.
// Unset fields will be set automatically by NCCL.
ncclResult_t (*getCollInfo)(void* context, ncclFunc_t collType, size_t nBytes,
int numPipeOps, float** collCostTable, int numAlgo, int numProto,
int regBuff, int* nChannels);
// Terminates the plugin and cleans up any resources that the plugin allocated.
// context: tuner context object
ncclResult_t (*destroy)(void* context);
} ncclTuner_v4_t;
typedef ncclTuner_v4_t ncclTuner_t;
#define NCCL_TUNER_PLUGIN_SYMBOL "ncclTunerPlugin_v4"
#endif

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# NCCL Tuner Configuration File (CSV Format)
# Format: collective_type,min_bytes,max_bytes,algorithm,protocol,channels,nNodes,nRanks,numPipeOps,regBuff
#
# Collective types: broadcast, reduce, allgather, reducescatter, allreduce
# Algorithms: tree, ring, collnet_direct, collnet_chain, nvls, nvls_tree, pat
# Protocols: ll, ll128, simple
# Channels: number of channels to use, or -1 to keep default
# nNodes: number of nodes to match, or -1 for any number of nodes
# nRanks: number of ranks to match, or -1 for any number of ranks
# numPipeOps: number of pipeline operations to match, or -1 for any number (optional)
# regBuff: whether user buffer can be registered (0=no, 1=yes, -1=any) (optional)
#
# Note: numPipeOps and regBuff parameters are optional - configurations without them will match any value
#
# Examples:
# For single-node configurations with registered buffers
# Small allreduce operations on single node - use tree algorithm, registered buffers
allreduce,0,65536,tree,simple,2,1,-1,-1,1
# For multi-node configurations with 4 nodes, 32 total ranks, single pipeline op, non-registered buffers
# Medium allreduce operations - use ring algorithm
allreduce,65537,1048576,ring,simple,4,4,32,1,0
# For any topology - large allreduce operations with LL128 protocol, multiple pipeline ops, any buffer type
allreduce,1048577,4294967295,ring,ll128,-1,-1,-1,4,-1
# Broadcast operations - different configs for different topologies, pipeline complexity, and buffer types
# Single node broadcast - prefer tree, any pipeOps, registered buffers only
broadcast,0,32768,tree,simple,-1,1,-1,-1,1
# Multi-node broadcast with single pipeline operation, non-registered buffers - use ring
broadcast,32769,4294967295,ring,simple,2,-1,-1,1,0
# AllGather operations - optimized for 2-node configurations, any pipeOps, any buffer type
allgather,0,4294967295,ring,simple,4,2,-1
# ReduceScatter operations
# Small messages on single node, single pipeline op, registered buffers
reducescatter,0,131072,tree,simple,2,1,-1,1,1
# Large messages on any topology, multiple pipeline ops, non-registered buffers
reducescatter,131073,4294967295,ring,simple,-1,-1,-1,2,0
# Reduce operations - any topology, keep default channels, any pipeOps, any buffer type
reduce,0,4294967295,tree,simple,-1,-1,-1

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/*************************************************************************
* Copyright (c) 2015-2019, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#include "tuner.h"
#include <stdio.h>
#include <string.h>
#include <stdlib.h>
#define __hidden __attribute__ ((visibility("hidden")))
#define MAX_LINE_LENGTH 256
// CSV field indices for configuration parsing
// Format: colltype,minbytes,maxbytes,algorithm,protocol,channels,nNodes,nRanks,numPipeOps,regBuff
#define CONFIG_FIELD_COLLTYPE 0
#define CONFIG_FIELD_MINBYTES 1
#define CONFIG_FIELD_MAXBYTES 2
#define CONFIG_FIELD_ALGORITHM 3
#define CONFIG_FIELD_PROTOCOL 4
#define CONFIG_FIELD_CHANNELS 5
#define CONFIG_FIELD_NNODES 6
#define CONFIG_FIELD_NRANKS 7
#define CONFIG_FIELD_PIPEOPS 8 // Optional field
#define CONFIG_FIELD_REGBUFF 9 // Optional field
// Field count constants
#define CONFIG_FIELDS_REQUIRED 8 // Minimum required fields (up to nRanks)
#define CONFIG_FIELDS_WITH_PIPEOPS 9 // Fields including numPipeOps
#define CONFIG_FIELDS_WITH_REGBUFF 10 // Fields including both numPipeOps and regBuff
#define CONFIG_FIELDS_MAX 10 // Maximum number of fields supported
typedef struct {
ncclFunc_t collType;
size_t minBytes;
size_t maxBytes;
int algorithm;
int protocol;
int nChannels;
int nNodes;
int nRanks;
int numPipeOps;
int regBuff;
} TuningConfig;
typedef struct {
TuningConfig* configs; // Changed from static array to dynamic pointer
int numConfigs;
int maxConfigs; // Added to track allocated size
size_t nRanks;
size_t nNodes;
ncclDebugLogger_t logFunction;
} TunerContext;
// Parse collective type from string
static ncclFunc_t parseCollType(const char* str) {
if (strcmp(str, "broadcast") == 0) return ncclFuncBroadcast;
if (strcmp(str, "reduce") == 0) return ncclFuncReduce;
if (strcmp(str, "allgather") == 0) return ncclFuncAllGather;
if (strcmp(str, "reducescatter") == 0) return ncclFuncReduceScatter;
if (strcmp(str, "allreduce") == 0) return ncclFuncAllReduce;
return ncclFuncAllReduce; // default
}
// Convert collective type to string
static const char* collTypeToString(ncclFunc_t collType) {
switch (collType) {
case ncclFuncBroadcast: return "broadcast";
case ncclFuncReduce: return "reduce";
case ncclFuncAllGather: return "allgather";
case ncclFuncReduceScatter: return "reducescatter";
case ncclFuncAllReduce: return "allreduce";
default: return "unknown";
}
}
// Parse algorithm from string
static int parseAlgorithm(const char* str) {
if (strcmp(str, "tree") == 0) return NCCL_ALGO_TREE;
if (strcmp(str, "ring") == 0) return NCCL_ALGO_RING;
if (strcmp(str, "collnet_direct") == 0) return NCCL_ALGO_COLLNET_DIRECT;
if (strcmp(str, "collnet_chain") == 0) return NCCL_ALGO_COLLNET_CHAIN;
if (strcmp(str, "nvls") == 0) return NCCL_ALGO_NVLS;
if (strcmp(str, "nvls_tree") == 0) return NCCL_ALGO_NVLS_TREE;
if (strcmp(str, "pat") == 0) return NCCL_ALGO_PAT;
return NCCL_ALGO_RING; // default
}
// Convert algorithm to string
static const char* algorithmToString(int algorithm) {
switch (algorithm) {
case NCCL_ALGO_TREE: return "tree";
case NCCL_ALGO_RING: return "ring";
case NCCL_ALGO_COLLNET_DIRECT: return "collnet_direct";
case NCCL_ALGO_COLLNET_CHAIN: return "collnet_chain";
case NCCL_ALGO_NVLS: return "nvls";
case NCCL_ALGO_NVLS_TREE: return "nvls_tree";
case NCCL_ALGO_PAT: return "pat";
default: return "unknown";
}
}
// Parse protocol from string
static int parseProtocol(const char* str) {
if (strcmp(str, "ll") == 0) return NCCL_PROTO_LL;
if (strcmp(str, "ll128") == 0) return NCCL_PROTO_LL128;
if (strcmp(str, "simple") == 0) return NCCL_PROTO_SIMPLE;
return NCCL_PROTO_SIMPLE; // default
}
// Convert protocol to string
static const char* protocolToString(int protocol) {
switch (protocol) {
case NCCL_PROTO_LL: return "ll";
case NCCL_PROTO_LL128: return "ll128";
case NCCL_PROTO_SIMPLE: return "simple";
default: return "unknown";
}
}
// Helper function to count valid configuration lines in file
static int countConfigLines(const char* filename) {
FILE* file = fopen(filename, "r");
if (!file) {
return 0;
}
char line[MAX_LINE_LENGTH];
int count = 0;
while (fgets(line, sizeof(line), file)) {
// Skip comments and empty lines
if (line[0] == '#' || line[0] == '\n') continue;
// Remove trailing newline
line[strcspn(line, "\n")] = 0;
// Check if line has content
if (strlen(line) > 0) {
count++;
}
}
fclose(file);
return count;
}
// Load configuration from file
static ncclResult_t loadConfig(TunerContext* ctx, const char* filename) {
FILE* file = fopen(filename, "r");
if (!file) {
if (ctx->logFunction) {
ctx->logFunction(NCCL_LOG_INFO, NCCL_TUNING, __FILE__, __LINE__,
"TUNER/ExamplePlugin: Config file %s not found, using defaults", filename);
}
return ncclSuccess; // Not finding config file is not an error
}
// First pass: count valid configuration lines
int configCount = countConfigLines(filename);
if (configCount == 0) {
if (ctx->logFunction) {
ctx->logFunction(NCCL_LOG_INFO, NCCL_TUNING, __FILE__, __LINE__,
"TUNER/ExamplePlugin: No valid configurations found in %s", filename);
}
fclose(file);
return ncclSuccess;
}
// Allocate memory for configurations based on actual count
ctx->configs = (TuningConfig*)malloc(configCount * sizeof(TuningConfig));
if (!ctx->configs) {
if (ctx->logFunction) {
ctx->logFunction(NCCL_LOG_INFO, NCCL_TUNING, __FILE__, __LINE__,
"TUNER/ExamplePlugin: Failed to allocate memory for %d configurations", configCount);
}
fclose(file);
return ncclSystemError;
}
ctx->maxConfigs = configCount;
ctx->numConfigs = 0;
if (ctx->logFunction) {
ctx->logFunction(NCCL_LOG_INFO, NCCL_TUNING, __FILE__, __LINE__,
"TUNER/ExamplePlugin: Allocated memory for %d configurations", configCount);
}
// Reset file pointer to beginning
fseek(file, 0, SEEK_SET);
char line[MAX_LINE_LENGTH];
while (fgets(line, sizeof(line), file) && ctx->numConfigs < ctx->maxConfigs) {
// Skip comments and empty lines
if (line[0] == '#' || line[0] == '\n') continue;
// Remove trailing newline
line[strcspn(line, "\n")] = 0;
// Parse CSV format: colltype,minbytes,maxbytes,algorithm,protocol,channels,nNodes,nRanks,numPipeOps,regBuff
char* token;
char* tokens[CONFIG_FIELDS_MAX];
int tokenCount = 0;
// Make a copy of the line for tokenizing
char lineCopy[MAX_LINE_LENGTH];
strncpy(lineCopy, line, sizeof(lineCopy));
lineCopy[sizeof(lineCopy) - 1] = '\0';
// Tokenize by comma
token = strtok(lineCopy, ",");
while (token != NULL && tokenCount < CONFIG_FIELDS_MAX) {
// Trim whitespace
while (*token == ' ' || *token == '\t') token++;
char* end = token + strlen(token) - 1;
while (end > token && (*end == ' ' || *end == '\t')) {
*end = '\0';
end--;
}
tokens[tokenCount++] = token;
token = strtok(NULL, ",");
}
// Validate field count: support required fields (8), with pipeOps (9), or with regBuff (10)
if (tokenCount >= CONFIG_FIELDS_REQUIRED && tokenCount <= CONFIG_FIELDS_MAX) {
TuningConfig* config = &ctx->configs[ctx->numConfigs];
config->collType = parseCollType(tokens[CONFIG_FIELD_COLLTYPE]);
config->minBytes = (size_t)strtoull(tokens[CONFIG_FIELD_MINBYTES], NULL, 10);
config->maxBytes = (size_t)strtoull(tokens[CONFIG_FIELD_MAXBYTES], NULL, 10);
config->algorithm = parseAlgorithm(tokens[CONFIG_FIELD_ALGORITHM]);
config->protocol = parseProtocol(tokens[CONFIG_FIELD_PROTOCOL]);
config->nChannels = atoi(tokens[CONFIG_FIELD_CHANNELS]);
config->nNodes = atoi(tokens[CONFIG_FIELD_NNODES]);
config->nRanks = atoi(tokens[CONFIG_FIELD_NRANKS]);
// numPipeOps is optional (9th field, index 8)
if (tokenCount >= CONFIG_FIELDS_WITH_PIPEOPS) {
config->numPipeOps = atoi(tokens[CONFIG_FIELD_PIPEOPS]);
} else {
config->numPipeOps = -1; // -1 means match any numPipeOps
}
// regBuff is optional (10th field, index 9)
if (tokenCount >= CONFIG_FIELDS_WITH_REGBUFF) {
config->regBuff = atoi(tokens[CONFIG_FIELD_REGBUFF]);
} else {
config->regBuff = -1; // -1 means match any regBuff value
}
ctx->numConfigs++;
if (ctx->logFunction) {
if (config->numPipeOps == -1 && config->regBuff == -1) {
ctx->logFunction(NCCL_LOG_INFO, NCCL_TUNING, __FILE__, __LINE__,
"TUNER/ExamplePlugin: Loaded config: %s [%zu-%zu] %s/%s channels=%d nodes=%d ranks=%d pipeOps=any regBuff=any",
tokens[CONFIG_FIELD_COLLTYPE], config->minBytes, config->maxBytes,
tokens[CONFIG_FIELD_ALGORITHM], tokens[CONFIG_FIELD_PROTOCOL],
config->nChannels, config->nNodes, config->nRanks);
} else if (config->regBuff == -1) {
ctx->logFunction(NCCL_LOG_INFO, NCCL_TUNING, __FILE__, __LINE__,
"TUNER/ExamplePlugin: Loaded config: %s [%zu-%zu] %s/%s channels=%d nodes=%d ranks=%d pipeOps=%d regBuff=any",
tokens[CONFIG_FIELD_COLLTYPE], config->minBytes, config->maxBytes,
tokens[CONFIG_FIELD_ALGORITHM], tokens[CONFIG_FIELD_PROTOCOL],
config->nChannels, config->nNodes, config->nRanks, config->numPipeOps);
} else if (config->numPipeOps == -1) {
ctx->logFunction(NCCL_LOG_INFO, NCCL_TUNING, __FILE__, __LINE__,
"TUNER/ExamplePlugin: Loaded config: %s [%zu-%zu] %s/%s channels=%d nodes=%d ranks=%d pipeOps=any regBuff=%d",
tokens[CONFIG_FIELD_COLLTYPE], config->minBytes, config->maxBytes,
tokens[CONFIG_FIELD_ALGORITHM], tokens[CONFIG_FIELD_PROTOCOL],
config->nChannels, config->nNodes, config->nRanks, config->regBuff);
} else {
ctx->logFunction(NCCL_LOG_INFO, NCCL_TUNING, __FILE__, __LINE__,
"TUNER/ExamplePlugin: Loaded config: %s [%zu-%zu] %s/%s channels=%d nodes=%d ranks=%d pipeOps=%d regBuff=%d",
tokens[CONFIG_FIELD_COLLTYPE], config->minBytes, config->maxBytes,
tokens[CONFIG_FIELD_ALGORITHM], tokens[CONFIG_FIELD_PROTOCOL],
config->nChannels, config->nNodes, config->nRanks, config->numPipeOps, config->regBuff);
}
}
}
}
fclose(file);
if (ctx->logFunction) {
ctx->logFunction(NCCL_LOG_INFO, NCCL_TUNING, __FILE__, __LINE__,
"TUNER/ExamplePlugin: Loaded %d tuning configurations from %s", ctx->numConfigs, filename);
}
return ncclSuccess;
}
__hidden ncclResult_t pluginInit(size_t nRanks, size_t nNodes, ncclDebugLogger_t logFunction, void **context) {
TunerContext* ctx = (TunerContext*)malloc(sizeof(TunerContext));
if (!ctx) return ncclSystemError;
ctx->configs = NULL; // Initialize to NULL
ctx->numConfigs = 0;
ctx->maxConfigs = 0; // Initialize to 0
ctx->nRanks = nRanks;
ctx->nNodes = nNodes;
ctx->logFunction = logFunction;
if (logFunction) {
logFunction(NCCL_LOG_INFO, NCCL_TUNING, __FILE__, __LINE__,
"TUNER/ExamplePlugin: Initializing tuner for %zu nodes, %zu ranks", nNodes, nRanks);
}
// Try to load config file from environment variable or default location
const char* configFile = getenv("NCCL_TUNER_CONFIG_FILE");
if (!configFile) {
configFile = "nccl_tuner.conf"; // default config file name
}
ncclResult_t result = loadConfig(ctx, configFile);
if (result != ncclSuccess) {
if (ctx->configs) {
free(ctx->configs); // Clean up allocated memory on error
}
free(ctx);
return result;
}
*context = ctx;
return ncclSuccess;
}
__hidden ncclResult_t pluginGetCollInfo(void* context, ncclFunc_t collType, size_t nBytes,
int numPipeOps, float** collCostTable, int numAlgo, int numProto,
int regBuff, int* nChannels) {
TunerContext* ctx = (TunerContext*)context;
if (!ctx) return ncclInternalError;
// Default channels
*nChannels = 1;
if (ctx->logFunction) {
ctx->logFunction(NCCL_LOG_TRACE, NCCL_TUNING, __FILE__, __LINE__,
"TUNER/ExamplePlugin: pluginGetCollInfo called - collType=%s, nBytes=%zu, numPipeOps=%d, regBuff=%d, numConfigs=%d",
collTypeToString(collType), nBytes, numPipeOps, regBuff, ctx->numConfigs);
}
// Look for matching configuration
for (int i = 0; i < ctx->numConfigs; i++) {
TuningConfig* config = &ctx->configs[i];
if (ctx->logFunction) {
ctx->logFunction(NCCL_LOG_TRACE, NCCL_TUNING, __FILE__, __LINE__,
"TUNER/ExamplePlugin: Checking config %d - collType=%s, minBytes=%zu, maxBytes=%zu, algo=%s, proto=%s, nNodes=%d, nRanks=%d, numPipeOps=%d, regBuff=%d",
i, collTypeToString(config->collType), config->minBytes, config->maxBytes, algorithmToString(config->algorithm), protocolToString(config->protocol),
config->nNodes, config->nRanks, config->numPipeOps, config->regBuff);
}
// Check if this config matches the current collective, size range, topology, pipeline ops, and regBuff
if (config->collType == collType &&
nBytes >= config->minBytes &&
nBytes <= config->maxBytes &&
(config->nNodes == -1 || config->nNodes == (int)ctx->nNodes) &&
(config->nRanks == -1 || config->nRanks == (int)ctx->nRanks) &&
(config->numPipeOps == -1 || config->numPipeOps == numPipeOps) &&
(config->regBuff == -1 || config->regBuff == regBuff)) {
if (ctx->logFunction) {
ctx->logFunction(NCCL_LOG_TRACE, NCCL_TUNING, __FILE__, __LINE__,
"TUNER/ExamplePlugin: Config matches. Applying algo=%s, proto=%s, channels=%d",
algorithmToString(config->algorithm), protocolToString(config->protocol), config->nChannels);
}
// Check bounds
if (config->algorithm < numAlgo && config->protocol < numProto) {
if (collCostTable[config->algorithm][config->protocol] != NCCL_ALGO_PROTO_IGNORE) {
if (ctx->logFunction) {
ctx->logFunction(NCCL_LOG_TRACE, NCCL_TUNING, __FILE__, __LINE__,
"TUNER/ExamplePlugin: Setting cost table[%s][%s] (%p) = 0.0 (was %.1f)",
algorithmToString(config->algorithm), protocolToString(config->protocol),
&collCostTable[config->algorithm][config->protocol], collCostTable[config->algorithm][config->protocol]);
}
collCostTable[config->algorithm][config->protocol] = 0.0; // Set low cost to prefer this configuration
// Only override channels if not set to -1 (keep default)
if (config->nChannels != -1) {
*nChannels = config->nChannels;
}
if (ctx->logFunction) {
if (config->nChannels == -1) {
ctx->logFunction(NCCL_LOG_INFO, NCCL_TUNING, __FILE__, __LINE__,
"TUNER/ExamplePlugin: Applied config for collType=%s, bytes=%zu, pipeOps=%d, regBuff=%d: algo=%s, proto=%s, channels=default (nodes=%d, ranks=%d)",
collTypeToString(config->collType), nBytes, numPipeOps, regBuff, algorithmToString(config->algorithm), protocolToString(config->protocol),
config->nNodes, config->nRanks);
} else {
ctx->logFunction(NCCL_LOG_INFO, NCCL_TUNING, __FILE__, __LINE__,
"TUNER/ExamplePlugin: Applied config for collType=%s, bytes=%zu, pipeOps=%d, regBuff=%d: algo=%s, proto=%s, channels=%d (nodes=%d, ranks=%d)",
collTypeToString(config->collType), nBytes, numPipeOps, regBuff, algorithmToString(config->algorithm), protocolToString(config->protocol),
config->nChannels, config->nNodes, config->nRanks);
}
}
return ncclSuccess;
} else {
if (ctx->logFunction) {
ctx->logFunction(NCCL_LOG_INFO, NCCL_TUNING, __FILE__, __LINE__,
"TUNER/ExamplePlugin: Algorithm/protocol combination [%s][%s] is marked as IGNORE",
algorithmToString(config->algorithm), protocolToString(config->protocol));
}
}
} else {
if (ctx->logFunction) {
ctx->logFunction(NCCL_LOG_INFO, NCCL_TUNING, __FILE__, __LINE__,
"TUNER/ExamplePlugin: Algorithm/protocol out of bounds - algo=%s (max %d), proto=%s (max %d)",
algorithmToString(config->algorithm), numAlgo, protocolToString(config->protocol), numProto);
}
}
} else {
if (ctx->logFunction) {
ctx->logFunction(NCCL_LOG_INFO, NCCL_TUNING, __FILE__, __LINE__,
"TUNER/ExamplePlugin: Config does not match - collType match=%d, size match=%d, nodes match=%d, ranks match=%d, pipeOps match=%d, regBuff match=%d",
config->collType == collType,
(nBytes >= config->minBytes && nBytes <= config->maxBytes),
(config->nNodes == -1 || config->nNodes == (int)ctx->nNodes),
(config->nRanks == -1 || config->nRanks == (int)ctx->nRanks),
(config->numPipeOps == -1 || config->numPipeOps == numPipeOps),
(config->regBuff == -1 || config->regBuff == regBuff));
}
}
}
// If no specific config found, apply default behavior
if (ctx->logFunction) {
ctx->logFunction(NCCL_LOG_INFO, NCCL_TUNING, __FILE__, __LINE__,
"TUNER/ExamplePlugin: No matching config found");
}
return ncclSuccess;
}
__hidden ncclResult_t pluginDestroy(void* context) {
if (context) {
TunerContext* ctx = (TunerContext*)context;
if (ctx->configs) {
free(ctx->configs); // Free dynamically allocated configs array
}
free(context);
}
return ncclSuccess;
}
#define PLUGIN_NAME "Example"
const ncclTuner_v4_t ncclTunerPlugin_v4 = {
.name = PLUGIN_NAME,
.init = pluginInit,
.getCollInfo = pluginGetCollInfo,
.destroy = pluginDestroy
};

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@ -1,106 +0,0 @@
# NCCL Tuner Configuration Scripts
This directory contains scripts for optimizing NCCL tuner configurations based on performance data.
## optimize_config.py
A Python script that reads performance data from CSV files and generates optimal NCCL tuner configurations.
### Usage
```bash
python scripts/optimize_config.py [options] <input_csv_file>
```
### Options
- `-o, --output FILE`: Output NCCL tuner config file (default: `nccl_tuner.conf`)
- `-m, --metric METRIC`: Optimization metric (`cost_metric`, `bandwidth_gbps`, `latency_us`)
- `--no-header`: Don't add header comments to output file
- `--dry-run`: Print configurations without writing to file
### CSV Input Format
The input CSV file should have the following columns:
```csv
collective,size_bytes,algorithm,protocol,channels,nodes,ranks,pipeOps,regBuff,cost_metric,bandwidth_gbps,latency_us
```
**Required columns:**
- `collective`: NCCL collective type (`allreduce`, `broadcast`, `reduce`, etc.)
- `size_bytes`: Message size in bytes
- `algorithm`: NCCL algorithm (`tree`, `ring`, `nvls`, etc.)
- `protocol`: NCCL protocol (`simple`, `ll`, `ll128`)
- `channels`: Number of channels (or `-1` for default)
- `nodes`: Number of nodes (or `-1` for any)
- `ranks`: Number of ranks (or `-1` for any)
- `pipeOps`: Number of pipeline operations (or `-1` for any)
- `regBuff`: Registered buffer flag (`0`, `1`, or `-1` for any)
**Optional metrics (must have at least one present):**
- `bandwidth_gbps`: Bandwidth in GB/s (higher is better)
- `latency_us`: Latency in microseconds (lower is better)
### Examples
**Basic usage with cost optimization:**
```bash
python scripts/optimize_config.py sample_performance_data.csv
```
**Optimize for bandwidth and write to custom file:**
```bash
python scripts/optimize_config.py -m bandwidth_gbps -o my_tuner.conf performance_data.csv
```
**Preview configurations without writing:**
```bash
python scripts/optimize_config.py --dry-run performance_data.csv
```
### How It Works
1. **Data Loading**: Reads CSV performance data and validates format
2. **Grouping**: Groups data by collective type, topology (nodes/ranks), and other parameters
3. **Size Ranges**: Automatically bins data into size ranges for optimization
4. **Optimization**: Finds the best performing configuration for each group/size combination
5. **Output**: Generates NCCL tuner config format and appends to specified file
### Default Size Ranges
The script uses these default size ranges (in bytes):
- Small: 0 - 1,024
- Medium: 1,025 - 65,536
- Large: 65,537 - 1,048,576
- XLarge: 1,048,577 - 16,777,216
- XXLarge: 16,777,217 - 4,294,967,295
### Sample Data
See `sample_performance_data.csv` for an example of the expected input format.
### Integration with NCCL
The generated configuration file can be used directly with the NCCL tuner plugin:
```bash
export NCCL_TUNER_CONFIG_FILE=/path/to/optimized_config.conf
export NCCL_TUNER_PLUGIN=/path/to/libnccl-tuner.so
mpirun -np 8 your_nccl_application
```
### Performance Data Collection
To collect performance data for optimization, you can:
1. **Use NCCL benchmarks** with different algorithm/protocol combinations
2. **Profile your applications** with various tuner settings
3. **Run systematic sweeps** across parameter combinations
4. **Use NCCL debug output** to collect timing information
The key is to have comprehensive data covering:
- Different message sizes (small to large)
- Various topologies (single node, multi-node)
- All relevant algorithm/protocol combinations
- Different channel counts and pipeline configurations

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@ -1,430 +0,0 @@
#!/usr/bin/env python3
"""
NCCL Tuner Configuration Optimizer
Reads a CSV file containing performance data across different tuning parameters
and generates optimal NCCL tuner configurations based on the best performing
combinations.
By default, creates growing size ranges that interpolate between the actual data sizes
for each unique dimension (node count, rank count combination). This ensures that
different cluster configurations get their own optimized size boundaries, as
performance characteristics often vary significantly between topologies.
Each dimension gets its own set of ranges starting from 0 and extending to the maximum
size for that dimension, with boundaries at midpoints between consecutive data sizes.
CSV Input Format:
collective,size_bytes,algorithm,protocol,channels,nodes,ranks,pipeOps,regBuff,bandwidth_gbps,latency_us
Output Format (NCCL Tuner Config):
collective_type,min_bytes,max_bytes,algorithm,protocol,channels,nNodes,nRanks,numPipeOps,regBuff
Usage Examples:
# Auto-create dimension-specific interpolated ranges (default)
python3 optimize_config.py data.csv
# Use custom size ranges (applied to all topologies)
python3 optimize_config.py data.csv --size-ranges "0-1024,1025-65536,65537-1048576"
# Use hardcoded default ranges (applied to all topologies)
python3 optimize_config.py data.csv --no-auto-ranges
"""
import csv
import argparse
import sys
import os
from collections import defaultdict
from typing import Dict, List, Tuple, Any
class PerformanceData:
def __init__(self, row: Dict[str, str]):
self.collective = row['collective']
self.size_bytes = int(row['size_bytes'])
self.algorithm = row['algorithm']
self.protocol = row['protocol']
self.channels = int(row['channels']) if row['channels'] != '-1' else -1
self.nodes = int(row['nodes']) if row['nodes'] != '-1' else -1
self.ranks = int(row['ranks']) if row['ranks'] != '-1' else -1
self.pipeOps = int(row['pipeOps']) if row['pipeOps'] != '-1' else -1
self.regBuff = int(row['regBuff']) if row['regBuff'] != '-1' else -1
# Performance metrics
self.bandwidth_gbps = float(row.get('bandwidth_gbps', 0)) # Higher is better
self.latency_us = float(row.get('latency_us', 0)) # Lower is better
def get_config_key(self) -> Tuple:
"""Generate a key for grouping similar configurations"""
return (self.collective, self.nodes, self.ranks, self.pipeOps, self.regBuff)
def get_size_range_key(self, topology_size_ranges: Dict[Tuple[int, int], List[Tuple[int, int]]]) -> Tuple[int, int]:
"""Find which size range this data point belongs to for its dimension"""
topology_key = (self.nodes, self.ranks)
# Get size ranges for this dimension, or fall back to default
if topology_key in topology_size_ranges:
size_ranges = topology_size_ranges[topology_key]
elif (-1, -1) in topology_size_ranges:
size_ranges = topology_size_ranges[(-1, -1)]
else:
# Fallback to first available dimension ranges
size_ranges = next(iter(topology_size_ranges.values()))
for min_size, max_size in size_ranges:
if min_size <= self.size_bytes <= max_size:
return (min_size, max_size)
# If no range found, create a single-point range
return (self.size_bytes, self.size_bytes)
class ConfigOptimizer:
def __init__(self, optimization_metric: str = 'latency_us'):
self.optimization_metric = optimization_metric
# Default size ranges - will be overridden by auto-detection
self.size_ranges = [
(0, 1024),
(1025, 64*1024),
(64*1024+1, 1024*1024),
(1024*1024+1, 16*1024*1024),
(16*1024*1024+1, 4*1024*1024*1024-1)
]
self.auto_size_ranges = True
def set_size_ranges(self, ranges: List[Tuple[int, int]]):
"""Set custom size ranges for optimization"""
self.size_ranges = ranges
self.auto_size_ranges = False
def auto_determine_size_ranges(self, data: List[PerformanceData]) -> Dict[Tuple[int, int], List[Tuple[int, int]]]:
"""Create growing size ranges for each unique (nodes, ranks) dimension"""
if not data:
return {(-1, -1): self.size_ranges}
# Group data by dimension (nodes, ranks)
topology_data = defaultdict(list)
for item in data:
topology_key = (item.nodes, item.ranks)
topology_data[topology_key].append(item)
topology_ranges = {}
for topology_key, items in topology_data.items():
nodes, ranks = topology_key
# Extract unique sizes for this dimension and sort them
unique_sizes = sorted(set(item.size_bytes for item in items))
if len(unique_sizes) <= 1:
# Only one size, create a single range from 0 to that size
size = unique_sizes[0] if unique_sizes else 0
ranges = [(0, size)]
else:
# Create growing ranges that interpolate between data points
ranges = []
for i, size in enumerate(unique_sizes):
if i == 0:
# First range: 0 to midpoint between first and second size
if len(unique_sizes) > 1:
next_size = unique_sizes[i + 1]
max_size = (size + next_size) // 2
else:
max_size = size
min_size = 0
elif i == len(unique_sizes) - 1:
# Last range: previous max + 1 to current size (and beyond)
min_size = ranges[-1][1] + 1
max_size = size
else:
# Intermediate ranges: previous max + 1 to midpoint with next size
min_size = ranges[-1][1] + 1
next_size = unique_sizes[i + 1]
max_size = (size + next_size) // 2
ranges.append((min_size, max_size))
topology_ranges[topology_key] = ranges
print(f"Dimension {nodes} nodes, {ranks} ranks: {len(ranges)} size ranges from {len(unique_sizes)} unique sizes:")
for i, (min_size, max_size) in enumerate(ranges):
# Count data points that fall in this range for this dimension
count = sum(1 for item in items if min_size <= item.size_bytes <= max_size)
actual_sizes = sorted(set(item.size_bytes for item in items if min_size <= item.size_bytes <= max_size))
if actual_sizes:
size_list = ', '.join(f"{s:,}" for s in actual_sizes[:3])
if len(actual_sizes) > 3:
size_list += f", ... (+{len(actual_sizes)-3} more)"
print(f" Range {i+1}: {min_size:,} - {max_size:,} bytes ({count} data points, sizes: {size_list})")
return topology_ranges
def load_data(self, csv_file: str) -> List[PerformanceData]:
"""Load performance data from CSV file"""
data = []
try:
with open(csv_file, 'r') as f:
reader = csv.DictReader(f)
for row in reader:
try:
data.append(PerformanceData(row))
except (ValueError, KeyError) as e:
print(f"Warning: Skipping invalid row: {row} - {e}")
except FileNotFoundError:
print(f"Error: File {csv_file} not found")
sys.exit(1)
except Exception as e:
print(f"Error reading {csv_file}: {e}")
sys.exit(1)
print(f"Loaded {len(data)} performance data points")
# Auto-determine size ranges if enabled
if self.auto_size_ranges and data:
self.topology_size_ranges = self.auto_determine_size_ranges(data)
else:
# Use default ranges for all topologies
self.topology_size_ranges = {(-1, -1): self.size_ranges}
return data
def is_better(self, new_data: PerformanceData, current_best: PerformanceData) -> bool:
"""Determine if new_data is better than current_best"""
if self.optimization_metric == 'bandwidth_gbps':
return new_data.bandwidth_gbps > current_best.bandwidth_gbps
elif self.optimization_metric == 'latency_us':
return new_data.latency_us < current_best.latency_us
else:
# Default to latency
return new_data.latency_us < current_best.latency_us
def optimize_configurations(self, data: List[PerformanceData]) -> List[str]:
"""Find optimal configurations and return as NCCL config strings"""
# Group data by configuration key and size range
grouped_data = defaultdict(lambda: defaultdict(list))
for item in data:
config_key = item.get_config_key()
size_range = item.get_size_range_key(self.topology_size_ranges)
grouped_data[config_key][size_range].append(item)
# Store optimal configurations before combining ranges
optimal_configs = []
for config_key, size_ranges_dict in grouped_data.items():
collective, nodes, ranks, pipeOps, regBuff = config_key
for (min_size, max_size), items in size_ranges_dict.items():
if not items:
continue
# Find the best performing configuration for this size range
best_item = items[0]
for item in items[1:]:
if self.is_better(item, best_item):
best_item = item
# Store the optimal configuration with its range
optimal_configs.append({
'collective': collective,
'min_size': min_size,
'max_size': max_size,
'algorithm': best_item.algorithm,
'protocol': best_item.protocol,
'channels': best_item.channels,
'nodes': best_item.nodes,
'ranks': best_item.ranks,
'pipeOps': best_item.pipeOps,
'regBuff': best_item.regBuff,
'metric_value': getattr(best_item, self.optimization_metric)
})
# Combine sequential ranges with identical tunings
combined_configs = self.combine_sequential_ranges(optimal_configs)
# Generate config strings
configs = []
for config in combined_configs:
config_str = f"{config['collective']},{config['min_size']},{config['max_size']},{config['algorithm']},{config['protocol']},{config['channels']},{config['nodes']},{config['ranks']},{config['pipeOps']},{config['regBuff']}"
configs.append(config_str)
print(f"Optimal for {config['collective']} [{config['min_size']}-{config['max_size']}] nodes={config['nodes']} ranks={config['ranks']}: "
f"{config['algorithm']}/{config['protocol']} channels={config['channels']} "
f"({self.optimization_metric}={config['metric_value']:.3f})")
return configs
def combine_sequential_ranges(self, configs: List[Dict]) -> List[Dict]:
"""Combine sequential ranges that have identical tuning parameters"""
if not configs:
return configs
# Group by collective and topology (nodes, ranks)
topology_groups = defaultdict(list)
for config in configs:
topology_key = (config['collective'], config['nodes'], config['ranks'],
config['pipeOps'], config['regBuff'])
topology_groups[topology_key].append(config)
combined_configs = []
for topology_key, topology_configs in topology_groups.items():
# Sort by min_size to ensure proper ordering
topology_configs.sort(key=lambda x: x['min_size'])
# Group by tuning parameters (algorithm, protocol, channels)
tuning_groups = defaultdict(list)
for config in topology_configs:
tuning_key = (config['algorithm'], config['protocol'], config['channels'])
tuning_groups[tuning_key].append(config)
# For each tuning group, combine sequential ranges
for tuning_key, tuning_configs in tuning_groups.items():
if not tuning_configs:
continue
# Sort by min_size
tuning_configs.sort(key=lambda x: x['min_size'])
# Combine sequential ranges
current_config = tuning_configs[0].copy()
for next_config in tuning_configs[1:]:
# Check if ranges are adjacent or overlapping
if current_config['max_size'] + 1 >= next_config['min_size']:
# Extend the current range
current_config['max_size'] = max(current_config['max_size'], next_config['max_size'])
# Update metric value to the better one
if self.optimization_metric == 'bandwidth_gbps':
if next_config['metric_value'] > current_config['metric_value']:
current_config['metric_value'] = next_config['metric_value']
else: # latency_us or default
if next_config['metric_value'] < current_config['metric_value']:
current_config['metric_value'] = next_config['metric_value']
else:
# Gap between ranges, save current and start new one
combined_configs.append(current_config)
current_config = next_config.copy()
# Add the last configuration
combined_configs.append(current_config)
# Sort final configs by collective, nodes, ranks, then min_size
combined_configs.sort(key=lambda x: (x['collective'], x['nodes'], x['ranks'], x['min_size']))
original_count = len(configs)
combined_count = len(combined_configs)
if combined_count < original_count:
print(f"Combined {original_count} ranges into {combined_count} ranges "
f"(reduced by {original_count - combined_count})")
return combined_configs
def append_to_config_file(self, configs: List[str], config_file: str, add_header: bool = True):
"""Append optimized configurations to NCCL tuner config file"""
try:
# Create directory if it doesn't exist
config_dir = os.path.dirname(config_file)
if config_dir and not os.path.exists(config_dir):
os.makedirs(config_dir)
print(f"Created directory: {config_dir}")
# Check if file exists and has content
file_exists = os.path.exists(config_file)
add_separator = False
if file_exists:
with open(config_file, 'r') as f:
content = f.read().strip()
add_separator = len(content) > 0
print(f"Appending to existing file: {config_file}")
else:
print(f"Creating new file: {config_file}")
with open(config_file, 'a') as f:
if add_separator:
f.write("\n\n")
if add_header:
f.write(f"# Optimized configurations generated by optimize_config.py\n")
f.write(f"# Optimization metric: {self.optimization_metric}\n")
f.write(f"# Format: collective_type,min_bytes,max_bytes,algorithm,protocol,channels,nNodes,nRanks,numPipeOps,regBuff\n")
for config in configs:
f.write(f"{config}\n")
if file_exists:
print(f"Appended {len(configs)} optimized configurations to {config_file}")
else:
print(f"Created {config_file} with {len(configs)} optimized configurations")
except PermissionError:
print(f"Error: Permission denied writing to {config_file}")
print("Try running with appropriate permissions or choose a different output location")
sys.exit(1)
except OSError as e:
print(f"Error: Cannot create/write to {config_file}: {e}")
print("Check that the path is valid and you have write permissions")
sys.exit(1)
except Exception as e:
print(f"Unexpected error writing to {config_file}: {e}")
sys.exit(1)
def main():
parser = argparse.ArgumentParser(description="Optimize NCCL tuner configurations from performance data")
parser.add_argument("csv_file", help="Input CSV file with performance data")
parser.add_argument("-o", "--output", default="nccl_tuner.conf",
help="Output NCCL tuner config file (default: nccl_tuner.conf)")
parser.add_argument("-m", "--metric", choices=['bandwidth_gbps', 'latency_us'],
default='latency_us', help="Optimization metric (default: latency_us)")
parser.add_argument("--no-header", action="store_true",
help="Don't add header comments to output file")
parser.add_argument("--dry-run", action="store_true",
help="Print configurations without writing to file")
parser.add_argument("--no-auto-ranges", action="store_true",
help="Disable automatic size range determination (use default ranges)")
parser.add_argument("--size-ranges", type=str,
help="Custom size ranges as comma-separated pairs: 'min1-max1,min2-max2,...'")
args = parser.parse_args()
optimizer = ConfigOptimizer(args.metric)
# Handle size range configuration
if args.size_ranges:
# Parse custom size ranges
try:
ranges = []
for range_str in args.size_ranges.split(','):
min_size, max_size = map(int, range_str.split('-'))
ranges.append((min_size, max_size))
optimizer.set_size_ranges(ranges)
print(f"Using custom size ranges: {ranges}")
except ValueError:
print("Error: Invalid size ranges format. Use 'min1-max1,min2-max2,...'")
sys.exit(1)
elif args.no_auto_ranges:
# Disable auto-ranging
optimizer.auto_size_ranges = False
print("Using default hardcoded size ranges")
else:
# Auto-ranging is enabled by default - creates one bucket per unique size
optimizer.auto_size_ranges = True
print("Auto-ranging enabled: will create one bucket per unique size in data")
# Load and optimize data
data = optimizer.load_data(args.csv_file)
if not data:
print("No valid data found in CSV file")
sys.exit(1)
configs = optimizer.optimize_configurations(data)
if args.dry_run:
print("\nGenerated configurations:")
for config in configs:
print(config)
else:
optimizer.append_to_config_file(configs, args.output, not args.no_header)
if __name__ == "__main__":
main()

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@ -1,24 +0,0 @@
collective,size_bytes,algorithm,protocol,channels,nodes,ranks,pipeOps,regBuff,cost_metric,bandwidth_gbps,latency_us
allreduce,1024,tree,simple,2,1,8,-1,-1,0.15,45.2,12.5
allreduce,1024,ring,simple,4,1,8,-1,-1,0.12,52.1,10.8
allreduce,1024,tree,ll,2,1,8,-1,-1,0.18,41.3,15.2
allreduce,1024,ring,ll,4,1,8,-1,-1,0.14,48.7,12.1
allreduce,32768,tree,simple,2,1,8,-1,-1,0.25,156.8,25.3
allreduce,32768,ring,simple,4,1,8,-1,-1,0.18,189.2,18.4
allreduce,32768,ring,ll128,8,1,8,-1,-1,0.16,201.5,16.2
allreduce,1048576,ring,simple,4,1,8,-1,-1,0.45,425.6,45.1
allreduce,1048576,ring,ll128,8,1,8,-1,-1,0.38,482.3,38.7
allreduce,1048576,nvls,simple,16,1,8,-1,-1,0.32,551.2,32.1
broadcast,1024,tree,simple,2,1,8,-1,-1,0.08,89.4,8.2
broadcast,1024,ring,simple,4,1,8,-1,-1,0.12,71.3,12.1
broadcast,32768,tree,simple,2,1,8,-1,-1,0.18,234.7,18.5
broadcast,32768,ring,ll128,4,1,8,-1,-1,0.15,267.8,15.2
broadcast,1048576,ring,simple,4,1,8,-1,-1,0.35,612.4,35.1
broadcast,1048576,ring,ll128,8,1,8,-1,-1,0.28,702.1,28.3
allreduce,1024,tree,simple,2,2,16,-1,-1,0.22,38.1,22.4
allreduce,1024,ring,simple,4,2,16,-1,-1,0.19,42.7,19.6
allreduce,32768,ring,simple,4,2,16,-1,-1,0.28,145.2,28.1
allreduce,32768,ring,ll128,8,2,16,-1,-1,0.24,167.8,24.3
allreduce,1048576,ring,simple,4,2,16,-1,-1,0.58,387.5,58.2
allreduce,1048576,ring,ll128,8,2,16,-1,-1,0.48,456.9,48.1
allreduce,1048576,nvls,simple,16,2,16,-1,-1,0.42,512.6,42.3
1 collective size_bytes algorithm protocol channels nodes ranks pipeOps regBuff cost_metric bandwidth_gbps latency_us
2 allreduce 1024 tree simple 2 1 8 -1 -1 0.15 45.2 12.5
3 allreduce 1024 ring simple 4 1 8 -1 -1 0.12 52.1 10.8
4 allreduce 1024 tree ll 2 1 8 -1 -1 0.18 41.3 15.2
5 allreduce 1024 ring ll 4 1 8 -1 -1 0.14 48.7 12.1
6 allreduce 32768 tree simple 2 1 8 -1 -1 0.25 156.8 25.3
7 allreduce 32768 ring simple 4 1 8 -1 -1 0.18 189.2 18.4
8 allreduce 32768 ring ll128 8 1 8 -1 -1 0.16 201.5 16.2
9 allreduce 1048576 ring simple 4 1 8 -1 -1 0.45 425.6 45.1
10 allreduce 1048576 ring ll128 8 1 8 -1 -1 0.38 482.3 38.7
11 allreduce 1048576 nvls simple 16 1 8 -1 -1 0.32 551.2 32.1
12 broadcast 1024 tree simple 2 1 8 -1 -1 0.08 89.4 8.2
13 broadcast 1024 ring simple 4 1 8 -1 -1 0.12 71.3 12.1
14 broadcast 32768 tree simple 2 1 8 -1 -1 0.18 234.7 18.5
15 broadcast 32768 ring ll128 4 1 8 -1 -1 0.15 267.8 15.2
16 broadcast 1048576 ring simple 4 1 8 -1 -1 0.35 612.4 35.1
17 broadcast 1048576 ring ll128 8 1 8 -1 -1 0.28 702.1 28.3
18 allreduce 1024 tree simple 2 2 16 -1 -1 0.22 38.1 22.4
19 allreduce 1024 ring simple 4 2 16 -1 -1 0.19 42.7 19.6
20 allreduce 32768 ring simple 4 2 16 -1 -1 0.28 145.2 28.1
21 allreduce 32768 ring ll128 8 2 16 -1 -1 0.24 167.8 24.3
22 allreduce 1048576 ring simple 4 2 16 -1 -1 0.58 387.5 58.2
23 allreduce 1048576 ring ll128 8 2 16 -1 -1 0.48 456.9 48.1
24 allreduce 1048576 nvls simple 16 2 16 -1 -1 0.42 512.6 42.3

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@ -1,30 +0,0 @@
#
# Makefile for NCCL Tuner Plugin Unit Tests
#
CC := gcc
CFLAGS := -Wall -Wextra -g -std=c99 -fPIC
INC := -I. -I../nccl
TARGET := test_plugin
SOURCES := test_plugin.c
# Default target
all: $(TARGET)
# Build the test executable
$(TARGET): $(SOURCES)
$(CC) $(CFLAGS) $(INC) -o $(TARGET) $(SOURCES)
# Run the tests
test: $(TARGET)
./$(TARGET) $(TEST_CASE)
# Run tests with verbose output
test-verbose: $(TARGET)
NCCL_DEBUG=INFO ./$(TARGET) $(TEST_CASE)
# Clean build artifacts
clean:
rm -f $(TARGET) *.o *.gcov *.gcda *.gcno test_*.conf
.PHONY: all test test-verbose clean

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@ -1,205 +0,0 @@
# NCCL Tuner Plugin Unit Tests
This directory contains comprehensive unit tests for the NCCL tuner plugin. The tests verify all major functionality including configuration parsing, matching logic, and cost table updates.
## Test Structure
```
test/
├── test_plugin.c # Main unit test file
├── Makefile # Build system for tests
└── README.md # This file
```
## Building and Running Tests
### Quick Start
```bash
# Build and run all tests
make test
# Or step by step
make # Build test executable
./test_plugin # Run tests
```
### Advanced Testing
```bash
# Run with memory leak detection (requires valgrind)
make test-memory
# Run with verbose logging
make test-verbose
# Generate code coverage report (requires gcov)
make coverage
# Create sample test configuration files
make test-configs
```
## Test Coverage
The unit tests cover the following functionality:
### 1. **Plugin Initialization (`test_plugin_init`)**
- Tests successful plugin initialization
- Verifies context allocation
- Tests cleanup on destroy
### 2. **Configuration Parsing (`test_config_parsing_valid`, `test_config_parsing_invalid`)**
- Valid CSV format parsing
- Comment and empty line handling
- Invalid format graceful handling
- Environment variable configuration
### 3. **Collective Type Matching (`test_collective_matching`)**
- Correct matching of allreduce, broadcast, etc.
- Algorithm/protocol selection
- Channel configuration
### 4. **Size Range Matching (`test_size_matching`)**
- Small, medium, large message size handling
- Proper range boundary checking
- Multiple size-based configurations
### 5. **Topology Matching (`test_topology_matching`)**
- Single-node vs multi-node configurations
- Exact nNodes/nRanks matching
- Wildcard matching (-1 values)
### 6. **Default Channels (`test_default_channels`)**
- Proper handling of -1 channel specification
- Preservation of NCCL default behavior
### 7. **Registered Buffer Matching (`test_regbuff_matching`)**
- Configurations based on regBuff parameter
- Registered vs non-registered buffer handling
- Backward compatibility with configs missing regBuff
### 8. **Pipeline Operations Matching (`test_pipeops_matching`)**
- Configurations based on numPipeOps parameter
- Single vs multiple pipeline operation handling
- Backward compatibility with configs missing numPipeOps
### 9. **Fallback Behavior (`test_no_match_fallback`)**
- Default behavior when no config matches
- Ring/Simple algorithm fallback
## Test Output
Successful test run:
```
Running NCCL Tuner Plugin Unit Tests
=====================================
PASS: test_plugin_init
PASS: test_config_parsing_valid
PASS: test_config_parsing_invalid
PASS: test_collective_matching
PASS: test_size_matching
PASS: test_topology_matching
PASS: test_default_channels
PASS: test_regbuff_matching
PASS: test_pipeops_matching
PASS: test_no_match_fallback
=====================================
Test Results: 9/9 tests passed
All tests PASSED!
```
Failed test example:
```
FAIL: test_collective_matching - Tree/Simple should have low cost
Test Results: 8/9 tests passed
Some tests FAILED!
```
## Mock NCCL Implementation
The tests use the actual NCCL header files from the `../nccl/` directory:
- `tuner.h` - Complete NCCL tuner interface and type definitions
- `common.h` - Common NCCL types and logging functions
- `err.h` - NCCL error codes
This allows testing with the real NCCL interface definitions while still being able to run tests without the full NCCL library installation.
## Integration with CI/CD
```bash
# Install tests for CI/CD pipeline
make install-test
# Run as part of automated testing
make test && echo "Tests passed" || echo "Tests failed"
```
## Memory Testing
The tests can be run with valgrind for memory leak detection:
```bash
make test-memory
```
This will detect:
- Memory leaks
- Invalid memory access
- Use of uninitialized memory
## Code Coverage
Generate code coverage reports to ensure comprehensive testing:
```bash
make coverage
# Creates test_plugin.c.gcov with line-by-line coverage
```
## Adding New Tests
To add a new test:
1. Create a new test function in `test_plugin.c`:
```c
int test_new_feature() {
// Test setup
TEST_ASSERT(condition, "description");
// Test cleanup
TEST_PASS();
}
```
2. Add the test to the main function:
```c
total++; passed += test_new_feature();
```
3. Rebuild and run:
```bash
make test
```
## Debugging Tests
For debugging failed tests:
```bash
# Compile with debug symbols
make CFLAGS="-g -O0 -DDEBUG"
# Run with gdb
gdb ./test_plugin
```
## Cleaning Up
```bash
# Remove all build artifacts and temporary files
make clean
```
This comprehensive test suite ensures the NCCL tuner plugin works correctly across all supported configurations and edge cases.

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@ -1,856 +0,0 @@
/*************************************************************************
* Unit tests for NCCL Tuner Plugin
************************************************************************/
#define _GNU_SOURCE // Enable setenv/unsetenv and other GNU extensions
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <assert.h>
#include <unistd.h>
#include <sys/stat.h>
#include <stdarg.h>
// Include NCCL tuner header (which includes common.h and err.h)
#include "tuner.h"
// Include plugin source for testing
#include "../plugin.c"
// Test framework macros
#define TEST_ASSERT(condition, message) \
do { \
if (!(condition)) { \
printf("FAIL: %s - %s\n", __func__, message); \
return 0; \
} \
} while(0)
#define TEST_PASS() \
do { \
printf("PASS: %s\n", __func__); \
return 1; \
} while(0)
// Global test state
static int test_log_count = 0;
// Mock logger function
void mock_logger(ncclDebugLogLevel level, unsigned long flags,
const char* file, int line, const char* fmt, ...) {
(void)flags; // Suppress unused parameter warning
test_log_count++;
// Check if we should print based on NCCL_DEBUG level
const char* debug_level = getenv("NCCL_DEBUG");
int should_print = 0;
if (debug_level) {
if (strcmp(debug_level, "TRACE") == 0) {
should_print = 1; // Print everything
} else if (strcmp(debug_level, "INFO") == 0 && level <= NCCL_LOG_INFO) {
should_print = 1; // Print INFO and below
} else if (strcmp(debug_level, "WARN") == 0 && level <= NCCL_LOG_WARN) {
should_print = 1; // Print WARN and below
}
}
if (!should_print) return;
// Convert log level to string
const char* level_str;
switch(level) {
case NCCL_LOG_NONE: level_str = "NONE"; break;
case NCCL_LOG_VERSION: level_str = "VERSION"; break;
case NCCL_LOG_WARN: level_str = "WARN"; break;
case NCCL_LOG_INFO: level_str = "INFO"; break;
case NCCL_LOG_ABORT: level_str = "ABORT"; break;
case NCCL_LOG_TRACE: level_str = "TRACE"; break;
default: level_str = "UNKNOWN"; break;
}
// Print log header
printf("[TUNER:%s:%s:%d] ", level_str, file, line);
// Print formatted message
va_list args;
va_start(args, fmt);
vprintf(fmt, args);
va_end(args);
printf("\n");
}
// Helper function to create test config file
void create_test_config(const char* filename, const char* content) {
FILE* f = fopen(filename, "w");
if (f) {
fprintf(f, "%s", content);
fclose(f);
}
}
// Test 1: Plugin initialization
int test_plugin_init() {
void* context = NULL;
// Test successful initialization
ncclResult_t result = pluginInit(8, 2, mock_logger, &context);
TEST_ASSERT(result == ncclSuccess, "Plugin init should succeed");
TEST_ASSERT(context != NULL, "Context should be allocated");
// Clean up
pluginDestroy(context);
TEST_PASS();
}
// Test 2: Configuration file parsing - valid CSV
int test_config_parsing_valid() {
const char* test_config =
"# Test configuration\n"
"allreduce,0,65536,tree,simple,2,1,-1,-1,-1\n"
"broadcast,0,32768,ring,ll128,4,2,16,-1,-1\n"
"# Comment line\n"
"\n" // Empty line
"reduce,1024,2048,tree,simple,-1,-1,-1,-1,-1\n";
create_test_config("test_valid.conf", test_config);
// Set environment variable to use our test config
setenv("NCCL_TUNER_CONFIG_FILE", "test_valid.conf", 1);
void* context = NULL;
ncclResult_t result = pluginInit(16, 2, mock_logger, &context);
TEST_ASSERT(result == ncclSuccess, "Plugin init with valid config should succeed");
// Clean up
pluginDestroy(context);
unlink("test_valid.conf");
unsetenv("NCCL_TUNER_CONFIG_FILE");
TEST_PASS();
}
// Test 3: Configuration file parsing - invalid CSV
int test_config_parsing_invalid() {
const char* test_config =
"allreduce,0,65536,tree,simple,2,1 # Missing nRanks and other fields\n"
"invalid_collective,0,1024,ring,simple,1,1,1,-1,-1\n"
"broadcast,abc,def,ring,simple,1,1,1,-1,-1\n"; // Invalid numbers
create_test_config("test_invalid.conf", test_config);
setenv("NCCL_TUNER_CONFIG_FILE", "test_invalid.conf", 1);
void* context = NULL;
ncclResult_t result = pluginInit(8, 1, mock_logger, &context);
// Should still succeed but with no valid configs loaded
TEST_ASSERT(result == ncclSuccess, "Plugin init should succeed even with invalid config");
// Clean up
pluginDestroy(context);
unlink("test_invalid.conf");
unsetenv("NCCL_TUNER_CONFIG_FILE");
TEST_PASS();
}
// Test 4: Collective type matching
int test_collective_matching() {
const char* test_config =
"allreduce,0,65536,tree,simple,8,1,-1,-1,-1\n"
"broadcast,0,32768,ring,ll128,4,-1,-1,-1,-1\n";
create_test_config("test_match.conf", test_config);
setenv("NCCL_TUNER_CONFIG_FILE", "test_match.conf", 1);
void* context = NULL;
pluginInit(8, 1, mock_logger, &context);
// Create mock cost table
float cost_table[NCCL_NUM_ALGORITHMS][NCCL_NUM_PROTOCOLS];
float* cost_table_ptr[NCCL_NUM_ALGORITHMS];
for (int i = 0; i < NCCL_NUM_ALGORITHMS; i++) {
cost_table_ptr[i] = cost_table[i];
for (int j = 0; j < NCCL_NUM_PROTOCOLS; j++) {
cost_table[i][j] = 1.0; // Default high cost
}
}
int nChannels;
// Test allreduce matching (should match first config)
ncclResult_t result = pluginGetCollInfo(context, ncclFuncAllReduce, 32768, 1,
cost_table_ptr, NCCL_NUM_ALGORITHMS, NCCL_NUM_PROTOCOLS,
0, &nChannels);
TEST_ASSERT(result == ncclSuccess, "GetCollInfo should succeed");
mock_logger(NCCL_LOG_INFO, NCCL_ALL, __FILE__, __LINE__,
"DEBUG: Checking cost_table[TREE][SIMPLE] (%p) = %.1f (expecting 0.0)",
&cost_table[NCCL_ALGO_TREE][NCCL_PROTO_SIMPLE], cost_table[NCCL_ALGO_TREE][NCCL_PROTO_SIMPLE]);
TEST_ASSERT(cost_table[NCCL_ALGO_TREE][NCCL_PROTO_SIMPLE] == 0.0, "Tree/Simple should have low cost");
TEST_ASSERT(nChannels == 8, "Should set 8 channels");
// Test broadcast matching (should match second config)
for (int i = 0; i < NCCL_NUM_ALGORITHMS; i++) {
for (int j = 0; j < NCCL_NUM_PROTOCOLS; j++) {
cost_table[i][j] = 1.0; // Reset costs
}
}
result = pluginGetCollInfo(context, ncclFuncBroadcast, 16384, 1,
cost_table_ptr, NCCL_NUM_ALGORITHMS, NCCL_NUM_PROTOCOLS,
0, &nChannels);
TEST_ASSERT(result == ncclSuccess, "GetCollInfo should succeed");
mock_logger(NCCL_LOG_INFO, NCCL_ALL, __FILE__, __LINE__,
"DEBUG: Checking cost_table[RING][LL128] (%p) = %.1f (expecting 0.0)",
&cost_table[NCCL_ALGO_RING][NCCL_PROTO_LL128], cost_table[NCCL_ALGO_RING][NCCL_PROTO_LL128]);
TEST_ASSERT(cost_table[NCCL_ALGO_RING][NCCL_PROTO_LL128] == 0.0, "Ring/LL128 should have low cost");
TEST_ASSERT(nChannels == 4, "Should set 4 channels");
// Clean up
pluginDestroy(context);
unlink("test_match.conf");
unsetenv("NCCL_TUNER_CONFIG_FILE");
TEST_PASS();
}
// Test 5: Size range matching
int test_size_matching() {
const char* test_config =
"allreduce,0,1024,tree,simple,2,-1,-1,-1,-1\n"
"allreduce,1025,65536,ring,simple,4,-1,-1,-1,-1\n"
"allreduce,65537,4294967295,ring,ll128,8,-1,-1,-1,-1\n";
create_test_config("test_size.conf", test_config);
setenv("NCCL_TUNER_CONFIG_FILE", "test_size.conf", 1);
void* context = NULL;
pluginInit(8, 1, mock_logger, &context);
float cost_table[NCCL_NUM_ALGORITHMS][NCCL_NUM_PROTOCOLS];
float* cost_table_ptr[NCCL_NUM_ALGORITHMS];
for (int i = 0; i < NCCL_NUM_ALGORITHMS; i++) {
cost_table_ptr[i] = cost_table[i];
for (int j = 0; j < NCCL_NUM_PROTOCOLS; j++) {
cost_table[i][j] = 1.0;
}
}
int nChannels = 1;
pluginGetCollInfo(context, ncclFuncAllReduce, 512, 1,
cost_table_ptr, NCCL_NUM_ALGORITHMS, NCCL_NUM_PROTOCOLS,
0, &nChannels);
mock_logger(NCCL_LOG_INFO, NCCL_ALL, __FILE__, __LINE__,
"DEBUG: Small message - checking cost_table[TREE][SIMPLE] (%p) = %.1f (expecting 0.0)",
&cost_table[NCCL_ALGO_TREE][NCCL_PROTO_SIMPLE], cost_table[NCCL_ALGO_TREE][NCCL_PROTO_SIMPLE]);
TEST_ASSERT(cost_table[NCCL_ALGO_TREE][NCCL_PROTO_SIMPLE] == 0.0, "Small: Tree/Simple should have low cost");
TEST_ASSERT(nChannels == 2, "Small: Should set 2 channels");
// Test medium message (should match second config)
for (int i = 0; i < NCCL_NUM_ALGORITHMS; i++) {
for (int j = 0; j < NCCL_NUM_PROTOCOLS; j++) {
cost_table[i][j] = 1.0;
}
}
pluginGetCollInfo(context, ncclFuncAllReduce, 32768, 1,
cost_table_ptr, NCCL_NUM_ALGORITHMS, NCCL_NUM_PROTOCOLS,
0, &nChannels);
mock_logger(NCCL_LOG_INFO, NCCL_ALL, __FILE__, __LINE__,
"DEBUG: Medium message - checking cost_table[RING][SIMPLE] (%p) = %.1f (expecting 0.0)",
&cost_table[NCCL_ALGO_RING][NCCL_PROTO_SIMPLE], cost_table[NCCL_ALGO_RING][NCCL_PROTO_SIMPLE]);
TEST_ASSERT(cost_table[NCCL_ALGO_RING][NCCL_PROTO_SIMPLE] == 0.0, "Medium: Ring/Simple should have low cost");
TEST_ASSERT(nChannels == 4, "Medium: Should set 4 channels");
// Test large message (should match third config)
for (int i = 0; i < NCCL_NUM_ALGORITHMS; i++) {
for (int j = 0; j < NCCL_NUM_PROTOCOLS; j++) {
cost_table[i][j] = 1.0;
}
}
pluginGetCollInfo(context, ncclFuncAllReduce, 1048576, 1,
cost_table_ptr, NCCL_NUM_ALGORITHMS, NCCL_NUM_PROTOCOLS,
0, &nChannels);
mock_logger(NCCL_LOG_INFO, NCCL_ALL, __FILE__, __LINE__,
"DEBUG: Large message - checking cost_table[RING][LL128] (%p) = %.1f (expecting 0.0)",
&cost_table[NCCL_ALGO_RING][NCCL_PROTO_LL128], cost_table[NCCL_ALGO_RING][NCCL_PROTO_LL128]);
TEST_ASSERT(cost_table[NCCL_ALGO_RING][NCCL_PROTO_LL128] == 0.0, "Large: Ring/LL128 should have low cost");
TEST_ASSERT(nChannels == 8, "Large: Should set 8 channels");
// Clean up
pluginDestroy(context);
unlink("test_size.conf");
unsetenv("NCCL_TUNER_CONFIG_FILE");
TEST_PASS();
}
// Test 6: Topology matching
int test_topology_matching() {
const char* test_config =
"allreduce,0,65536,tree,simple,2,1,-1,-1,-1\n" // Single node only
"allreduce,0,65536,ring,simple,4,4,32,-1,-1\n" // 4 nodes, 32 ranks exactly
"allreduce,0,65536,ring,ll128,8,-1,-1,-1,-1\n"; // Any topology
create_test_config("test_topo.conf", test_config);
setenv("NCCL_TUNER_CONFIG_FILE", "test_topo.conf", 1);
// Test with single node setup
void* context1 = NULL;
pluginInit(8, 1, mock_logger, &context1); // 8 ranks, 1 node
float cost_table[NCCL_NUM_ALGORITHMS][NCCL_NUM_PROTOCOLS];
float* cost_table_ptr[NCCL_NUM_ALGORITHMS];
for (int i = 0; i < NCCL_NUM_ALGORITHMS; i++) {
cost_table_ptr[i] = cost_table[i];
for (int j = 0; j < NCCL_NUM_PROTOCOLS; j++) {
cost_table[i][j] = 1.0;
}
}
int nChannels;
pluginGetCollInfo(context1, ncclFuncAllReduce, 32768, 1,
cost_table_ptr, NCCL_NUM_ALGORITHMS, NCCL_NUM_PROTOCOLS,
0, &nChannels);
TEST_ASSERT(cost_table[NCCL_ALGO_TREE][NCCL_PROTO_SIMPLE] == 0.0, "Single node: Should match tree config");
TEST_ASSERT(nChannels == 2, "Single node: Should set 2 channels");
pluginDestroy(context1);
// Test with 4 nodes, 32 ranks setup
void* context2 = NULL;
pluginInit(32, 4, mock_logger, &context2); // 32 ranks, 4 nodes
for (int i = 0; i < NCCL_NUM_ALGORITHMS; i++) {
for (int j = 0; j < NCCL_NUM_PROTOCOLS; j++) {
cost_table[i][j] = 1.0;
}
}
pluginGetCollInfo(context2, ncclFuncAllReduce, 32768, 1,
cost_table_ptr, NCCL_NUM_ALGORITHMS, NCCL_NUM_PROTOCOLS,
0, &nChannels);
TEST_ASSERT(cost_table[NCCL_ALGO_RING][NCCL_PROTO_SIMPLE] == 0.0, "4-node: Should match ring/simple config");
TEST_ASSERT(nChannels == 4, "4-node: Should set 4 channels");
// Clean up
unlink("test_topo.conf");
unsetenv("NCCL_TUNER_CONFIG_FILE");
TEST_PASS();
}
// Test 7: Default channels behavior (-1)
int test_default_channels() {
const char* test_config =
"allreduce,0,65536,tree,simple,-1,-1,-1,-1,-1\n"; // Use default channels
create_test_config("test_default.conf", test_config);
setenv("NCCL_TUNER_CONFIG_FILE", "test_default.conf", 1);
void* context = NULL;
pluginInit(8, 1, mock_logger, &context);
float cost_table[NCCL_NUM_ALGORITHMS][NCCL_NUM_PROTOCOLS];
float* cost_table_ptr[NCCL_NUM_ALGORITHMS];
for (int i = 0; i < NCCL_NUM_ALGORITHMS; i++) {
cost_table_ptr[i] = cost_table[i];
for (int j = 0; j < NCCL_NUM_PROTOCOLS; j++) {
cost_table[i][j] = 1.0;
}
}
int nChannels = 99; // Set to known value
pluginGetCollInfo(context, ncclFuncAllReduce, 32768, 1,
cost_table_ptr, NCCL_NUM_ALGORITHMS, NCCL_NUM_PROTOCOLS,
0, &nChannels);
TEST_ASSERT(cost_table[NCCL_ALGO_TREE][NCCL_PROTO_SIMPLE] == 0.0, "Should apply algorithm/protocol");
TEST_ASSERT(nChannels == 1, "Should keep default channels (1) when config has -1");
// Clean up
pluginDestroy(context);
unlink("test_default.conf");
unsetenv("NCCL_TUNER_CONFIG_FILE");
TEST_PASS();
}
// Test 8: regBuff matching
int test_regbuff_matching() {
const char* test_config =
"allreduce,0,65536,tree,simple,2,-1,-1,-1,1\n" // Registered buffers only
"allreduce,0,65536,ring,simple,4,-1,-1,-1,0\n" // Non-registered buffers only
"allreduce,0,65536,ring,ll128,8,-1,-1,-1,-1\n"; // Any buffer type (backward compatible)
create_test_config("test_regbuff.conf", test_config);
setenv("NCCL_TUNER_CONFIG_FILE", "test_regbuff.conf", 1);
void* context = NULL;
pluginInit(8, 1, mock_logger, &context);
float cost_table[NCCL_NUM_ALGORITHMS][NCCL_NUM_PROTOCOLS];
float* cost_table_ptr[NCCL_NUM_ALGORITHMS];
for (int i = 0; i < NCCL_NUM_ALGORITHMS; i++) {
cost_table_ptr[i] = cost_table[i];
}
int nChannels;
// Test registered buffer (should match first config)
for (int i = 0; i < NCCL_NUM_ALGORITHMS; i++) {
for (int j = 0; j < NCCL_NUM_PROTOCOLS; j++) {
cost_table[i][j] = 1.0;
}
}
pluginGetCollInfo(context, ncclFuncAllReduce, 32768, 1,
cost_table_ptr, NCCL_NUM_ALGORITHMS, NCCL_NUM_PROTOCOLS,
1, &nChannels); // regBuff = 1 (registered)
TEST_ASSERT(cost_table[NCCL_ALGO_TREE][NCCL_PROTO_SIMPLE] == 0.0, "Registered buffer: Tree/Simple should have low cost");
TEST_ASSERT(nChannels == 2, "Registered buffer: Should set 2 channels");
// Test non-registered buffer (should match second config)
for (int i = 0; i < NCCL_NUM_ALGORITHMS; i++) {
for (int j = 0; j < NCCL_NUM_PROTOCOLS; j++) {
cost_table[i][j] = 1.0;
}
}
pluginGetCollInfo(context, ncclFuncAllReduce, 32768, 1,
cost_table_ptr, NCCL_NUM_ALGORITHMS, NCCL_NUM_PROTOCOLS,
0, &nChannels); // regBuff = 0 (non-registered)
TEST_ASSERT(cost_table[NCCL_ALGO_RING][NCCL_PROTO_SIMPLE] == 0.0, "Non-registered buffer: Ring/Simple should have low cost");
TEST_ASSERT(nChannels == 4, "Non-registered buffer: Should set 4 channels");
// Test backward compatibility - config without regBuff should match any regBuff value
for (int i = 0; i < NCCL_NUM_ALGORITHMS; i++) {
for (int j = 0; j < NCCL_NUM_PROTOCOLS; j++) {
cost_table[i][j] = 1.0;
}
}
// First try with regBuff=2 (unusual value, should match third config)
pluginGetCollInfo(context, ncclFuncAllReduce, 32768, 1,
cost_table_ptr, NCCL_NUM_ALGORITHMS, NCCL_NUM_PROTOCOLS,
2, &nChannels); // regBuff = 2 (only third config should match)
TEST_ASSERT(cost_table[NCCL_ALGO_RING][NCCL_PROTO_LL128] == 0.0, "Any regBuff: Ring/LL128 should have low cost");
TEST_ASSERT(nChannels == 8, "Any regBuff: Should set 8 channels");
// Clean up
pluginDestroy(context);
unlink("test_regbuff.conf");
unsetenv("NCCL_TUNER_CONFIG_FILE");
TEST_PASS();
}
// Test 9: numPipeOps matching
int test_pipeops_matching() {
const char* test_config =
"allreduce,0,65536,tree,simple,2,-1,-1,1,-1\n" // Single pipeline op
"allreduce,0,65536,ring,simple,4,-1,-1,4,-1\n" // Multiple pipeline ops
"allreduce,0,65536,ring,ll128,8,-1,-1,-1,-1\n"; // Any pipeline ops (backward compatible)
create_test_config("test_pipeops.conf", test_config);
setenv("NCCL_TUNER_CONFIG_FILE", "test_pipeops.conf", 1);
void* context = NULL;
pluginInit(8, 1, mock_logger, &context);
float cost_table[NCCL_NUM_ALGORITHMS][NCCL_NUM_PROTOCOLS];
float* cost_table_ptr[NCCL_NUM_ALGORITHMS];
for (int i = 0; i < NCCL_NUM_ALGORITHMS; i++) {
cost_table_ptr[i] = cost_table[i];
}
int nChannels;
// Test single pipeline op (should match first config)
for (int i = 0; i < NCCL_NUM_ALGORITHMS; i++) {
for (int j = 0; j < NCCL_NUM_PROTOCOLS; j++) {
cost_table[i][j] = 1.0;
}
}
pluginGetCollInfo(context, ncclFuncAllReduce, 32768, 1,
cost_table_ptr, NCCL_NUM_ALGORITHMS, NCCL_NUM_PROTOCOLS,
0, &nChannels);
TEST_ASSERT(cost_table[NCCL_ALGO_TREE][NCCL_PROTO_SIMPLE] == 0.0, "Single pipeOp: Tree/Simple should have low cost");
TEST_ASSERT(nChannels == 2, "Single pipeOp: Should set 2 channels");
// Test multiple pipeline ops (should match second config)
for (int i = 0; i < NCCL_NUM_ALGORITHMS; i++) {
for (int j = 0; j < NCCL_NUM_PROTOCOLS; j++) {
cost_table[i][j] = 1.0;
}
}
pluginGetCollInfo(context, ncclFuncAllReduce, 32768, 4,
cost_table_ptr, NCCL_NUM_ALGORITHMS, NCCL_NUM_PROTOCOLS,
0, &nChannels);
TEST_ASSERT(cost_table[NCCL_ALGO_RING][NCCL_PROTO_SIMPLE] == 0.0, "Multiple pipeOps: Ring/Simple should have low cost");
TEST_ASSERT(nChannels == 4, "Multiple pipeOps: Should set 4 channels");
// Test different number of pipeline ops (should match third config - backward compatible)
for (int i = 0; i < NCCL_NUM_ALGORITHMS; i++) {
for (int j = 0; j < NCCL_NUM_PROTOCOLS; j++) {
cost_table[i][j] = 1.0;
}
}
pluginGetCollInfo(context, ncclFuncAllReduce, 32768, 2,
cost_table_ptr, NCCL_NUM_ALGORITHMS, NCCL_NUM_PROTOCOLS,
0, &nChannels);
TEST_ASSERT(cost_table[NCCL_ALGO_RING][NCCL_PROTO_LL128] == 0.0, "Any pipeOps: Ring/LL128 should have low cost");
TEST_ASSERT(nChannels == 8, "Any pipeOps: Should set 8 channels");
// Clean up
pluginDestroy(context);
unlink("test_pipeops.conf");
unsetenv("NCCL_TUNER_CONFIG_FILE");
TEST_PASS();
}
// Test 10: No matching configuration (fallback behavior)
int test_no_match_fallback() {
const char* test_config =
"broadcast,0,1024,tree,simple,2,-1,-1,-1,-1\n"; // Only broadcast config
create_test_config("test_fallback.conf", test_config);
setenv("NCCL_TUNER_CONFIG_FILE", "test_fallback.conf", 1);
void* context = NULL;
pluginInit(8, 1, mock_logger, &context);
float cost_table[NCCL_NUM_ALGORITHMS][NCCL_NUM_PROTOCOLS];
float* cost_table_ptr[NCCL_NUM_ALGORITHMS];
for (int i = 0; i < NCCL_NUM_ALGORITHMS; i++) {
cost_table_ptr[i] = cost_table[i];
for (int j = 0; j < NCCL_NUM_PROTOCOLS; j++) {
cost_table[i][j] = 1.0;
}
}
int nChannels;
// Try allreduce (should not match, use fallback)
pluginGetCollInfo(context, ncclFuncAllReduce, 32768, 1,
cost_table_ptr, NCCL_NUM_ALGORITHMS, NCCL_NUM_PROTOCOLS,
0, &nChannels);
mock_logger(NCCL_LOG_INFO, NCCL_ALL, __FILE__, __LINE__,
"DEBUG: Fallback test - checking cost_table[RING][SIMPLE] (%p) = %.1f (expecting 0.0)",
&cost_table[NCCL_ALGO_RING][NCCL_PROTO_SIMPLE], cost_table[NCCL_ALGO_RING][NCCL_PROTO_SIMPLE]);
TEST_ASSERT(cost_table[NCCL_ALGO_RING][NCCL_PROTO_SIMPLE] == 1.0, "Should use pass through unmodified");
TEST_ASSERT(nChannels == 1, "Should use default channels");
// Clean up
pluginDestroy(context);
unlink("test_fallback.conf");
unsetenv("NCCL_TUNER_CONFIG_FILE");
TEST_PASS();
}
// Test 11: Large configuration files (testing dynamic allocation)
int test_large_config() {
const char* large_config_file = "test_large.conf";
// Create a large configuration file with many entries
// This tests the dynamic allocation functionality
FILE* f = fopen(large_config_file, "w");
TEST_ASSERT(f != NULL, "Should be able to create large config file");
// Write header comment
fprintf(f, "# Large configuration file for testing dynamic allocation\n");
fprintf(f, "# This file contains many configurations to test memory allocation\n");
// Generate a large number of configurations (much more than the old MAX_CONFIGS=100)
const int num_configs = 500; // 5x the old static limit
const char* collectives[] = {"allreduce", "broadcast", "reduce", "allgather", "reducescatter"};
const char* algorithms[] = {"tree", "ring", "collnet_direct", "nvls"};
const char* protocols[] = {"simple", "ll", "ll128"};
for (int i = 0; i < num_configs; i++) {
// Vary the configurations to create realistic test data
const char* coll = collectives[i % 5];
const char* algo = algorithms[i % 4];
const char* proto = protocols[i % 3];
size_t min_bytes = (i * 1024) % 1048576; // Vary from 0 to 1MB
size_t max_bytes = min_bytes + 65536; // 64KB range
int channels = (i % 8) + 1; // 1-8 channels
int nodes = (i % 4) == 0 ? -1 : (i % 4); // Mix of -1 and 1-3 nodes
int ranks = (i % 8) == 0 ? -1 : (i % 32) + 1; // Mix of -1 and 1-32 ranks
int pipeOps = (i % 3) == 0 ? -1 : (i % 4) + 1; // Mix of -1 and 1-4 pipeOps
int regBuff = (i % 3) == 0 ? -1 : (i % 2); // Mix of -1, 0, 1
fprintf(f, "%s,%zu,%zu,%s,%s,%d,%d,%d,%d,%d\n",
coll, min_bytes, max_bytes, algo, proto, channels, nodes, ranks, pipeOps, regBuff);
}
fclose(f);
// Set environment to use our large config file
setenv("NCCL_TUNER_CONFIG_FILE", large_config_file, 1);
// Initialize plugin with large config
void* context = NULL;
ncclResult_t result = pluginInit(16, 4, mock_logger, &context);
TEST_ASSERT(result == ncclSuccess, "Plugin init with large config should succeed");
TEST_ASSERT(context != NULL, "Context should be allocated");
// Verify that configurations were loaded
TunerContext* ctx = (TunerContext*)context;
TEST_ASSERT(ctx->numConfigs == num_configs, "Should load all configurations from large file");
TEST_ASSERT(ctx->maxConfigs == num_configs, "maxConfigs should match allocated size");
TEST_ASSERT(ctx->configs != NULL, "Configs array should be dynamically allocated");
// Test that we can access configurations throughout the array
// (This would have failed with the old static MAX_CONFIGS=100 limit)
for (int i = 0; i < ctx->numConfigs; i++) {
TuningConfig* config = &ctx->configs[i];
// Basic sanity checks on the loaded configurations
TEST_ASSERT(config->collType >= ncclFuncBroadcast && config->collType <= ncclFuncAllReduce,
"Collective type should be valid");
TEST_ASSERT(config->maxBytes >= config->minBytes, "maxBytes should be >= minBytes");
TEST_ASSERT(config->nChannels > 0, "nChannels should be positive");
}
// Test specific configuration access at various indices
// Index 0 (first config)
TuningConfig* first_config = &ctx->configs[0];
TEST_ASSERT(first_config != NULL, "First config should be accessible");
// Index in middle
TuningConfig* mid_config = &ctx->configs[num_configs / 2];
TEST_ASSERT(mid_config != NULL, "Middle config should be accessible");
// Index near end (this would have crashed with static array of 100)
TuningConfig* late_config = &ctx->configs[num_configs - 1];
TEST_ASSERT(late_config != NULL, "Last config should be accessible");
// Test memory allocation size - verify we didn't over-allocate
mock_logger(NCCL_LOG_INFO, NCCL_ALL, __FILE__, __LINE__,
"Successfully loaded %d configurations (dynamic allocation)", ctx->numConfigs);
mock_logger(NCCL_LOG_INFO, NCCL_ALL, __FILE__, __LINE__,
"Memory allocated for %d configurations (%zu bytes total)",
ctx->maxConfigs, ctx->maxConfigs * sizeof(TuningConfig));
// Test that the plugin can still find matching configurations from the large set
float cost_table[NCCL_NUM_ALGORITHMS][NCCL_NUM_PROTOCOLS];
float* cost_table_ptr[NCCL_NUM_ALGORITHMS];
for (int i = 0; i < NCCL_NUM_ALGORITHMS; i++) {
cost_table_ptr[i] = cost_table[i];
for (int j = 0; j < NCCL_NUM_PROTOCOLS; j++) {
cost_table[i][j] = 1.0; // Default high cost
}
}
int nChannels;
// Try to find a matching configuration - should work with large config set
result = pluginGetCollInfo(context, ncclFuncAllReduce, 32768, 1,
cost_table_ptr, NCCL_NUM_ALGORITHMS, NCCL_NUM_PROTOCOLS,
0, &nChannels);
TEST_ASSERT(result == ncclSuccess, "GetCollInfo should work with large config set");
// Clean up
pluginDestroy(context);
unlink(large_config_file);
unsetenv("NCCL_TUNER_CONFIG_FILE");
TEST_PASS();
}
// Test 12: Very large configuration stress test
int test_very_large_config_stress() {
const char* stress_config_file = "test_stress.conf";
// Create an even larger configuration file to stress test the implementation
FILE* f = fopen(stress_config_file, "w");
TEST_ASSERT(f != NULL, "Should be able to create stress test config file");
fprintf(f, "# Stress test configuration with very large number of entries\n");
// Generate an extremely large number of configurations
const int stress_configs = 2000; // 20x the old static limit
for (int i = 0; i < stress_configs; i++) {
// Create varied but valid configurations
fprintf(f, "allreduce,%d,%d,ring,simple,4,-1,-1,-1,-1\n",
i * 512, (i * 512) + 1024);
}
fclose(f);
setenv("NCCL_TUNER_CONFIG_FILE", stress_config_file, 1);
// Test initialization with stress config
void* context = NULL;
ncclResult_t result = pluginInit(8, 2, mock_logger, &context);
TEST_ASSERT(result == ncclSuccess, "Plugin should handle very large config files");
TunerContext* ctx = (TunerContext*)context;
TEST_ASSERT(ctx->numConfigs == stress_configs, "Should load all stress test configurations");
TEST_ASSERT(ctx->configs != NULL, "Stress test configs should be allocated");
mock_logger(NCCL_LOG_INFO, NCCL_ALL, __FILE__, __LINE__,
"Stress test - loaded %d configurations successfully", stress_configs);
mock_logger(NCCL_LOG_INFO, NCCL_ALL, __FILE__, __LINE__,
"Memory usage: %zu bytes for configuration array",
stress_configs * sizeof(TuningConfig));
// Verify we can access configurations throughout the entire range
for (int i = 0; i < stress_configs; i += 100) { // Sample every 100th config
TuningConfig* config = &ctx->configs[i];
TEST_ASSERT(config->collType == ncclFuncAllReduce, "Config should have correct collective type");
TEST_ASSERT(config->minBytes == (size_t)(i * 512), "Config should have correct minBytes");
}
// Clean up
pluginDestroy(context);
unlink(stress_config_file);
unsetenv("NCCL_TUNER_CONFIG_FILE");
TEST_PASS();
}
// Test 13: Edge case - empty config file
int test_empty_config() {
const char* empty_config_file = "test_empty.conf";
// Create empty config file (only comments)
create_test_config(empty_config_file,
"# Empty configuration file\n"
"# No actual configurations\n"
"\n"
"\n");
setenv("NCCL_TUNER_CONFIG_FILE", empty_config_file, 1);
void* context = NULL;
ncclResult_t result = pluginInit(8, 2, mock_logger, &context);
TEST_ASSERT(result == ncclSuccess, "Plugin should handle empty config files");
TunerContext* ctx = (TunerContext*)context;
TEST_ASSERT(ctx->numConfigs == 0, "Should have zero configurations");
TEST_ASSERT(ctx->maxConfigs == 0, "Should have zero max configurations");
TEST_ASSERT(ctx->configs == NULL, "Should not allocate memory for empty config");
// Test that plugin still works with no configurations (fallback behavior)
float cost_table[NCCL_NUM_ALGORITHMS][NCCL_NUM_PROTOCOLS];
float* cost_table_ptr[NCCL_NUM_ALGORITHMS];
for (int i = 0; i < NCCL_NUM_ALGORITHMS; i++) {
cost_table_ptr[i] = cost_table[i];
for (int j = 0; j < NCCL_NUM_PROTOCOLS; j++) {
cost_table[i][j] = 1.0;
}
}
int nChannels;
result = pluginGetCollInfo(context, ncclFuncAllReduce, 32768, 1,
cost_table_ptr, NCCL_NUM_ALGORITHMS, NCCL_NUM_PROTOCOLS,
0, &nChannels);
TEST_ASSERT(result == ncclSuccess, "GetCollInfo should work with empty config");
// Clean up
pluginDestroy(context);
unlink(empty_config_file);
unsetenv("NCCL_TUNER_CONFIG_FILE");
TEST_PASS();
}
// Test runner function pointer type
typedef int (*TestFunction)(void);
// Test registry
typedef struct {
const char* name;
TestFunction func;
const char* description;
} TestCase;
// All available tests
TestCase test_cases[] = {
{"init", test_plugin_init, "Plugin initialization"},
{"config-valid", test_config_parsing_valid, "Valid configuration parsing"},
{"config-invalid", test_config_parsing_invalid, "Invalid configuration parsing"},
{"collective", test_collective_matching, "Collective type matching"},
{"size", test_size_matching, "Size range matching"},
{"topology", test_topology_matching, "Topology matching"},
{"channels", test_default_channels, "Default channels behavior"},
{"regbuff", test_regbuff_matching, "Registered buffer matching"},
{"pipeops", test_pipeops_matching, "Pipeline operations matching"},
{"fallback", test_no_match_fallback, "Fallback behavior"},
{"large-config", test_large_config, "Large configuration files (dynamic allocation)"},
{"stress-config", test_very_large_config_stress, "Very large configuration stress test"},
{"empty-config", test_empty_config, "Empty configuration file handling"},
{NULL, NULL, NULL} // End marker
};
// Show help/usage information
void show_help(const char* program_name) {
printf("Usage: %s [test_name ...]\n\n", program_name);
printf("Available tests:\n");
for (int i = 0; test_cases[i].name != NULL; i++) {
printf(" %-15s - %s\n", test_cases[i].name, test_cases[i].description);
}
printf("\nExamples:\n");
printf(" %s # Run all tests\n", program_name);
printf(" %s init # Run only initialization test\n", program_name);
printf(" %s init collective # Run initialization and collective tests\n", program_name);
printf(" %s --help # Show this help\n", program_name);
}
// Find test by name
TestFunction find_test(const char* name) {
for (int i = 0; test_cases[i].name != NULL; i++) {
if (strcmp(test_cases[i].name, name) == 0) {
return test_cases[i].func;
}
}
return NULL;
}
// Main test runner
int main(int argc, char* argv[]) {
int passed = 0, total = 0;
// Check for help
if (argc > 1 && (strcmp(argv[1], "--help") == 0 || strcmp(argv[1], "-h") == 0)) {
show_help(argv[0]);
return 0;
}
printf("Running NCCL Tuner Plugin Unit Tests\n");
printf("=====================================\n");
if (argc == 1) {
// No arguments - run all tests
for (int i = 0; test_cases[i].name != NULL; i++) {
total++;
passed += test_cases[i].func();
}
} else {
// Run specific tests
for (int arg = 1; arg < argc; arg++) {
TestFunction test_func = find_test(argv[arg]);
if (test_func) {
total++;
passed += test_func();
} else {
printf("ERROR: Unknown test '%s'\n", argv[arg]);
printf("Use --help to see available tests\n");
return 1;
}
}
}
printf("\n=====================================\n");
printf("Test Results: %d/%d tests passed\n", passed, total);
if (passed == total) {
printf("All tests PASSED!\n");
return 0;
} else {
printf("Some tests FAILED!\n");
return 1;
}
}

View File

@ -1,165 +0,0 @@
#
# Copyright (c) 2015-2022, NVIDIA CORPORATION. All rights reserved.
#
# See LICENSE.txt for license information
#
CUDA_HOME ?= /usr/local/cuda
PREFIX ?= /usr/local
VERBOSE ?= 0
KEEP ?= 0
DEBUG ?= 0
ASAN ?= 0
UBSAN ?= 0
TRACE ?= 0
WERROR ?= 0
PROFAPI ?= 1
NVTX ?= 1
RDMA_CORE ?= 0
NET_PROFILER ?= 0
MLX5DV ?= 0
MAX_EXT_NET_PLUGINS ?= 0
NVCC = $(CUDA_HOME)/bin/nvcc
CUDA_LIB ?= $(CUDA_HOME)/lib64
CUDA_INC ?= $(CUDA_HOME)/include
CUDA_VERSION = $(strip $(shell which $(NVCC) >/dev/null && $(NVCC) --version | grep release | sed 's/.*release //' | sed 's/\,.*//'))
#CUDA_VERSION ?= $(shell ls $(CUDA_LIB)/libcudart.so.* | head -1 | rev | cut -d "." -f -2 | rev)
CUDA_MAJOR = $(shell echo $(CUDA_VERSION) | cut -d "." -f 1)
CUDA_MINOR = $(shell echo $(CUDA_VERSION) | cut -d "." -f 2)
#$(info CUDA_VERSION ${CUDA_MAJOR}.${CUDA_MINOR})
# You should define NVCC_GENCODE in your environment to the minimal set
# of archs to reduce compile time.
CUDA8_GENCODE = -gencode=arch=compute_50,code=sm_50 \
-gencode=arch=compute_60,code=sm_60 \
-gencode=arch=compute_61,code=sm_61
ifeq ($(shell test "0$(CUDA_MAJOR)" -lt 12; echo $$?),0)
# SM35 is deprecated from CUDA12.0 onwards
CUDA8_GENCODE += -gencode=arch=compute_35,code=sm_35
endif
CUDA9_GENCODE = -gencode=arch=compute_70,code=sm_70
CUDA10_GENCODE = -gencode=arch=compute_75,code=sm_75
CUDA11_GENCODE = -gencode=arch=compute_80,code=sm_80
CUDA12_GENCODE = -gencode=arch=compute_90,code=sm_90
CUDA12_8_GENCODE = -gencode=arch=compute_100,code=sm_100 \
-gencode=arch=compute_120,code=sm_120
CUDA13_GENCODE = -gencode=arch=compute_110,code=sm_110
CUDA8_PTX = -gencode=arch=compute_61,code=compute_61
CUDA9_PTX = -gencode=arch=compute_70,code=compute_70
CUDA11_PTX = -gencode=arch=compute_80,code=compute_80
CUDA12_PTX = -gencode=arch=compute_90,code=compute_90
CUDA13_PTX = -gencode=arch=compute_120,code=compute_120
ifeq ($(shell test "0$(CUDA_MAJOR)" -ge 13; echo $$?),0)
# Prior to SM75 is deprecated from CUDA13.0 onwards
NVCC_GENCODE ?= $(CUDA10_GENCODE) $(CUDA11_GENCODE) $(CUDA12_GENCODE) $(CUDA12_8_GENCODE) $(CUDA13_GENCODE) $(CUDA13_PTX)
else ifeq ($(shell test "0$(CUDA_MAJOR)" -eq 12 -a "0$(CUDA_MINOR)" -ge 8; echo $$?),0)
# Include Blackwell support if we're using CUDA12.8 or above
NVCC_GENCODE ?= $(CUDA8_GENCODE) $(CUDA9_GENCODE) $(CUDA11_GENCODE) $(CUDA12_GENCODE) $(CUDA12_8_GENCODE) $(CUDA13_PTX)
else ifeq ($(shell test "0$(CUDA_MAJOR)" -eq 11 -a "0$(CUDA_MINOR)" -ge 8 -o "0$(CUDA_MAJOR)" -gt 11; echo $$?),0)
# Include Hopper support if we're using CUDA11.8 or above
NVCC_GENCODE ?= $(CUDA8_GENCODE) $(CUDA9_GENCODE) $(CUDA11_GENCODE) $(CUDA12_GENCODE) $(CUDA12_PTX)
else ifeq ($(shell test "0$(CUDA_MAJOR)" -ge 11; echo $$?),0)
NVCC_GENCODE ?= $(CUDA8_GENCODE) $(CUDA9_GENCODE) $(CUDA11_GENCODE) $(CUDA11_PTX)
# Include Volta support if we're using CUDA9 or above
else ifeq ($(shell test "0$(CUDA_MAJOR)" -ge 9; echo $$?),0)
NVCC_GENCODE ?= $(CUDA8_GENCODE) $(CUDA9_GENCODE) $(CUDA9_PTX)
else
NVCC_GENCODE ?= $(CUDA8_GENCODE) $(CUDA8_PTX)
endif
$(info NVCC_GENCODE is ${NVCC_GENCODE})
# CUDA 13.0 requires c++17
ifeq ($(shell test "0$(CUDA_MAJOR)" -ge 13; echo $$?),0)
CXXSTD ?= -std=c++17
else
CXXSTD ?= -std=c++11
endif
CXXFLAGS := -DCUDA_MAJOR=$(CUDA_MAJOR) -DCUDA_MINOR=$(CUDA_MINOR) -fPIC -fvisibility=hidden \
-Wall -Wno-unused-function -Wno-sign-compare $(CXXSTD) -Wvla \
-I $(CUDA_INC) -I $(CUDA_INC)/cccl \
$(CXXFLAGS)
# Maxrregcount needs to be set accordingly to NCCL_MAX_NTHREADS (otherwise it will cause kernel launch errors)
# 512 : 120, 640 : 96, 768 : 80, 1024 : 60
# We would not have to set this if we used __launch_bounds__, but this only works on kernels, not on functions.
NVCUFLAGS := -ccbin $(CXX) $(NVCC_GENCODE) $(CXXSTD) --expt-extended-lambda -Xptxas -maxrregcount=96 -Xfatbin -compress-all
# Use addprefix so that we can specify more than one path
NVLDFLAGS := -L${CUDA_LIB} -lcudart -lrt
########## GCOV ##########
GCOV ?= 0 # disable by default.
GCOV_FLAGS := $(if $(filter 0,${GCOV} ${DEBUG}),,--coverage) # only gcov=1 and debug =1
CXXFLAGS += ${GCOV_FLAGS}
NVCUFLAGS += ${GCOV_FLAGS:%=-Xcompiler %}
LDFLAGS += ${GCOV_FLAGS}
NVLDFLAGS += ${GCOV_FLAGS:%=-Xcompiler %}
# $(warning GCOV_FLAGS=${GCOV_FLAGS})
########## GCOV ##########
ifeq ($(DEBUG), 0)
NVCUFLAGS += -O3
CXXFLAGS += -O3 -g
else
NVCUFLAGS += -O0 -G -g
CXXFLAGS += -O0 -g -ggdb3
endif
# Make sure to run with ASAN_OPTIONS=protect_shadow_gap=0 otherwise CUDA will fail with OOM
ifneq ($(ASAN), 0)
CXXFLAGS += -fsanitize=address
LDFLAGS += -fsanitize=address -static-libasan
NVLDFLAGS += -Xcompiler -fsanitize=address,-static-libasan
endif
ifneq ($(UBSAN), 0)
CXXFLAGS += -fsanitize=undefined
LDFLAGS += -fsanitize=undefined -static-libubsan
NVLDFLAGS += -Xcompiler -fsanitize=undefined,-static-libubsan
endif
ifneq ($(VERBOSE), 0)
NVCUFLAGS += -Xptxas -v -Xcompiler -Wall,-Wextra,-Wno-unused-parameter
CXXFLAGS += -Wall -Wextra
else
.SILENT:
endif
ifneq ($(TRACE), 0)
CXXFLAGS += -DENABLE_TRACE
endif
ifeq ($(NVTX), 0)
CXXFLAGS += -DNVTX_DISABLE
endif
ifneq ($(WERROR), 0)
CXXFLAGS += -Werror
endif
ifneq ($(KEEP), 0)
NVCUFLAGS += -keep
endif
ifneq ($(PROFAPI), 0)
CXXFLAGS += -DPROFAPI
endif
ifneq ($(RDMA_CORE), 0)
CXXFLAGS += -DNCCL_BUILD_RDMA_CORE=1 -libverbs
endif
ifneq ($(MLX5DV), 0)
CXXFLAGS += -DNCCL_BUILD_MLX5DV=1 -lmlx5
endif
ifneq ($(NET_PROFILER), 0)
CXXFLAGS += -DNCCL_ENABLE_NET_PROFILING=1
endif
ifneq ($(MAX_EXT_NET_PLUGINS), 0)
CXXFLAGS += -DNCCL_NET_MAX_PLUGINS=$(MAX_EXT_NET_PLUGINS)
endif

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@ -1,33 +0,0 @@
#
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# See LICENSE.txt for license information
#
# Prerequisite: $(FILESTOFORMAT) contains the list of files of interest for formatting
# As this file defines a new target (format), it should be included at least after the definition of the
# default target.
ASTYLE_FORMAT_OPTS=-Qv --style=java --indent-after-parens --indent-modifiers --indent-switches --indent-continuation=2 --keep-one-line-blocks --keep-one-line-statements --indent=spaces=2 --lineend=linux --suffix=none
ASTYLEDIR := $(BUILDDIR)/contrib
ASTYLETAR := $(ASTYLEDIR)/astyle.tar.gz
ASTYLEBIN := $(ASTYLEDIR)/astyle/build/gcc/bin/astyle
ASTYLEBLD := $(ASTYLEDIR)/astyle/build/gcc/
ASTYLEVER := 3.1
ASTYLEURL := "https://versaweb.dl.sourceforge.net/project/astyle/astyle/astyle%20$(ASTYLEVER)/astyle_$(ASTYLEVER)_linux.tar.gz"
$(ASTYLEDIR) :
@mkdir -p $(ASTYLEDIR)
$(ASTYLETAR) : $(ASTYLEDIR)
@wget -q -O $(ASTYLETAR) $(ASTYLEURL)
$(ASTYLEBLD) : $(ASTYLETAR)
@cd $(ASTYLEDIR) && tar xzf $(ASTYLETAR)
$(ASTYLEBIN) : $(ASTYLEBLD)
${MAKE} -C $(ASTYLEBLD)
.PHONY : format
format : $(ASTYLEBIN)
@$(ASTYLEBIN) $(ASTYLE_FORMAT_OPTS) $(FILESTOFORMAT)

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@ -1,6 +0,0 @@
##### version
NCCL_MAJOR := 2
NCCL_MINOR := 27
NCCL_PATCH := 5
NCCL_SUFFIX :=
PKG_REVISION := 1

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@ -1,26 +0,0 @@
#
# Copyright (c) 2015-2019, NVIDIA CORPORATION. All rights reserved.
#
# See LICENSE.txt for license information
#
.PHONY : all clean
default : build
build : debian.build txz.build
BUILDDIR ?= $(abspath ../build)
ABSBUILDDIR := $(abspath $(BUILDDIR))
TARGETS := debian txz
all: ${TARGETS:%=%.build}
prep: ${TARGETS:%=%.prep}
build: ${TARGETS:%=%.build}
clean: ${TARGETS:%=%.clean}
%.prep:
${MAKE} -C $* prep BUILDDIR=${ABSBUILDDIR}
%.build:
${MAKE} -C $* build BUILDDIR=${ABSBUILDDIR}
%.clean:
${MAKE} -C $* clean

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@ -1,53 +0,0 @@
#
# Copyright (c) 2015-2019, NVIDIA CORPORATION. All rights reserved.
#
# See LICENSE.txt for license information
#
include ../../makefiles/common.mk
include ../../makefiles/version.mk
BUILDDIR ?= $(abspath ../../build)
DEBPREPDIR := $(BUILDDIR)/debian
PKGDIR := $(BUILDDIR)/pkg/deb/
DEBGEN_IN := $(wildcard *.in)
DEBGEN := $(DEBGEN_IN:.in=)
DEBFILES := compat copyright libnccl-dev.install rules $(DEBGEN)
DEBTARGETS := $(patsubst %, $(DEBPREPDIR)/%, $(DEBFILES))
PKG_TIMESTAMP := $(shell date -R)
PKG_ARCH ?= $(shell dpkg-architecture -qDEB_HOST_ARCH)
PKG_MULTIARCH ?= $(shell dpkg-architecture -qDEB_HOST_MULTIARCH)
prep : $(DEBTARGETS)
$(MAKE) -C ../.. lic BUILDDIR=$(BUILDDIR)
build : prep
$(MAKE) -C ../.. src.build BUILDDIR=$(BUILDDIR)
@printf "Building Debian package\n"
(cd $(BUILDDIR); debuild -eLD_LIBRARY_PATH -uc -us -d -b -Zxz)
mkdir -p $(PKGDIR)
mv $(BUILDDIR)/../libnccl*.deb $(PKGDIR)/
clean:
rm -Rf $(DEBPREPDIR) $(PKGDIR)
$(DEBPREPDIR)/% : %.in
@printf "Generating %-35s > %s\n" $< $@
mkdir -p $(DEBPREPDIR)
sed -e "s/\$${nccl:Major}/$(NCCL_MAJOR)/g" \
-e "s/\$${nccl:Minor}/$(NCCL_MINOR)/g" \
-e "s/\$${nccl:Patch}/$(NCCL_PATCH)/g" \
-e "s/\$${nccl:Suffix}/$(NCCL_SUFFIX)/g" \
-e "s/\$${cuda:Major}/$(CUDA_MAJOR)/g" \
-e "s/\$${cuda:Minor}/$(CUDA_MINOR)/g" \
-e "s/\$${pkg:Revision}/$(PKG_REVISION)/g" \
-e "s/\$${pkg:Timestamp}/$(PKG_TIMESTAMP)/g" \
-e "s/\$${pkg:Arch}/$(PKG_ARCH)/g" \
-e "s/\$${pkg:MultiArch}/$(PKG_MULTIARCH)/g" \
$< > $@
$(DEBPREPDIR)/% : %
@printf "Grabbing %-35s > %s\n" $< $@
mkdir -p $(DEBPREPDIR)
cp -f $< $@

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@ -1,5 +0,0 @@
nccl (${nccl:Major}.${nccl:Minor}.${nccl:Patch}${nccl:Suffix}-${pkg:Revision}+cuda${cuda:Major}.${cuda:Minor}) trusty; urgency=medium
* Automatic Debian package from build
-- cudatools <cudatools@nvidia.com> ${pkg:Timestamp}

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@ -1,30 +0,0 @@
Source: nccl
Section: libs
Maintainer: cudatools <cudatools@nvidia.com>
Priority: optional
Build-depends: debhelper(>=9)
Standards-Version: 3.9.5
Package: libnccl${nccl:Major}
Section: libs
Architecture: ${pkg:Arch}
Depends: ${misc:Depends}, ${shlibs:Depends}
Description: NVIDIA Collective Communication Library (NCCL) Runtime
NCCL (pronounced "Nickel") is a stand-alone library of standard collective
communication routines for GPUs, implementing all-reduce, all-gather, reduce,
broadcast, and reduce-scatter.
It has been optimized to achieve high bandwidth on any platform using PCIe,
NVLink, NVswitch, as well as networking using InfiniBand Verbs or TCP/IP
sockets.
Package: libnccl-dev
Section: libdevel
Architecture: ${pkg:Arch}
Depends: ${misc:Depends}, ${shlibs:Depends}, libnccl${nccl:Major} (= ${binary:Version})
Description: NVIDIA Collective Communication Library (NCCL) Development Files
NCCL (pronounced "Nickel") is a stand-alone library of standard collective
communication routines for GPUs, implementing all-reduce, all-gather, reduce,
broadcast, and reduce-scatter.
It has been optimized to achieve high bandwidth on any platform using PCIe,
NVLink, NVswitch, as well as networking using InfiniBand Verbs or TCP/IP
sockets.

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@ -1 +0,0 @@
../../LICENSE.txt

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@ -1,9 +0,0 @@
[DEFAULT]
debian-branch = master
upstream-branch = master
ignore-new = True
[git-buildpackage]
no-purge = True

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@ -1,4 +0,0 @@
bin/ncclras /usr/bin
include/nccl.h /usr/include
lib/libnccl.so /usr/lib/${pkg:MultiArch}
lib/libnccl_static.a /usr/lib/${pkg:MultiArch}

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@ -1,2 +0,0 @@
lib/libnccl.so.${nccl:Major} /usr/lib/${pkg:MultiArch}
lib/libnccl.so.${nccl:Major}.${nccl:Minor}.${nccl:Patch} /usr/lib/${pkg:MultiArch}

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@ -1,62 +0,0 @@
#
# Copyright (c) 2015-2019, NVIDIA CORPORATION. All rights reserved.
#
# See LICENSE.txt for license information
#
include ../../makefiles/common.mk
include ../../makefiles/version.mk
BUILDDIR ?= $(abspath ../../build)
RPMPREPDIR := $(BUILDDIR)/redhat
PKGDIR := $(BUILDDIR)/pkg/rpm/
RPMGEN_IN := $(wildcard *.in)
RPMGEN := $(RPMGEN_IN:.in=)
RPMFILES := $(RPMGEN)
RPMTARGETS := $(patsubst %, $(RPMPREPDIR)/%, $(RPMFILES))
PKG_TIMESTAMP := $(shell date -R)
ARCH := $(shell uname -m)
PKG_ARCH ?= $(shell uname -m)
PKG_MULTIARCH ?= $(shell $(CXX) -print-multiarch)
ifeq ($(PKG_MULTIARCH),)
# Hardwire the PKG_MULTIARCH directory as the RHEL6 distribution agnostic compiler (gcc 4.8.3) doesn't set it
PKG_MULTIARCH := $(ARCH)-linux-gnu
endif
prep : $(RPMTARGETS)
$(MAKE) -C ../.. lic BUILDDIR=$(BUILDDIR)
build : prep
$(MAKE) -C ../.. src.build BUILDDIR=$(BUILDDIR)
$(MAKE) -C ../txz build BUILDDIR=$(BUILDDIR)
@printf "Building Redhat package\n"
mkdir -p $(PKGDIR)
rpmbuild --define "_sourcedir $(BUILDDIR)/pkg/txz" \
--define "_rpmdir $(PKGDIR)" \
--define "_builddir $(PKGDIR)/build/" \
--define "_buildrootdir $(PKGDIR)/buildroot/" \
-bb $(BUILDDIR)/redhat/nccl.spec
clean:
rm -Rf $(RPMPREPDIR) $(PKGDIR)
$(RPMPREPDIR)/% : %.in
@printf "Generating %-35s > %s\n" $< $@
mkdir -p $(RPMPREPDIR)
sed -e "s/\$${nccl:Major}/$(NCCL_MAJOR)/g" \
-e "s/\$${nccl:Minor}/$(NCCL_MINOR)/g" \
-e "s/\$${nccl:Patch}/$(NCCL_PATCH)/g" \
-e "s/\$${nccl:Suffix}/$(NCCL_SUFFIX)/g" \
-e "s/\$${cuda:Major}/$(CUDA_MAJOR)/g" \
-e "s/\$${cuda:Minor}/$(CUDA_MINOR)/g" \
-e "s/\$${pkg:Revision}/$(PKG_REVISION)/g" \
-e "s/\$${pkg:Timestamp}/$(PKG_TIMESTAMP)/g" \
-e "s/\$${pkg:Arch}/$(PKG_ARCH)/g" \
-e "s/\$${pkg:MultiArch}/$(PKG_MULTIARCH)/g" \
$< > $@
$(RPMPREPDIR)/% : %
@printf "Grabbing %-35s > %s\n" $< $@
mkdir -p $(RPMPREPDIR)
cp -f $< $@

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@ -1,84 +0,0 @@
Name: libnccl
Version: ${nccl:Major}.${nccl:Minor}.${nccl:Patch}${nccl:Suffix}
Release: ${pkg:Revision}+cuda${cuda:Major}.${cuda:Minor}
Summary: NVIDIA Collective Communication Library (NCCL) Runtime
Group: Development/Libraries
License: BSD
URL: http://developer.nvidia.com/nccl
Source0: nccl_${nccl:Major}.${nccl:Minor}.${nccl:Patch}${nccl:Suffix}-${pkg:Revision}+cuda${cuda:Major}.${cuda:Minor}_${pkg:Arch}.txz
Requires(pre,preun): /sbin/ldconfig
%description
NCCL (pronounced "Nickel") is a stand-alone library of standard collective
communication routines for GPUs, implementing all-reduce, all-gather, reduce,
broadcast, and reduce-scatter.
It has been optimized to achieve high bandwidth on any platform using PCIe,
NVLink, NVswitch, as well as networking using InfiniBand Verbs or TCP/IP
sockets.
%package devel
Summary: NVIDIA Collective Communication Library (NCCL) Runtime
Group: Development/Libraries
Requires: libnccl >= ${nccl:Major}.${nccl:Minor}.${nccl:Patch}
%description devel
NCCL development files
%package static
Summary: NVIDIA Collective Communication Library (NCCL) Runtime
Group: Development/Libraries
%description static
NCCL static library
%define debug_package %{nil}
%prep
%setup -n nccl_${nccl:Major}.${nccl:Minor}.${nccl:Patch}${nccl:Suffix}-${pkg:Revision}+cuda${cuda:Major}.${cuda:Minor}_${pkg:Arch} -q
%build
%install
rm -rf $RPM_BUILD_ROOT
install -m 755 -d $RPM_BUILD_ROOT
install -m 755 -d $RPM_BUILD_ROOT/%{_libdir}
install -m 755 lib/libnccl.so.${nccl:Major}.${nccl:Minor}.${nccl:Patch} $RPM_BUILD_ROOT/%{_libdir}
ln -s libnccl.so.${nccl:Major}.${nccl:Minor}.${nccl:Patch} $RPM_BUILD_ROOT/%{_libdir}/libnccl.so.${nccl:Major}
# devel
install -m 755 -d $RPM_BUILD_ROOT/%{_bindir}
install -m 755 -d $RPM_BUILD_ROOT/%{_includedir}
install -m 755 bin/ncclras $RPM_BUILD_ROOT/%{_bindir}
install -m 644 include/nccl.h $RPM_BUILD_ROOT/%{_includedir}
ln -s libnccl.so.${nccl:Major} $RPM_BUILD_ROOT/%{_libdir}/libnccl.so
# static
install -m 644 lib/libnccl_static.a $RPM_BUILD_ROOT/%{_libdir}
%post -p /sbin/ldconfig
%postun -p /sbin/ldconfig
%post devel -p /sbin/ldconfig
%postun devel -p /sbin/ldconfig
%clean
rm -rf $RPM_BUILD_ROOT
%files devel
%doc LICENSE.txt
%defattr(-,root,root,-)
%{_bindir}/ncclras
%{_includedir}/nccl.h
%{_libdir}/libnccl.so
%files static
%doc LICENSE.txt
%defattr(-,root,root,-)
%{_libdir}/libnccl_static.a
%files
%doc LICENSE.txt
%defattr(-,root,root,-)
%{_libdir}/libnccl.so.${nccl:Major}
%{_libdir}/libnccl.so.${nccl:Major}.${nccl:Minor}.${nccl:Patch}
%changelog

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@ -1,40 +0,0 @@
#
# Copyright (c) 2015-2019, NVIDIA CORPORATION. All rights reserved.
#
# See LICENSE.txt for license information
#
include ../../makefiles/common.mk
include ../../makefiles/version.mk
BUILDDIR ?= $(abspath ../../build)
TXZPREPDIR := $(BUILDDIR)/srctxz
PKGDIR := $(BUILDDIR)/pkg/srctxz/
TXZGEN_IN := $(wildcard *.in)
TXZGEN := $(TXZGEN_IN:.in=)
TXZTARGETS := $(patsubst %, $(TXZPREPDIR)/%, $(TXZGEN))
PKG_REVISION ?= 3
PKG_ARCH := $(shell uname -m)
prep: $(TXZTARGETS)
build: prep
$(MAKE) -C ../../src clean
@printf "Building source tar.xz package\n"
(cd $(BUILDDIR); bash srctxz/create_srctxz.sh)
mkdir -p $(PKGDIR)
mv $(BUILDDIR)/../../nccl-src*.txz $(PKGDIR)
clean:
rm -Rf $(TXZPREPDIR) $(PKGDIR)
$(TXZPREPDIR)/% : %.in
@printf "Generating %-35s > %s\n" $< $@
mkdir -p $(TXZPREPDIR)
sed -e "s/\$${nccl:Major}/$(NCCL_MAJOR)/g" \
-e "s/\$${nccl:Minor}/$(NCCL_MINOR)/g" \
-e "s/\$${nccl:Patch}/$(NCCL_PATCH)/g" \
-e "s/\$${nccl:Suffix}/$(NCCL_SUFFIX)/g" \
-e "s/\$${pkg:Revision}/$(PKG_REVISION)/g" \
$< > $@

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@ -1,35 +0,0 @@
#!/bin/bash
#
# Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved.
#
# See LICENSE.txt for license information
#
# To run from $BUILDDIR/
cd ..
NCCLDIR=`basename $PWD`
echo "Checking for unclean directory ..."
git clean -x -i
echo "Clean done."
echo "Checking for uncommited files ..."
if [ "`git status -s | wc -l`" != "0" ]; then
git status -s
echo "Some changes are not committed yet. Continue ? (Ctrl-C to abort)"
read
fi
cd ..
NCCL_MAJOR=${nccl:Major}
NCCL_MINOR=${nccl:Minor}
NCCL_PATCH=${nccl:Patch}
NCCL_SUFFIX=${nccl:Suffix}
NCCL_BUILD=${pkg:Revision}
NCCLNAME="nccl-src_${NCCL_MAJOR}.${NCCL_MINOR}.${NCCL_PATCH}${NCCL_SUFFIX}-${NCCL_BUILD}"
tar --exclude build \
--exclude ".git*" \
--exclude pkg/srctxz \
--transform "s/^$NCCLDIR/$NCCLNAME/" -Jcf $NCCLNAME.txz --owner=0 --group=0 $NCCLDIR

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@ -1,43 +0,0 @@
#
# Copyright (c) 2015-2019, NVIDIA CORPORATION. All rights reserved.
#
# See LICENSE.txt for license information
#
include ../../makefiles/common.mk
include ../../makefiles/version.mk
BUILDDIR ?= $(abspath ../../build)
TXZPREPDIR := $(BUILDDIR)/txz
PKGDIR := $(BUILDDIR)/pkg/txz/
TXZGEN_IN := $(wildcard *.in)
TXZGEN := $(TXZGEN_IN:.in=)
TXZTARGETS := $(patsubst %, $(TXZPREPDIR)/%, $(TXZGEN))
PKG_ARCH := $(shell uname -m)
prep: $(TXZTARGETS)
$(MAKE) -C ../.. lic BUILDDIR=$(BUILDDIR)
build: prep
$(MAKE) -C ../.. src.build BUILDDIR=$(BUILDDIR)
@printf "Building tar.xz package\n"
(cd $(BUILDDIR); bash txz/create_txz.sh)
mkdir -p $(PKGDIR)
mv $(BUILDDIR)/../nccl*.txz $(PKGDIR)
clean:
rm -Rf $(TXZPREPDIR) $(PKGDIR)
$(TXZPREPDIR)/% : %.in
@printf "Generating %-35s > %s\n" $< $@
mkdir -p $(TXZPREPDIR)
sed -e "s/\$${nccl:Major}/$(NCCL_MAJOR)/g" \
-e "s/\$${nccl:Minor}/$(NCCL_MINOR)/g" \
-e "s/\$${nccl:Patch}/$(NCCL_PATCH)/g" \
-e "s/\$${nccl:Suffix}/$(NCCL_SUFFIX)/g" \
-e "s/\$${cuda:Major}/$(CUDA_MAJOR)/g" \
-e "s/\$${cuda:Minor}/$(CUDA_MINOR)/g" \
-e "s/\$${pkg:Revision}/$(PKG_REVISION)/g" \
-e "s/\$${pkg:Arch}/$(PKG_ARCH)/g" \
$< > $@

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@ -1,24 +0,0 @@
#!/bin/bash
#
# Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved.
#
# See LICENSE.txt for license information
#
# To run from $BUILDDIR/
BUILDDIR=`basename $PWD`
cd ..
NCCL_MAJOR=${nccl:Major}
NCCL_MINOR=${nccl:Minor}
NCCL_PATCH=${nccl:Patch}
NCCL_SUFFIX=${nccl:Suffix}
CUDA_MAJOR=${cuda:Major}
CUDA_MINOR=${cuda:Minor}
PKG_REVISION=${pkg:Revision}
PKG_ARCH=${pkg:Arch}
NCCLNAME="nccl_${NCCL_MAJOR}.${NCCL_MINOR}.${NCCL_PATCH}${NCCL_SUFFIX}-${PKG_REVISION}+cuda${CUDA_MAJOR}.${CUDA_MINOR}_${PKG_ARCH}"
tar --transform "s/^$BUILDDIR/$NCCLNAME/" -Jcf $NCCLNAME.txz --owner=0 --group=0 $BUILDDIR/bin $BUILDDIR/include $BUILDDIR/lib $BUILDDIR/*.txt

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@ -1,159 +0,0 @@
#
# Copyright (c) 2015-2022, NVIDIA CORPORATION. All rights reserved.
#
# See LICENSE.txt for license information
#
include ../makefiles/common.mk
include ../makefiles/version.mk
##### src files
INCEXPORTS := nccl.h
LIBSRCFILES := \
bootstrap.cc channel.cc collectives.cc debug.cc enqueue.cc group.cc \
init.cc init_nvtx.cc proxy.cc transport.cc mnnvl.cc allocator.cc symmetric.cc \
$(wildcard graph/*.cc) \
$(wildcard misc/*.cc) \
$(wildcard transport/*.cc) \
$(wildcard register/*.cc) \
$(wildcard plugin/*.cc) \
$(wildcard plugin/net/*.cc) \
$(wildcard plugin/tuner/*.cc) \
$(wildcard plugin/profiler/*.cc) \
$(filter-out ras/client.cc,$(wildcard ras/*.cc))
BINSRCFILES := ras/client.cc
##### lib files
LIBNAME := libnccl.so
STATICLIBNAME := libnccl_static.a
##### binaries
BINNAME := ncclras
##### pkgconfig files
PKGCONFIGFILE := nccl.pc
##### dirs
BUILDDIR ?= $(abspath ../build)
INCDIR := $(BUILDDIR)/include
LIBDIR := $(BUILDDIR)/lib
OBJDIR := $(BUILDDIR)/obj
PKGDIR := $(BUILDDIR)/lib/pkgconfig
BINDIR := $(BUILDDIR)/bin
##### target files
CUDARTLIB ?= cudart_static
# Use compatibility shim only with static cudart; see https://github.com/NVIDIA/nccl/issues/658
ifeq ($(CUDARTLIB), cudart_static)
LIBSRCFILES += enhcompat.cc
endif
INCTARGETS := $(INCEXPORTS:%=$(INCDIR)/%)
LIBSONAME := $(LIBNAME:%=%.$(NCCL_MAJOR))
LIBTARGET := $(LIBNAME:%=%.$(NCCL_MAJOR).$(NCCL_MINOR).$(NCCL_PATCH))
STATICLIBTARGET := $(STATICLIBNAME)
PKGTARGET := $(PKGCONFIGFILE)
LIBOBJ := $(LIBSRCFILES:%.cc=$(OBJDIR)/%.o)
BINOBJ := $(BINSRCFILES:%.cc=$(OBJDIR)/%.o)
DEPFILES := $(LIBOBJ:%.o=%.d) $(BINOBJ:%.o=%.d)
LDFLAGS += -L${CUDA_LIB} -l$(CUDARTLIB) -lpthread -lrt -ldl
INCPLUGIN := include/plugin
DEVMANIFEST := $(BUILDDIR)/obj/device/manifest
##### rules
build : lib staticlib binary
lib : $(INCTARGETS) $(LIBDIR)/$(LIBTARGET) $(PKGDIR)/$(PKGTARGET)
staticlib : $(LIBDIR)/$(STATICLIBTARGET)
binary : $(BINDIR)/$(BINNAME)
$(DEVMANIFEST): ALWAYS_REBUILD $(INCTARGETS)
$(MAKE) -C ./device
# Empty target to force rebuild
ALWAYS_REBUILD:
-include $(DEPFILES)
$(LIBDIR)/$(LIBTARGET) $(LIBDIR)/$(STATICLIBTARGET) : $(LIBOBJ)
$(INCDIR)/nccl.h : nccl.h.in ../makefiles/version.mk
# NCCL_VERSION(X,Y,Z) ((X) * 10000 + (Y) * 100 + (Z))
@$(eval NCCL_VERSION := $(shell printf "%d%02d%02d" $(NCCL_MAJOR) $(NCCL_MINOR) $(NCCL_PATCH)))
mkdir -p $(INCDIR)
@printf "Generating %-35s > %s\n" $< $@
sed -e "s/\$${nccl:Major}/$(NCCL_MAJOR)/g" \
-e "s/\$${nccl:Minor}/$(NCCL_MINOR)/g" \
-e "s/\$${nccl:Patch}/$(NCCL_PATCH)/g" \
-e "s/\$${nccl:Suffix}/$(NCCL_SUFFIX)/g" \
-e "s/\$${nccl:Version}/$(NCCL_VERSION)/g" \
$< > $@
$(LIBDIR)/$(LIBTARGET): $(LIBOBJ) $(DEVMANIFEST)
@printf "Linking %-35s > %s\n" $(LIBTARGET) $@
mkdir -p $(LIBDIR)
$(CXX) $(CXXFLAGS) -shared -Wl,--no-as-needed -Wl,-soname,$(LIBSONAME) -o $@ $(LIBOBJ) $$(cat $(DEVMANIFEST)) $(LDFLAGS)
ln -sf $(LIBSONAME) $(LIBDIR)/$(LIBNAME)
ln -sf $(LIBTARGET) $(LIBDIR)/$(LIBSONAME)
$(LIBDIR)/$(STATICLIBTARGET): $(LIBOBJ) $(DEVMANIFEST)
@printf "Archiving %-35s > %s\n" $(STATICLIBTARGET) $@
mkdir -p $(LIBDIR)
ar cr $@ $(LIBOBJ) $$(cat $(DEVMANIFEST))
$(BINDIR)/$(BINNAME): $(BINOBJ)
@printf "Linking %-35s > %s\n" $(BINNAME) $@
mkdir -p $(BINDIR)
$(CXX) $(CXXFLAGS) $^ -o $@
$(PKGDIR)/nccl.pc : nccl.pc.in
mkdir -p $(PKGDIR)
@printf "Generating %-35s > %s\n" $< $@
sed -e 's|$${nccl:Prefix}|\$(PREFIX)|g' \
-e "s/\$${nccl:Major}/$(NCCL_MAJOR)/g" \
-e "s/\$${nccl:Minor}/$(NCCL_MINOR)/g" \
-e "s/\$${nccl:Patch}/$(NCCL_PATCH)/g" \
$< > $@
$(INCDIR)/%.h : %.h
@printf "Grabbing %-35s > %s\n" $< $@
mkdir -p $(INCDIR)
install -m 644 $< $@
$(INCDIR)/nccl_%.h : include/nccl_%.h
@printf "Grabbing %-35s > %s\n" $< $@
mkdir -p $(INCDIR)
install -m 644 $< $@
$(PKGDIR)/%.pc : %.pc
@printf "Grabbing %-35s > %s\n" $< $@
mkdir -p $(PKGDIR)
install -m 644 $< $@
$(OBJDIR)/%.o : %.cc $(INCTARGETS)
@printf "Compiling %-35s > %s\n" $< $@
mkdir -p `dirname $@`
$(CXX) -I. -I$(INCDIR) $(CXXFLAGS) -Iinclude -I$(INCPLUGIN) -c $< -o $@
@$(CXX) -I. -I$(INCDIR) $(CXXFLAGS) -Iinclude -I$(INCPLUGIN) -M $< > $(@:%.o=%.d.tmp)
@sed "0,/^.*:/s//$(subst /,\/,$@):/" $(@:%.o=%.d.tmp) > $(@:%.o=%.d)
@sed -e 's/.*://' -e 's/\\$$//' < $(@:%.o=%.d.tmp) | fmt -1 | \
sed -e 's/^ *//' -e 's/$$/:/' >> $(@:%.o=%.d)
@rm -f $(@:%.o=%.d.tmp)
clean :
$(MAKE) -C device clean
rm -rf ${BINDIR} ${INCDIR} ${LIBDIR} ${PKGDIR} ${OBJDIR}
install : build
mkdir -p $(PREFIX)/lib
mkdir -p $(PREFIX)/lib/pkgconfig
mkdir -p $(PREFIX)/include
mkdir -p $(PREFIX)/bin
cp -P -v $(BUILDDIR)/lib/lib* $(PREFIX)/lib/
cp -P -v $(BUILDDIR)/lib/pkgconfig/* $(PREFIX)/lib/pkgconfig/
cp -v $(BUILDDIR)/include/* $(PREFIX)/include/
cp -v $(BUILDDIR)/bin/ncclras $(PREFIX)/bin/
FILESTOFORMAT := $(shell find . -name ".\#*" -prune -o \( -name "*.cc" -o -name "*.h" \) -print | grep -v -E 'ibvwrap.h|nvmlwrap.h|gdrwrap.h|nccl.h')
# Note that formatting.mk defines a new target so in order to not overwrite the default target,
# it shouldn't be included at the top. Also, it uses the above definition of FILESTOFORMAT as well
# as the BUILDDIR variable.
include ../makefiles/formatting.mk

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/*************************************************************************
* Copyright (c) 2015-2016, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#include "core.h"
#include "enqueue.h"
#include "primitives.h"
#define NUM_SUBSTEPS 2
#define NUM_BUFCHUNKS 2
// Increase Step and poffset/noffset for buffer sync
#define NEXT_STEP \
step++; \
poffset = noffset; \
noffset += sliceSize; \
if (noffset == buffSize) noffset = 0;
#define ALIGN_SIZE(size, align) \
size = ((size + (align) - 1) / (align)) * (align);
template<int THREADS, int UNROLL, class FUNC, typename T>
__launch_bounds__(THREADS+WARP_SIZE, 1)
__global__ void AllGatherKernel(const KernelArgs<T> args) {
const int tid = threadIdx.x;
__shared__ T* sharedNextOutput;
__shared__ DevRing<T> ring;
bool pushrecv = args.pushrecv;
LoadRing<THREADS>(args.ring, &ring);
__syncthreads();
if (tid == 0) {
WaitFlag prevCommOp(ring.prevOpCounter, 0);
WaitFlag nextCommOp(ring.nextOpCounter, 0);
prevCommOp.wait(args.opIndex);
nextCommOp.wait(args.opIndex);
if (pushrecv) {
*ring.sendPtrToPrev = (T*)args.ThisOutput;
Wait([=] {
return *ring.recvPtrFromNext != nullptr;
});
sharedNextOutput = *ring.recvPtrFromNext;
*ring.recvPtrFromNext = nullptr;
}
}
__syncthreads();
WaitFlag waitDoneFromNext(ring.recvFlagFromNext, -NUM_BUFCHUNKS*NUM_SUBSTEPS);
WaitFlag waitReadyFromPrev(ring.recvFlagFromPrev, -1*NUM_SUBSTEPS);
PostFlag postDoneToPrev(ring.sendFlagToPrev, -1*NUM_SUBSTEPS);
PostFlag postReadyToNext(ring.sendFlagToNext, 0);
typedef Primitives<THREADS, UNROLL, NUM_SUBSTEPS, T> Prims;
const int size = args.N;
const int nranks = args.nRanks;
const int buffSize = args.buffSize / sizeof(T);
const int sliceSize = buffSize / NUM_BUFCHUNKS;
int step = 0;
int poffset, noffset = 0;
// Compute pointers
const T * __restrict__ thisInput = args.ThisInput;
T * __restrict__ thisOutput = args.ThisOutput;
T * __restrict__ prevInput = ring.recvBuffer;
T * __restrict__ nextOutput = ring.sendBuffer;
for (int chunkOffset = 0; chunkOffset < size; chunkOffset += sliceSize) {
/////////////// begin AllGather steps ///////////////
int offset;
int maxOffset = size-chunkOffset;
int rankDest;
// step 0: push data to next GPU
rankDest = ring.userRank[0];
offset = chunkOffset + rankDest * size;
if (thisInput == thisOutput) {
Prims::Copy(
thisInput + offset,
pushrecv ? sharedNextOutput + offset : nextOutput + noffset,
sliceSize, maxOffset,
step,
waitDoneFromNext, waitReadyFromPrev,
postReadyToNext, postDoneToPrev);
} else {
Prims::DoubleCopy(
thisInput + chunkOffset,
thisOutput + offset,
pushrecv ? sharedNextOutput + offset : nextOutput + noffset,
sliceSize, maxOffset,
step,
waitDoneFromNext, waitReadyFromPrev,
postReadyToNext, postDoneToPrev);
}
NEXT_STEP; // Increases step, poffset, noffset
// k-2 steps: copy to next GPU
if (pushrecv) {
for (int j=1; j<nranks-1; ++j) {
rankDest = ring.userRank[nranks-j];
offset = chunkOffset + rankDest * size;
Prims::Copy(
thisOutput + offset,
sharedNextOutput + offset,
sliceSize, maxOffset,
step,
waitDoneFromNext, waitReadyFromPrev,
postReadyToNext, postDoneToPrev);
NEXT_STEP;
}
} else {
for (int j=1; j<nranks-1; ++j) {
rankDest = ring.userRank[nranks-j];
offset = chunkOffset + rankDest * size;
Prims::DoubleCopy(
prevInput + poffset,
thisOutput + offset,
nextOutput + noffset,
sliceSize, maxOffset,
step,
waitDoneFromNext, waitReadyFromPrev,
postReadyToNext, postDoneToPrev);
NEXT_STEP;
}
// Make final copy from buffer to dest.
rankDest = ring.userRank[1];
offset = chunkOffset + rankDest * size;
// Here we need to copy from buffer to this output.
Prims::Copy(
prevInput + poffset,
thisOutput + offset,
sliceSize, maxOffset,
step,
waitDoneFromNext, waitReadyFromPrev,
postReadyToNext, postDoneToPrev);
NEXT_STEP;
}
}
// wait for the last data to be pushed to us
if (tid == 0) {
// Wait for last update from next then reset the flag
waitDoneFromNext.wait(NUM_SUBSTEPS*(step+NUM_BUFCHUNKS-1));
*ring.recvFlagFromNext = 0;
// Wait for last update from prev then reset the flag
waitReadyFromPrev.wait(NUM_SUBSTEPS*(step+1));
*ring.recvFlagFromPrev = 0;
incrementOpCounter(&args);
}
}
#define THREADS 384
#define UNROLL 8
template<class FUNC, typename T>
ncclResult_t RingAllGather(const void* sendbuff, void* recvbuff,
const int count, ncclComm* comm, cudaStream_t stream) {
if (count == 0)
return ncclSuccess;
if (comm->nRanks == 1) {
if (sendbuff != recvbuff)
CUDACHECK(cudaMemcpyAsync(recvbuff, sendbuff, count*sizeof(T), cudaMemcpyDeviceToDevice, stream));
} else {
KernelArgs<T> args;
ArgsSetup(&args, sendbuff, recvbuff, 0, count, comm);
LAUNCH_KERNEL(AllGatherKernel, THREADS, UNROLL, FUNC, T, args, stream);
}
return ncclSuccess;
}
template<typename T, template<typename> class RedOp>
class AllGather {
public:
static ncclResult_t entry(const void* sendbuff, void* recvbuff,
int count, int /*root*/, ncclComm* comm, cudaStream_t stream) {
return RingAllGather<RedOp<T>, T>(sendbuff, recvbuff, count, comm, stream);
}
};
NCCL_API(ncclResult_t, ncclAllGather, const void* sendbuff, int count, ncclDataType_t datatype,
void* recvbuff, ncclComm_t comm, cudaStream_t stream);
ncclResult_t ncclAllGather(const void* sendbuff, int count, ncclDataType_t datatype,
void* recvbuff, ncclComm_t comm, cudaStream_t stream) {
return enqueue<AllGather, FuncNull>(sendbuff, recvbuff, count, datatype, 0, comm, stream);
}

233
src/all_reduce.cu Normal file
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/*************************************************************************
* Copyright (c) 2015-2016, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#include "core.h"
#include "enqueue.h"
#include "primitives.h"
#define NUM_SUBSTEPS 2
#define NUM_BUFCHUNKS 2
// Increase Step and poffset/noffset for buffer sync
#define NEXT_STEP \
step++; \
poffset = noffset; \
noffset += sliceSize; \
if (noffset == buffSize) noffset = 0;
#define ALIGN_SIZE(size, align) \
size = ((size + (align) - 1) / (align)) * (align);
template<int THREADS, int UNROLL, class FUNC, typename T>
__launch_bounds__(THREADS+WARP_SIZE, 1)
__global__ void AllReduceKernel(const KernelArgs<T> args) {
const int tid = threadIdx.x;
__shared__ T* sharedNextOutput;
__shared__ DevRing<T> ring;
bool pushrecv = args.pushrecv;
LoadRing<THREADS>(args.ring, &ring);
__syncthreads();
if (tid == 0) {
WaitFlag prevCommOp(ring.prevOpCounter, 0);
WaitFlag nextCommOp(ring.nextOpCounter, 0);
prevCommOp.wait(args.opIndex);
nextCommOp.wait(args.opIndex);
if (pushrecv) {
*ring.sendPtrToPrev = (T*)args.ThisOutput;
Wait([=] {
return *ring.recvPtrFromNext != nullptr;
});
sharedNextOutput = *ring.recvPtrFromNext;
*ring.recvPtrFromNext = nullptr;
}
}
__syncthreads();
WaitFlag waitDoneFromNext(ring.recvFlagFromNext, -NUM_BUFCHUNKS*NUM_SUBSTEPS);
WaitFlag waitReadyFromPrev(ring.recvFlagFromPrev, -1*NUM_SUBSTEPS);
PostFlag postDoneToPrev(ring.sendFlagToPrev, -1*NUM_SUBSTEPS);
PostFlag postReadyToNext(ring.sendFlagToNext, 0);
typedef Primitives<THREADS, UNROLL, NUM_SUBSTEPS, T, FUNC> Prims;
const int size = args.N;
const int nranks = args.nRanks;
const int buffSize = args.buffSize / sizeof(T);
const int sliceSize = buffSize / NUM_BUFCHUNKS;
int step = 0;
int poffset, noffset = 0;
// Compute pointers
const T * __restrict__ thisInput = args.ThisInput;
T * __restrict__ thisOutput = args.ThisOutput;
T * __restrict__ prevInput = ring.recvBuffer;
T * __restrict__ nextOutput = ring.sendBuffer;
for (int chunkOffset = 0; chunkOffset < size; chunkOffset += nranks*sliceSize) {
/////////////// begin AllReduce steps ///////////////
int offset;
int maxOffset;
int slice;
// step 0: push data to next GPU
slice = ring.userRank[nranks-1];
offset = chunkOffset + slice * sliceSize;
maxOffset = size-offset;
Prims::Copy(
thisInput + offset,
nextOutput + noffset,
sliceSize, maxOffset,
step,
waitDoneFromNext, waitReadyFromPrev,
postReadyToNext, postDoneToPrev);
NEXT_STEP; // Increases step, poffset, noffset
// k-2 steps: reduce and copy to next GPU
for (int j=2; j<nranks; ++j) {
slice = ring.userRank[nranks-j];
offset = chunkOffset + slice * sliceSize;
maxOffset = size-offset;
Prims::Reduce(
prevInput + poffset,
thisInput + offset,
nextOutput + noffset,
sliceSize, maxOffset,
step,
waitDoneFromNext, waitReadyFromPrev,
postReadyToNext, postDoneToPrev);
NEXT_STEP;
}
// step k - 1: reduce this buffer and data, which will produce the final
// result that we store in this data and push to the next GPU
slice = ring.userRank[0];
offset = chunkOffset + slice * sliceSize;
maxOffset = size-offset;
Prims::ReduceCopy(
prevInput + poffset,
thisInput + offset,
pushrecv ? (sharedNextOutput + offset) : (nextOutput + noffset),
thisOutput + offset,
sliceSize, maxOffset,
step,
waitDoneFromNext, waitReadyFromPrev,
postReadyToNext, postDoneToPrev);
NEXT_STEP;
if (pushrecv) {
// k-2 steps: copy result to next GPU
for (int j=1; j<nranks-1; ++j) {
slice = ring.userRank[nranks - j];
offset = chunkOffset + slice * sliceSize;
maxOffset = size-offset;
Prims::Copy(
thisOutput + offset,
sharedNextOutput + offset,
sliceSize, maxOffset,
step,
waitDoneFromNext, waitReadyFromPrev,
postReadyToNext, postDoneToPrev);
NEXT_STEP;
}
} else {
// k-2 steps: copy result to next GPU
for (int j=1; j<nranks-1; ++j) {
slice = ring.userRank[nranks - j];
offset = chunkOffset + slice * sliceSize;
maxOffset = size-offset;
Prims::DoubleCopy(
prevInput + poffset,
thisOutput + offset,
nextOutput + noffset,
sliceSize, maxOffset,
step,
waitDoneFromNext, waitReadyFromPrev,
postReadyToNext, postDoneToPrev);
NEXT_STEP;
}
// Make final copy from buffer to dest.
slice = ring.userRank[1];
offset = chunkOffset + slice * sliceSize;
maxOffset = size-offset;
// Here we need to copy from buffer to this output.
Prims::Copy(
prevInput + poffset,
thisOutput + offset,
sliceSize, maxOffset,
step,
waitDoneFromNext, waitReadyFromPrev,
postReadyToNext, postDoneToPrev);
NEXT_STEP;
}
}
// wait for the last data to be pushed to us
if (tid == 0) {
// Wait for last update from next then reset the flag
waitDoneFromNext.wait(NUM_SUBSTEPS*(step+NUM_BUFCHUNKS-1));
*ring.recvFlagFromNext = 0;
// Wait for last update from prev then reset the flag
waitReadyFromPrev.wait(NUM_SUBSTEPS*(step+1));
*ring.recvFlagFromPrev = 0;
incrementOpCounter(&args);
}
}
#define THREADS 512
#define UNROLL 8
template<class FUNC, typename T>
ncclResult_t RingAllReduce(const void* sendbuff, void* recvbuff,
const int count, ncclComm* comm, cudaStream_t stream) {
if (count == 0)
return ncclSuccess;
if (comm->nRanks == 1) {
if (sendbuff != recvbuff)
CUDACHECK(cudaMemcpyAsync(recvbuff, sendbuff, count*sizeof(T), cudaMemcpyDeviceToDevice, stream));
} else {
KernelArgs<T> args;
ArgsSetup(&args, sendbuff, recvbuff, 0, count, comm);
LAUNCH_KERNEL(AllReduceKernel, THREADS, UNROLL, FUNC, T, args, stream);
}
return ncclSuccess;
}
template<typename T, template <typename> class RedOp>
class AllReduce {
public:
static ncclResult_t entry(const void* sendbuff, void* recvbuff,
int count, int /*root*/, ncclComm* comm, cudaStream_t stream) {
return RingAllReduce<RedOp<T>, T>(sendbuff, recvbuff, count, comm, stream);
}
};
NCCL_API(ncclResult_t, ncclAllReduce, const void* sendbuff, void* recvbuff, int count,
ncclDataType_t datatype, ncclRedOp_t op, ncclComm_t comm, cudaStream_t stream);
ncclResult_t ncclAllReduce(const void* sendbuff, void* recvbuff, int count,
ncclDataType_t datatype, ncclRedOp_t op, ncclComm_t comm, cudaStream_t stream) {
return enqueue<AllReduce>(sendbuff, recvbuff, count, datatype, op, 0, comm, stream);
}

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@ -1,196 +0,0 @@
/*************************************************************************
* Copyright (c) 2015-2025, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#include "comm.h"
#include "transport.h"
#include "group.h"
NCCL_API(ncclResult_t, ncclMemAlloc, void **ptr, size_t size);
ncclResult_t ncclMemAlloc(void **ptr, size_t size) {
NVTX3_FUNC_RANGE_IN(nccl_domain);
ncclResult_t ret = ncclSuccess;
#if CUDART_VERSION >= 12010
size_t memGran = 0;
CUdevice currentDev;
CUmemAllocationProp memprop = {};
CUmemAccessDesc accessDesc = {};
CUmemGenericAllocationHandle handle = (CUmemGenericAllocationHandle)-1;
int cudaDev;
int flag;
int dcnt;
if (ptr == NULL || size == 0) goto fallback;
if (ncclCudaLibraryInit() != ncclSuccess) goto fallback;
CUDACHECK(cudaGetDevice(&cudaDev));
CUCHECK(cuDeviceGet(&currentDev, cudaDev));
if (ncclCuMemEnable()) {
size_t handleSize = size;
int requestedHandleTypes = CU_MEM_HANDLE_TYPE_POSIX_FILE_DESCRIPTOR;
// Query device to see if FABRIC handle support is available
flag = 0;
(void) CUPFN(cuDeviceGetAttribute(&flag, CU_DEVICE_ATTRIBUTE_HANDLE_TYPE_FABRIC_SUPPORTED, currentDev));
if (flag) requestedHandleTypes |= CU_MEM_HANDLE_TYPE_FABRIC;
memprop.type = CU_MEM_ALLOCATION_TYPE_PINNED;
memprop.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
memprop.requestedHandleTypes = (CUmemAllocationHandleType) requestedHandleTypes;
memprop.location.id = currentDev;
// Query device to see if RDMA support is available
flag = 0;
CUCHECK(cuDeviceGetAttribute(&flag, CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_WITH_CUDA_VMM_SUPPORTED, currentDev));
if (flag) memprop.allocFlags.gpuDirectRDMACapable = 1;
CUCHECK(cuMemGetAllocationGranularity(&memGran, &memprop, CU_MEM_ALLOC_GRANULARITY_RECOMMENDED));
CUDACHECK(cudaGetDeviceCount(&dcnt));
ALIGN_SIZE(handleSize, memGran);
if (requestedHandleTypes & CU_MEM_HANDLE_TYPE_FABRIC) {
/* First try cuMemCreate() with FABRIC handle support and then remove if it fails */
CUresult err = CUPFN(cuMemCreate(&handle, handleSize, &memprop, 0));
if (err == CUDA_ERROR_NOT_PERMITTED || err == CUDA_ERROR_NOT_SUPPORTED) {
requestedHandleTypes &= ~CU_MEM_HANDLE_TYPE_FABRIC;
memprop.requestedHandleTypes = (CUmemAllocationHandleType) requestedHandleTypes;
/* Allocate the physical memory on the device */
CUCHECK(cuMemCreate(&handle, handleSize, &memprop, 0));
} else if (err != CUDA_SUCCESS) {
// Catch and report any error from above
CUCHECK(cuMemCreate(&handle, handleSize, &memprop, 0));
}
} else {
/* Allocate the physical memory on the device */
CUCHECK(cuMemCreate(&handle, handleSize, &memprop, 0));
}
/* Reserve a virtual address range */
CUCHECK(cuMemAddressReserve((CUdeviceptr*)ptr, handleSize, memGran, 0, 0));
/* Map the virtual address range to the physical allocation */
CUCHECK(cuMemMap((CUdeviceptr)*ptr, handleSize, 0, handle, 0));
/* Now allow RW access to the newly mapped memory */
for (int i = 0; i < dcnt; ++i) {
int p2p = 0;
if (i == cudaDev || ((cudaDeviceCanAccessPeer(&p2p, i, cudaDev) == cudaSuccess) && p2p)) {
accessDesc.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
accessDesc.location.id = i;
accessDesc.flags = CU_MEM_ACCESS_FLAGS_PROT_READWRITE;
CUCHECK(cuMemSetAccess((CUdeviceptr)*ptr, handleSize, &accessDesc, 1));
}
if (0 == p2p && i != cudaDev) INFO(NCCL_ALLOC, "P2P not supported between GPU%d and GPU%d", cudaDev, i);
}
goto exit;
}
fallback:
#endif
// Coverity is right to complain that we may pass a NULL ptr to cudaMalloc. That's deliberate though:
// we want CUDA to return an error to the caller.
// coverity[var_deref_model]
CUDACHECKGOTO(cudaMalloc(ptr, size), ret, fail);
exit:
return ret;
fail:
goto exit;
}
NCCL_API(ncclResult_t, ncclMemFree, void *ptr);
ncclResult_t ncclMemFree(void *ptr) {
NVTX3_FUNC_RANGE_IN(nccl_domain);
ncclResult_t ret = ncclSuccess;
int saveDevice;
CUDACHECK(cudaGetDevice(&saveDevice));
#if CUDART_VERSION >= 12010
CUdevice ptrDev = 0;
if (ptr == NULL) goto fallback;
if (ncclCudaLibraryInit() != ncclSuccess) goto fallback;
CUCHECKGOTO(cuPointerGetAttribute((void*)&ptrDev, CU_POINTER_ATTRIBUTE_DEVICE_ORDINAL, (CUdeviceptr)ptr), ret, fail);
CUDACHECKGOTO(cudaSetDevice((int)ptrDev), ret, fail);
if (ncclCuMemEnable()) {
NCCLCHECKGOTO(ncclCuMemFree(ptr), ret, fail);
goto exit;
}
fallback:
#endif
CUDACHECKGOTO(cudaFree(ptr), ret, fail);
exit:
CUDACHECK(cudaSetDevice(saveDevice));
return ret;
fail:
goto exit;
}
// This is a collective function and should be called by all ranks in the communicator
ncclResult_t ncclCommSymmetricAllocInternal(struct ncclComm* comm, size_t size, size_t alignment, void** symPtr) {
ncclResult_t ret = ncclSuccess;
void* regSymAddr = NULL;
size_t allocSize = size;
size_t granularity;
CUdevice cuDev;
CUmemAllocationProp memprop = {};
CUmemGenericAllocationHandle memHandle;
int bit = 0, cnt = 0;
// aligment must be power of 2 as an input
while (bit < sizeof(size_t) * 8) {
if (alignment & (1L << bit)) cnt++;
if (cnt == 2) {
WARN("rank %d alignment %ld is not power of 2", comm->rank, alignment);
goto fail;
}
bit++;
}
// temporarily align the alignment to NCCL_REC_PAGE_SIZE
ALIGN_SIZE(alignment, NCCL_REC_PAGE_SIZE);
CUCHECKGOTO(cuDeviceGet(&cuDev, comm->cudaDev), ret, fail);
memprop.type = CU_MEM_ALLOCATION_TYPE_PINNED;
memprop.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
memprop.requestedHandleTypes = ncclCuMemHandleType;
memprop.location.id = cuDev;
CUCHECKGOTO(cuMemGetAllocationGranularity(&granularity, &memprop, CU_MEM_ALLOC_GRANULARITY_RECOMMENDED), ret, fail);
ALIGN_SIZE(allocSize, granularity);
CUCHECKGOTO(cuMemCreate(&memHandle, allocSize, &memprop, 0), ret, fail);
ALIGN_SIZE(comm->symAllocHead, alignment);
NCCLCHECKGOTO(ncclIpcSymmetricMap(comm, comm->symAllocHead, allocSize, memHandle, &regSymAddr), ret, fail);
NCCLCHECKGOTO(ncclNvlsSymmetricMap(comm, comm->symAllocHead, allocSize, regSymAddr), ret, fail);
NCCLCHECKGOTO(bootstrapIntraNodeBarrier(comm->bootstrap, comm->localRankToRank, comm->localRank, comm->localRanks, comm->localRankToRank[0]), ret, fail);
comm->symAllocHead += allocSize;
*symPtr = regSymAddr;
exit:
return ret;
fail:
*symPtr = NULL;
goto exit;
}
ncclResult_t ncclCommSymmetricFreeInternal(struct ncclComm* comm, void* symPtr) {
CUmemGenericAllocationHandle handle;
size_t size = 0;
ncclResult_t ret = ncclSuccess;
int saveDev = comm->cudaDev;
CUDACHECKGOTO(cudaGetDevice(&saveDev), ret, fail);
if (ncclCuMemEnable()) {
CUDACHECKGOTO(cudaSetDevice(comm->cudaDev), ret, fail);
CUCHECKGOTO(cuMemRetainAllocationHandle(&handle, symPtr), ret, fail);
CUCHECKGOTO(cuMemRelease(handle), ret, fail);
CUCHECKGOTO(cuMemGetAddressRange(NULL, &size, (CUdeviceptr)symPtr), ret, fail);
NCCLCHECKGOTO(ncclNvlsSymmetricFree(comm, size, symPtr), ret, fail);
NCCLCHECKGOTO(ncclIpcSymmetricFree(comm, size, symPtr), ret, fail);
CUCHECKGOTO(cuMemRelease(handle), ret, fail);
}
exit:
CUDACHECK(cudaSetDevice(saveDev));
return ret;
fail:
goto exit;
}

File diff suppressed because it is too large Load Diff

165
src/broadcast.cu Normal file
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/*************************************************************************
* Copyright (c) 2015-2016, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#include "core.h"
#include "enqueue.h"
#include "primitives.h"
#define NUM_SUBSTEPS 2
#define NUM_BUFCHUNKS 2
// Increase Step and boffset for buffer sync
#define NEXT_STEP \
step++; \
boffset += sliceSize; \
if (boffset == buffSize) boffset = 0;
#define ALIGN_SIZE(size, align) \
size = ((size + (align) - 1) / (align)) * (align);
template<int THREADS, int UNROLL, class FUNC, typename T>
__launch_bounds__(THREADS+WARP_SIZE, 1)
__global__ void BroadcastKernel(const KernelArgs<T> args) {
const int tid = threadIdx.x;
__shared__ T* sharedNextOutput;
__shared__ DevRing<T> ring;
bool pushrecv = args.pushrecv;
LoadRing<THREADS>(args.ring, &ring);
__syncthreads();
if (tid == 0) {
WaitFlag prevCommOp(ring.prevOpCounter, 0);
WaitFlag nextCommOp(ring.nextOpCounter, 0);
prevCommOp.wait(args.opIndex);
nextCommOp.wait(args.opIndex);
if (pushrecv) {
*ring.sendPtrToPrev = (T*)args.ThisOutput;
Wait([=] {
return *ring.recvPtrFromNext != nullptr;
});
sharedNextOutput = *ring.recvPtrFromNext;
*ring.recvPtrFromNext = nullptr;
}
}
__syncthreads();
WaitFlag waitDoneFromNext(ring.recvFlagFromNext, (1-NUM_BUFCHUNKS)*NUM_SUBSTEPS);
WaitFlag waitReadyFromPrev(ring.recvFlagFromPrev, 0);
PostFlag postDoneToPrev(ring.sendFlagToPrev, 0);
PostFlag postReadyToNext(ring.sendFlagToNext, 0);
typedef Primitives<THREADS, UNROLL, NUM_SUBSTEPS, T> Prims;
const int size = args.N;
const int rank = ring.userRank[0];
const int nextRank = ring.userRank[1];
const int root = args.root;
const int buffSize = args.buffSize / sizeof(T);
const int sliceSize = buffSize / NUM_BUFCHUNKS;
int step = 0;
int boffset = 0;
// Compute pointers
const T * __restrict__ thisInput = args.ThisInput;
T * __restrict__ thisOutput = args.ThisOutput;
T * __restrict__ prevInput = ring.recvBuffer;
T * __restrict__ nextOutput = ring.sendBuffer;
for (int offset = 0; offset < size; offset += sliceSize) {
int maxOffset = size-offset;
if (rank == root) {
Prims::Copy(
thisInput + offset,
pushrecv ? sharedNextOutput + offset : nextOutput + boffset,
sliceSize, maxOffset,
step,
waitDoneFromNext,
postReadyToNext);
} else if (nextRank == root) {
if (pushrecv) maxOffset = 0; // Only wait for signals
Prims::Copy(
prevInput + boffset,
thisOutput + offset,
sliceSize, maxOffset,
step,
waitReadyFromPrev,
postDoneToPrev);
} else {
if (pushrecv) {
Prims::Copy(
thisOutput + offset,
sharedNextOutput + offset,
sliceSize, maxOffset,
step,
waitDoneFromNext, waitReadyFromPrev,
postReadyToNext, postDoneToPrev);
} else {
Prims::DoubleCopy(
prevInput + boffset,
thisOutput + offset,
nextOutput + boffset,
sliceSize, maxOffset,
step,
waitDoneFromNext, waitReadyFromPrev,
postReadyToNext, postDoneToPrev);
}
}
NEXT_STEP; // Increases step, boffset
}
// wait for the last data to be pushed to us
if (tid == 0) {
if (nextRank != root) {
// Wait for last update from next then reset the flag
waitDoneFromNext.wait(NUM_SUBSTEPS*(step+NUM_BUFCHUNKS-1));
*ring.recvFlagFromNext = 0;
}
if (rank != root) {
// reset the flag
*ring.recvFlagFromPrev = 0;
}
incrementOpCounter(&args);
}
}
#define THREADS 256
#define UNROLL 8
template<class FUNC, typename T>
ncclResult_t RingBroadcast(void* buff, const int count, const int root,
ncclComm* comm, cudaStream_t stream) {
if (count == 0)
return ncclSuccess;
if (comm->nRanks != 1) {
KernelArgs<T> args;
ArgsSetup(&args, buff, buff, root, count, comm);
LAUNCH_KERNEL(BroadcastKernel, THREADS, UNROLL, FUNC, T, args, stream);
}
return ncclSuccess;
}
template<typename T, template<typename> class RedOp>
class Broadcast {
public:
static ncclResult_t entry(const void* sendbuff, void* recvbuff,
int count, int root, ncclComm* comm, cudaStream_t stream) {
return RingBroadcast<RedOp<T>, T>(recvbuff, count, root, comm, stream);
}
};
NCCL_API(ncclResult_t, ncclBcast, void* buff, int count, ncclDataType_t datatype, int root,
ncclComm_t comm, cudaStream_t stream);
ncclResult_t ncclBcast(void* buff, int count, ncclDataType_t datatype, int root,
ncclComm_t comm, cudaStream_t stream) {
return enqueue<Broadcast, FuncNull>(nullptr, buff, count, datatype, root, comm, stream);
}

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@ -1,177 +0,0 @@
/*************************************************************************
* Copyright (c) 2015-2022, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#include "channel.h"
#include "param.h"
#include "gdrwrap.h"
#include "transport.h"
ncclResult_t initChannel(struct ncclComm* comm, int channelId) {
struct ncclChannel* channel = &comm->channels[channelId];
if (channel->id != -1) return ncclSuccess;
int nRanks = comm->nRanks;
int nvlsRanks = comm->localRanks;
int nPeers = nRanks + 1 /* Collnet */ + nvlsRanks /* NVLS */;
channel->id = channelId;
channel->workFifoProduced = 0;
struct ncclSharedResources* sharedRes = comm->sharedRes;
cudaStream_t deviceStream;
NCCLCHECK(ncclStrongStreamAcquire(ncclCudaGraphNone(), &sharedRes->deviceStream, /*concurrent=*/false, &deviceStream));
if (channel->peers == NULL) {
// The extra on nRanks+1 is for collnet root (i.e. network)
// Allocate everything related to sharedRes with ncclCalloc as this can be
// shared between communicators hence should not be tied to comm.
if (sharedRes->peers[channelId] == NULL) {
NCCLCHECK(ncclCalloc(sharedRes->peers + channelId, sharedRes->tpNRanks));
}
channel->peers = ncclMemoryStackAlloc<struct ncclChannelPeer*>(&comm->memPermanent, nPeers);
for (int r = 0; r < nRanks; r++) {
channel->peers[r] = comm->sharedRes->peers[channelId] + comm->topParentRanks[r];
ncclAtomicRefCountIncrement(&channel->peers[r]->refCount);
}
}
if (channel->devPeers == NULL) {
if (sharedRes->devPeers[channelId] == NULL) {
NCCLCHECK(ncclCudaCallocAsync(sharedRes->devPeers + channelId, sharedRes->tpNRanks, deviceStream));
}
/* channel->devPeers is not shared, so just free it when calling commFree() */
NCCLCHECK(ncclCudaCallocAsync(&channel->devPeers, nPeers, deviceStream));
ncclCommPushCudaFree(comm, channel->devPeers);
NCCLCHECK(ncclCalloc(&channel->devPeersHostPtr, nPeers));
for (int r = 0; r < nRanks; r++) {
uintptr_t addr = (uintptr_t)(comm->sharedRes->devPeers[channelId] + comm->topParentRanks[r]);
NCCLCHECK(ncclCudaMemcpyAsync((uintptr_t*)(channel->devPeers + r), (uintptr_t*)&addr, 1, deviceStream));
channel->devPeersHostPtr[r] = (struct ncclDevChannelPeer*)addr;
}
}
channel->ring.userRanks = ncclMemoryStackAlloc<int>(&comm->memPermanent, nRanks);
NCCLCHECK(ncclCudaCallocAsync(&channel->devRingUserRanks, nRanks, deviceStream));
ncclCommPushCudaFree(comm, channel->devRingUserRanks);
/* guarantee addr has been copied into channel->devPeers */
NCCLCHECK(ncclStrongStreamRelease(ncclCudaGraphNone(), &sharedRes->deviceStream, /*concurrent=*/false));
NCCLCHECK(ncclStrongStreamSynchronize(&sharedRes->deviceStream));
return ncclSuccess;
}
ncclResult_t initNvlsChannel(struct ncclComm* comm, int channelId, struct ncclComm* parent, bool share) {
struct ncclChannel* channel = &comm->channels[channelId];
struct ncclSharedResources* sharedRes = comm->sharedRes;
cudaStream_t deviceStream;
if (channel->nvlsPeers != NULL)
return ncclSuccess;
if (channel->id == -1)
NCCLCHECK(initChannel(comm, channelId));
NCCLCHECK(ncclStrongStreamAcquire(ncclCudaGraphNone(), &sharedRes->deviceStream, /*concurrent=*/false, &deviceStream));
int nvlsRanks = comm->localRanks;
if (share) {
channel->nvlsPeers = parent->channels[channelId].nvlsPeers;
channel->nvlsDevPeers = parent->channels[channelId].nvlsDevPeers;
for (int r = 0; r < nvlsRanks; ++r) {
int tr = comm->topParentLocalRanks[r];
uintptr_t addr = (uintptr_t)(parent->channels[channelId].nvlsDevPeers + tr);
channel->peers[comm->nRanks + 1 + r] = parent->channels[channelId].nvlsPeers + tr;
NCCLCHECK(ncclCudaMemcpyAsync((uintptr_t*)(channel->devPeers + comm->nRanks + 1 + r), (uintptr_t*)&addr, 1, deviceStream));
channel->devPeersHostPtr[comm->nRanks + 1 + r] = (struct ncclDevChannelPeer*)addr;
ncclAtomicRefCountIncrement(&parent->channels[channelId].nvlsPeers[tr].refCount);
}
} else {
NCCLCHECK(ncclCalloc(&channel->nvlsPeers, nvlsRanks));
NCCLCHECK(ncclCudaCallocAsync(&channel->nvlsDevPeers, nvlsRanks, deviceStream));
for (int r = 0; r < nvlsRanks; ++r) {
uintptr_t addr = (uintptr_t)(channel->nvlsDevPeers + r);
channel->peers[comm->nRanks + 1 + r] = channel->nvlsPeers + r;
NCCLCHECK(ncclCudaMemcpyAsync((uintptr_t*)(channel->devPeers + comm->nRanks + 1 + r), (uintptr_t*)&addr, 1, deviceStream));
channel->devPeersHostPtr[comm->nRanks + 1 + r] = (struct ncclDevChannelPeer*)addr;
ncclAtomicRefCountIncrement(&channel->nvlsPeers[r].refCount);
}
}
NCCLCHECK(ncclStrongStreamRelease(ncclCudaGraphNone(), &sharedRes->deviceStream, /*concurrent=*/false));
NCCLCHECK(ncclStrongStreamSynchronize(&sharedRes->deviceStream));
return ncclSuccess;
}
ncclResult_t initCollnetChannel(struct ncclComm* comm, int channelId, struct ncclComm* parent, bool share) {
struct ncclChannel* channel = &comm->channels[channelId];
struct ncclSharedResources* sharedRes = comm->sharedRes;
uintptr_t addr;
cudaStream_t deviceStream;
if (channel->collnetPeers != NULL)
return ncclSuccess;
if (channel->id == -1)
NCCLCHECK(initChannel(comm, channelId));
NCCLCHECK(ncclStrongStreamAcquire(ncclCudaGraphNone(), &sharedRes->deviceStream, /*concurrent=*/false, &deviceStream));
if (share) {
channel->collnetPeers = parent->channels[channelId].collnetPeers;
channel->collnetDevPeers = parent->channels[channelId].collnetDevPeers;
addr = (uintptr_t)parent->channels[channelId].collnetDevPeers;
channel->peers[comm->nRanks] = parent->channels[channelId].collnetPeers;
NCCLCHECK(ncclCudaMemcpyAsync((uintptr_t*)(channel->devPeers + comm->nRanks), (uintptr_t*)&addr, 1, deviceStream));
channel->devPeersHostPtr[comm->nRanks] = (struct ncclDevChannelPeer*)addr;
ncclAtomicRefCountIncrement(&parent->channels[channelId].collnetPeers->refCount);
} else {
NCCLCHECK(ncclCalloc(&channel->collnetPeers, 1));
NCCLCHECK(ncclCudaCallocAsync(&channel->collnetDevPeers, 1, deviceStream));
addr = (uintptr_t)channel->collnetDevPeers;
channel->peers[comm->nRanks] = channel->collnetPeers;
NCCLCHECK(ncclCudaMemcpyAsync((uintptr_t*)(channel->devPeers + comm->nRanks), (uintptr_t*)&addr, 1, deviceStream));
channel->devPeersHostPtr[comm->nRanks] = (struct ncclDevChannelPeer*)addr;
ncclAtomicRefCountIncrement(&channel->collnetPeers->refCount);
}
NCCLCHECK(ncclStrongStreamRelease(ncclCudaGraphNone(), &sharedRes->deviceStream, /*concurrent=*/false));
NCCLCHECK(ncclStrongStreamSynchronize(&sharedRes->deviceStream));
return ncclSuccess;
}
ncclResult_t freeChannel(struct ncclChannel* channel, int nRanks, int collnetNRanks, int nvlsNRanks) {
int nPeers = nRanks + collnetNRanks + nvlsNRanks;
/* channel peers are only valid when async init thread completes commAlloc() and
* the channel is initialized with initChannel(); if either is not done, this channel
* should never be free. */
if (channel->id == -1 || channel->peers == NULL) return ncclSuccess;
// Free transport proxy resources
// Note: free all send resources first due to CollNet arrangement
for (int r = 0; r < nPeers; r++) {
struct ncclChannelPeer* peer = channel->peers[r];
if (peer) {
if (ncclAtomicRefCountDecrement(&peer->refCount) == 0) {
for (int b=0; b<NCCL_MAX_CONNS; b++) {
if (peer->send[b].transportComm) NCCLCHECK(peer->send[b].transportComm->free(peer->send+b));
if (peer->recv[b].transportComm) NCCLCHECK(peer->recv[b].transportComm->free(peer->recv+b));
}
if (r == nRanks) {
free(channel->collnetPeers);
ncclCudaFree(channel->collnetDevPeers);
} else if (r == nPeers - 1) {
free(channel->nvlsPeers);
ncclCudaFree(channel->nvlsDevPeers);
}
}
}
}
free(channel->devPeersHostPtr);
return ncclSuccess;
}

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@ -1,174 +0,0 @@
/*************************************************************************
* Copyright (c) 2015-2023, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#include "argcheck.h" // Need some checks here since we access comm
#include "collectives.h"
#include "enqueue.h"
#include "nccl.h"
#include "nvtx_payload_schemas.h"
const char* ncclFuncToString(ncclFunc_t fn) {
switch (fn) {
case ncclFuncAllGather: return "AllGather";
case ncclFuncAllReduce: return "AllReduce";
case ncclFuncBroadcast: return "Broadcast";
case ncclFuncRecv: return "Recv";
case ncclFuncReduce: return "Reduce";
case ncclFuncReduceScatter: return "ReduceScatter";
case ncclFuncSendRecv: return "SendRecv";
case ncclFuncSend: return "Send";
default: return "Invalid";
}
}
const char* ncclDevRedOpToString(ncclDevRedOp_t op) {
switch (op) {
case ncclDevSum: return "Sum";
case ncclDevProd: return "Prod";
case ncclDevMinMax: return "MinMax";
case ncclDevPreMulSum: return "PreMulSum";
case ncclDevSumPostDiv: return "SumPostDiv";
default: return "Unknown";
}
}
const char* ncclDatatypeToString(ncclDataType_t type) {
switch (type) {
case ncclInt8: return "ncclInt8";
case ncclInt32: return "ncclInt32";
case ncclUint32: return "ncclUint32";
case ncclInt64: return "ncclInt64";
case ncclUint64: return "ncclUint64";
case ncclFloat16: return "ncclFloat16";
case ncclFloat32: return "ncclFloat32";
case ncclFloat64: return "ncclFloat64";
case ncclBfloat16: return "ncclBfloat16";
case ncclFloat8e4m3: return "ncclFloat8e4m3";
case ncclFloat8e5m2: return "ncclFloat8e5m2";
default: return "Unknown";
}
}
const char* ncclAlgoToString(int algo) {
switch (algo) {
case NCCL_ALGO_TREE: return "TREE";
case NCCL_ALGO_RING: return "RING";
case NCCL_ALGO_COLLNET_DIRECT: return "COLLNET_DIRECT";
case NCCL_ALGO_COLLNET_CHAIN: return "COLLNET_CHAIN";
case NCCL_ALGO_NVLS: return "NVLS";
case NCCL_ALGO_NVLS_TREE: return "NVLS_TREE";
case NCCL_ALGO_PAT: return "PAT";
default: return "Unknown";
}
}
const char* ncclProtoToString(int proto) {
switch (proto) {
case NCCL_PROTO_LL: return "LL";
case NCCL_PROTO_LL128: return "LL128";
case NCCL_PROTO_SIMPLE: return "SIMPLE";
default: return "Unknown";
}
}
NCCL_API(ncclResult_t, ncclAllGather, const void* sendbuff, void* recvbuff, size_t sendcount,
ncclDataType_t datatype, ncclComm_t comm, cudaStream_t stream);
ncclResult_t ncclAllGather(const void* sendbuff, void* recvbuff, size_t sendcount,
ncclDataType_t datatype, ncclComm_t comm, cudaStream_t stream) {
// Just pass the size of one message and not the total bytes sent/received.
NVTX3_FUNC_WITH_PARAMS(AllGather, NcclNvtxParamsAllGather,
NVTX3_PAYLOAD(comm ? comm->commHash : 0, sendcount * ncclTypeSize(datatype)));
struct ncclInfo info = { ncclFuncAllGather, "AllGather",
sendbuff, recvbuff, sendcount, datatype, ncclSum, 0, comm, stream, /* Args */
ALLGATHER_CHUNKSTEPS, ALLGATHER_SLICESTEPS };
return ncclEnqueueCheck(&info);
}
NCCL_API(ncclResult_t, ncclAllReduce, const void* sendbuff, void* recvbuff, size_t count,
ncclDataType_t datatype, ncclRedOp_t op, ncclComm* comm, cudaStream_t stream);
ncclResult_t ncclAllReduce(const void* sendbuff, void* recvbuff, size_t count,
ncclDataType_t datatype, ncclRedOp_t op, ncclComm* comm, cudaStream_t stream) {
NVTX3_FUNC_WITH_PARAMS(AllReduce, NcclNvtxParamsAllReduce,
NVTX3_PAYLOAD(comm ? comm->commHash : 0, count * ncclTypeSize(datatype), op));
struct ncclInfo info = { ncclFuncAllReduce, "AllReduce",
sendbuff, recvbuff, count, datatype, op, 0, comm, stream, /* Args */
ALLREDUCE_CHUNKSTEPS, ALLREDUCE_SLICESTEPS };
return ncclEnqueueCheck(&info);
}
NCCL_API(ncclResult_t, ncclBroadcast, const void* sendbuff, void* recvbuff, size_t count, ncclDataType_t datatype, int root,
ncclComm_t comm, cudaStream_t stream);
ncclResult_t ncclBroadcast(const void* sendbuff, void* recvbuff, size_t count, ncclDataType_t datatype, int root,
ncclComm_t comm, cudaStream_t stream) {
NVTX3_FUNC_WITH_PARAMS(Broadcast, NcclNvtxParamsBroadcast,
NVTX3_PAYLOAD(comm ? comm->commHash : 0, count * ncclTypeSize(datatype), root));
struct ncclInfo info = { ncclFuncBroadcast, "Broadcast",
sendbuff, recvbuff, count, datatype, ncclSum, root, comm, stream, /* Args */
BROADCAST_CHUNKSTEPS, BROADCAST_SLICESTEPS };
return ncclEnqueueCheck(&info);
}
/* Deprecated original "in place" function, similar to MPI */
NCCL_API(ncclResult_t, ncclBcast, void* buff, size_t count, ncclDataType_t datatype, int root,
ncclComm_t comm, cudaStream_t stream);
ncclResult_t ncclBcast(void* buff, size_t count, ncclDataType_t datatype, int root,
ncclComm_t comm, cudaStream_t stream) {
return ncclBroadcast(buff, buff, count, datatype, root, comm, stream);
}
NCCL_API(ncclResult_t, ncclReduce, const void* sendbuff, void* recvbuff, size_t count,
ncclDataType_t datatype, ncclRedOp_t op, int root, ncclComm_t comm, cudaStream_t stream);
ncclResult_t ncclReduce(const void* sendbuff, void* recvbuff, size_t count,
ncclDataType_t datatype, ncclRedOp_t op, int root, ncclComm_t comm, cudaStream_t stream) {
NVTX3_FUNC_WITH_PARAMS(Reduce, NcclNvtxParamsReduce,
NVTX3_PAYLOAD(comm ? comm->commHash : 0, count * ncclTypeSize(datatype), root, op));
struct ncclInfo info = { ncclFuncReduce, "Reduce",
sendbuff, recvbuff, count, datatype, op, root, comm, stream, /* Args */
REDUCE_CHUNKSTEPS, REDUCE_SLICESTEPS };
return ncclEnqueueCheck(&info);
}
NCCL_API(ncclResult_t, ncclReduceScatter, const void* sendbuff, void* recvbuff, size_t recvcount,
ncclDataType_t datatype, ncclRedOp_t op, ncclComm* comm, cudaStream_t stream);
ncclResult_t ncclReduceScatter(const void* sendbuff, void* recvbuff, size_t recvcount,
ncclDataType_t datatype, ncclRedOp_t op, ncclComm* comm, cudaStream_t stream) {
NVTX3_FUNC_WITH_PARAMS(ReduceScatter, NcclNvtxParamsReduceScatter,
NVTX3_PAYLOAD(comm ? comm->commHash : 0, recvcount * ncclTypeSize(datatype), op));
struct ncclInfo info = { ncclFuncReduceScatter, "ReduceScatter",
sendbuff, recvbuff, recvcount, datatype, op, 0, comm, stream, /* Args */
REDUCESCATTER_CHUNKSTEPS, REDUCESCATTER_SLICESTEPS };
return ncclEnqueueCheck(&info);
}
NCCL_API(ncclResult_t, ncclSend, const void* sendbuff, size_t count, ncclDataType_t datatype, int peer,
ncclComm_t comm, cudaStream_t stream);
ncclResult_t ncclSend(const void* sendbuff, size_t count, ncclDataType_t datatype, int peer,
ncclComm_t comm, cudaStream_t stream) {
NVTX3_FUNC_WITH_PARAMS(Send, NcclNvtxParamsSendRecv,
NVTX3_PAYLOAD(comm ? comm->commHash : 0, count * ncclTypeSize(datatype), peer));
struct ncclInfo info = { ncclFuncSend, "Send",
NULL, (void*)sendbuff, count, datatype, ncclSum, peer, comm, stream, /* Args */
1, 1 };
return ncclEnqueueCheck(&info);
}
NCCL_API(ncclResult_t, ncclRecv, void* recvbuff, size_t count, ncclDataType_t datatype, int peer,
ncclComm_t comm, cudaStream_t stream);
ncclResult_t ncclRecv(void* recvbuff, size_t count, ncclDataType_t datatype, int peer,
ncclComm_t comm, cudaStream_t stream) {
NVTX3_FUNC_WITH_PARAMS(Recv, NcclNvtxParamsSendRecv,
NVTX3_PAYLOAD(comm ? comm->commHash : 0, count * ncclTypeSize(datatype), peer));
struct ncclInfo info = { ncclFuncRecv, "Recv",
NULL, recvbuff, count, datatype, ncclSum, peer, comm, stream, /* Args */
1, 1 };
return ncclEnqueueCheck(&info);
}

530
src/common_kernel.h Normal file
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/*************************************************************************
* Copyright (c) 2015-2016, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#ifndef COMMON_KERNEL_H_
#define COMMON_KERNEL_H_
#include <cstdio>
#include <cstdint>
#include <cuda_runtime.h>
// BAR macro and helpers
#define WARP_SIZE 32
#define ROUNDUP(x, y) \
(((((x) + (y) - 1) / (y))) * (y))
#define BAR_EXEC(type, barid, nthreads) \
asm("bar." #type " " #barid ", " #nthreads ";\n\t")
#define BAR_EXPAND(type, barid, nthreads) \
BAR_EXEC(type, barid, (nthreads))
// Named barrier macro.
// Expands to asm("bar.type barid, nthreads") where
// nthreads has been rounded up to WARP_SIZE.
#define BAR(type, barid, nthreads) \
BAR_EXPAND(type, barid, ROUNDUP(nthreads, WARP_SIZE))
__device__ unsigned int spinct;
// Spin wait until func evaluates to true
template<typename FUNC>
__device__ inline void Wait(const FUNC& func) {
while (!func()) {
// waste time
atomicInc(&spinct, 10);
}
}
typedef uint64_t PackType;
// unpack x and y to elements of type T and apply FUNC to each element
template<class FUNC, typename T>
struct MULTI {
__device__ PackType operator()(const PackType x, const PackType y) const;
};
template<class FUNC>
struct MULTI<FUNC, char> {
static_assert(sizeof(PackType) == 2 * sizeof(uint32_t),
"PackType must be twice the size of uint32_t.");
union converter {
PackType storage;
struct {
uint32_t a, b;
};
};
__device__ PackType operator()(const PackType x, const PackType y) const {
converter cx, cy, cr;
cx.storage = x;
cy.storage = y;
// for char, we do these as vector ops
cr.a = FUNC()(cx.a, cy.a);
cr.b = FUNC()(cx.b, cy.b);
return cr.storage;
}
};
template<class FUNC>
struct MULTI<FUNC, int> {
static_assert(sizeof(PackType) == 2 * sizeof(int),
"PackType must be twice the size of int.");
union converter {
PackType storage;
struct {
int a, b;
};
};
__device__ PackType operator()(const PackType x, const PackType y) const {
converter cx, cy, cr;
cx.storage = x;
cy.storage = y;
cr.a = FUNC()(cx.a, cy.a);
cr.b = FUNC()(cx.b, cy.b);
return cr.storage;
}
};
#ifdef CUDA_HAS_HALF
template<class FUNC>
struct MULTI<FUNC, half> {
static_assert(sizeof(PackType) == 2 * sizeof(float),
"PackType must be twice the size of float.");
union converter {
PackType storage;
struct {
half2 a, b;
};
};
__device__ PackType operator()(const PackType x, const PackType y) const {
converter cx, cy, cr;
cx.storage = x;
cy.storage = y;
cr.a = FUNC()(cx.a, cy.a);
cr.b = FUNC()(cx.b, cy.b);
return cr.storage;
}
};
#endif
template<class FUNC>
struct MULTI<FUNC, float> {
static_assert(sizeof(PackType) == 2 * sizeof(float),
"PackType must be twice the size of float.");
union converter {
PackType storage;
struct {
float a, b;
};
};
__device__ PackType operator()(const PackType x, const PackType y) const {
converter cx, cy, cr;
cx.storage = x;
cy.storage = y;
cr.a = FUNC()(cx.a, cy.a);
cr.b = FUNC()(cx.b, cy.b);
return cr.storage;
}
};
template<class FUNC>
struct MULTI<FUNC, double> {
static_assert(sizeof(PackType) == sizeof(double),
"PackType must be the same size as double.");
__device__ PackType operator()(const PackType x, const PackType y) const {
double rv = FUNC()(__longlong_as_double(x), __longlong_as_double(y));
return __double_as_longlong(rv);
}
};
template<class FUNC>
struct MULTI<FUNC, unsigned long long> {
static_assert(sizeof(PackType) == sizeof(unsigned long long),
"PackType must be the same size as unsigned long long.");
__device__ PackType operator()(const PackType x, const PackType y) const {
unsigned long long rv = FUNC()(x, y);
return rv;
}
};
template<class FUNC>
struct MULTI<FUNC, long long> {
static_assert(sizeof(PackType) == sizeof(long long),
"PackType must be the same size as long long.");
__device__ PackType operator()(const PackType x, const PackType y) const {
long long rv = FUNC()((long long)x, (long long)y);
return rv;
}
};
template<typename T, bool FETCHTWO>
__device__ inline void FetchOneOrTwo64b(PackType& s0,
const volatile T * __restrict__ const src0, PackType& s1,
const volatile T * __restrict__ const src1, const int idx) {
s0 = (reinterpret_cast<const volatile PackType *>(src0))[idx];
if (FETCHTWO) {
s1 = (reinterpret_cast<const volatile PackType *>(src1))[idx];
}
}
template<typename T, bool STORETWO>
__device__ inline void StoreOneOrTwo64b(volatile T * __restrict__ const dest0,
volatile T * __restrict__ const dest1, PackType val, const int idx) {
(reinterpret_cast<volatile PackType *>(dest0))[idx] = val;
if (STORETWO) {
(reinterpret_cast<volatile PackType *>(dest1))[idx] = val;
}
}
template<class FUNC, typename T, bool ISREDUCE>
__device__ inline PackType ReduceOrCopy64b(const PackType s0,
const PackType s1) {
if (ISREDUCE) {
return MULTI<FUNC, T>()(s0, s1);
} else {
return s0;
}
}
#define ALIGNUP(x, a) ((((x)-1) & ~((a)-1)) + (a))
template<typename T>
__device__ inline volatile T* AlignUp(volatile T * ptr, size_t align) {
size_t ptrval = reinterpret_cast<size_t>(ptr);
return reinterpret_cast<volatile T*>(ALIGNUP(ptrval, align));
}
template<typename T> inline __device__
T vFetch(const volatile T* ptr) {
return *ptr;
}
#ifdef CUDA_HAS_HALF
template<> inline __device__
half vFetch<half>(const volatile half* ptr) {
half r;
r.x = ptr->x;
return r;
}
#endif
template<typename T> inline __device__
void vStore(volatile T* ptr, const T val) {
*ptr = val;
}
#ifdef CUDA_HAS_HALF
template<> inline __device__
void vStore<half>(volatile half* ptr, const half val) {
ptr->x = val.x;
}
#endif
// Assumptions:
// - there is exactly 1 block
// - THREADS is the number of producer threads
// - this function is called by all producer threads
template<int UNROLL, int THREADS, class FUNC, typename T, bool HAS_DEST1,
bool HAS_SRC1>
__device__ inline void ReduceOrCopy(const int tid,
volatile T * __restrict__ dest0, volatile T * __restrict__ dest1,
const volatile T * __restrict__ src0, const volatile T * __restrict__ src1,
int N) {
if (N<=0) {
return;
}
const int UNROLL2 = (UNROLL >= 2) ? (UNROLL / 2) : 1;
const bool NOUNROLL2 = ((UNROLL / 2) == 0);
int Npreamble = (N<alignof(PackType)) ? N : AlignUp(dest0, alignof(PackType)) - dest0;
// stage 0: check if we'll be able to use the fast, 64-bit aligned path.
// If not, we'll just use the slow preamble path for the whole operation
bool alignable = (((AlignUp(src0, alignof(PackType)) == src0 + Npreamble)) &&
(!HAS_DEST1 || (AlignUp(dest1, alignof(PackType)) == dest1 + Npreamble)) &&
(!HAS_SRC1 || (AlignUp(src1, alignof(PackType)) == src1 + Npreamble)));
if (!alignable) {
Npreamble = N;
}
/*
if (threadIdx.x == 0) {
printf("** alignable: %s", (alignable ? "YES" : " NO"));
printf(", dest0 = 0x%08X", dest0);
printf(", src0 = 0x%08X", src0);
if (HAS_DEST1) printf(", dest1 = 0x%08X", dest1);
if (HAS_SRC1) printf(", src1 = 0x%08X", src1);
printf("\n");
}
*/
// stage 1: preamble: handle any elements up to the point of everything coming
// into alignment
for (int idx = tid; idx < Npreamble; idx += THREADS) {
// ought to be no way this is ever more than one iteration, except when
// alignable is false
T val = vFetch(src0+idx);
if (HAS_SRC1) {
val = FUNC()(val, vFetch(src1+idx));
}
vStore(dest0+idx, val);
if (HAS_DEST1) {
vStore(dest1+idx, val);
}
}
// reduce N by however many elements we've handled already
int Ndone = Npreamble;
int Nrem = N - Ndone;
// stage 2: fast path: use 64b loads/stores to do the bulk of the work,
// assuming the pointers we have are all 64-bit alignable.
if (alignable) {
if (Ndone > 0) {
// align up pointers
dest0 += Ndone; if (HAS_DEST1) { dest1 += Ndone; }
src0 += Ndone; if (HAS_SRC1) { src1 += Ndone; }
}
// stage 2a: main loop
int Nalign = (Nrem / (sizeof(PackType) / sizeof(T)) / (UNROLL * THREADS))
* (UNROLL * THREADS); // round down
#pragma unroll 1 // don't unroll this loop
for (int idx = tid; idx < Nalign; idx += UNROLL * THREADS) {
PackType t0[UNROLL2];
PackType t1[UNROLL2];
PackType t2[UNROLL2];
#pragma unroll
for (int j = 0; j < UNROLL2; ++j)
FetchOneOrTwo64b<T, HAS_SRC1>(t0[j], src0, t1[j], src1,
idx + j * THREADS);
#pragma unroll
for (int j = 0; j < UNROLL2; ++j)
t2[j] = ReduceOrCopy64b<FUNC, T, HAS_SRC1>(t0[j], t1[j]);
if (!NOUNROLL2) {
#pragma unroll
for (int j = 0; j < UNROLL2; ++j)
FetchOneOrTwo64b<T, HAS_SRC1>(t0[j], src0, t1[j], src1,
idx + (UNROLL2 + j) * THREADS);
}
#pragma unroll
for (int j = 0; j < UNROLL2; ++j)
StoreOneOrTwo64b<T, HAS_DEST1>(dest0, dest1, t2[j], idx + j * THREADS);
if (!NOUNROLL2) {
#pragma unroll
for (int j = 0; j < UNROLL2; ++j)
t2[j] = ReduceOrCopy64b<FUNC, T, HAS_SRC1>(t0[j], t1[j]);
#pragma unroll
for (int j = 0; j < UNROLL2; ++j)
StoreOneOrTwo64b<T, HAS_DEST1>(dest0, dest1, t2[j],
idx + (UNROLL2 + j) * THREADS);
}
}
// stage 2b: slightly less optimized for section when we don't have full
// UNROLLs
int Ndone2a = Nalign * (sizeof(PackType)/sizeof(T));
Ndone += Ndone2a;
Nrem = N - Ndone;
// TODO: This kind of pointer update arithmetic is expensive. Should
// probably find a better way.
if (Nrem > 0) {
dest0 += Ndone2a; if (HAS_DEST1) { dest1 += Ndone2a; }
src0 += Ndone2a; if (HAS_SRC1) { src1 += Ndone2a; }
}
Nalign = Nrem / (sizeof(PackType)/sizeof(T));
#pragma unroll 4
for (int idx = tid; idx < Nalign; idx += THREADS) {
PackType t0, t1, t2;
FetchOneOrTwo64b<T, HAS_SRC1>(t0, src0, t1, src1, idx);
t2 = ReduceOrCopy64b<FUNC, T, HAS_SRC1>(t0, t1);
StoreOneOrTwo64b<T, HAS_DEST1>(dest0, dest1, t2, idx);
}
// stage 2c: tail
int Ndone2b = Nalign * (sizeof(PackType)/sizeof(T));
Ndone += Nalign * (sizeof(PackType)/sizeof(T));
Nrem = N - Ndone;
if (Nrem > 0) {
dest0 += Ndone2b; if (HAS_DEST1) { dest1 += Ndone2b; }
src0 += Ndone2b; if (HAS_SRC1) { src1 += Ndone2b; }
}
for (int idx = tid; idx < Nrem; idx += THREADS) {
// never ought to make it more than one time through this loop. only a
// few threads should even participate
T val = vFetch(src0+idx);
if (HAS_SRC1) {
val = FUNC()(val, vFetch(src1+idx));
}
vStore(dest0+idx, val);
if (HAS_DEST1) {
vStore(dest1+idx, val);
}
}
} // done fast path
}
template<int THREADS, int UNROLL, typename T>
__device__ inline void CalcLastChunk(int * const bigSliceN,
int * const smallSliceN, int * const lastSliceN, int * const numSlices,
int * const numBigSlices, int * const numSmallSlices, const int N,
const int numChunks, const int chunkSize) {
int Nleft = N - ((numChunks - 1) * chunkSize);
// semi-equally split up the remaining work into numslices slices.
// it's "semi"-equal because we want the divisions to land as neatly as we
// can on alignable boundaries
int NperTile = UNROLL * THREADS * (sizeof(PackType)/sizeof(T));
int numTiles = (Nleft + NperTile - 1) / NperTile;
int numTilesPerBigSlice = (numTiles + *numSlices - 1)
/ *numSlices;
int numTilesPerSmallSlice = numTiles / *numSlices;
*bigSliceN = NperTile * numTilesPerBigSlice;
*smallSliceN = NperTile * numTilesPerSmallSlice;
*numBigSlices = numTiles % *numSlices;
*numSmallSlices = (*smallSliceN > 0) ?
*numSlices - *numBigSlices : 0;
// the lastSlice will take the place of one of the small slices unless
// there are no small slices (because this is a very small reduction), in
// which case we replace one of the big slices and leave the small slices
// as 0.
if (*numSmallSlices > 0) {
--*numSmallSlices;
if (*numSmallSlices == 0)
*smallSliceN = 0;
}
else {
--*numBigSlices;
if (*numBigSlices == 0)
*bigSliceN = 0;
}
*lastSliceN = Nleft -
(*numBigSlices * *bigSliceN
+ *numSmallSlices * *smallSliceN);
// in cases where args.N % numSlices is pretty small, we'd rather have one
// slightly big last slice than one big slice, a bunch of small slices,
// and one smaller last slice
if ((*numBigSlices == 1) &&
(*numSmallSlices == *numSlices - 2) &&
(*lastSliceN < *smallSliceN)) {
*numBigSlices += *numSmallSlices;
*numSmallSlices = 0;
*bigSliceN = *smallSliceN;
*smallSliceN = 0;
*lastSliceN = Nleft -
*numBigSlices * *bigSliceN;
}
// done recalculating
*numSlices = *numBigSlices +
*numSmallSlices + 1;
}
// Kernel launch
template<typename T>
struct KernelArgs {
// general parameters
int nRanks;
int root;
int buffSize;
int N;
int opIndex;
volatile int * __restrict__ opCounter;
bool pushrecv;
// some pre-computed sizes
int SliceSize;
int SliceOffset;
int ChunkSize;
int NumChunks;
// local and remote input, output, and buffer
const T * __restrict__ ThisInput;
T * __restrict__ ThisOutput;
DevRing<char>* ring;
};
template<typename T>
void ArgsSetup(KernelArgs<T> *args, const void* sendbuff, void* recvbuff,
const int root, const int count, ncclComm *comm) {
args->nRanks = comm->nRanks;
args->root = root;
args->buffSize = comm->buffSize;
args->N = count;
args->opIndex = comm->opSched;
args->opCounter = comm->opCounter;
args->ThisInput = (const T*)sendbuff;
args->ThisOutput = (T*)recvbuff;
args->ring = comm->devRing;
args->pushrecv = comm->globalMemSpace;
}
#define LAUNCH_KERNEL(K, THREADS, UNROLL, FUNC, T, \
args, stream) do { \
dim3 grid(1, 1, 1); \
dim3 block(THREADS+1, 1, 1); \
void* argptrs[] = {&args}; \
CUDACHECK(cudaLaunchKernel( \
(void*)K<THREADS, UNROLL, FUNC, T>, \
grid, block, argptrs, 0, stream)); \
} while (0)
template <typename T>
__device__ inline void incrementOpCounter(const KernelArgs<T> *args) {
// increment comm's operation counts
__threadfence_system(); // Technically need to ensure that cleared flags
// are visible before incrementing op counter.
*args->opCounter = args->opIndex+1;
}
template <int THREADS, typename T> __device__ __forceinline__
void LoadRing(const DevRing<char>* src, DevRing<T>* dst) {
enum { NUM_WORDS = sizeof(DevRing<char>) / sizeof(long long) };
static_assert(sizeof(DevRing<char>) % sizeof(long long) == 0, "Bad alignment");
static_assert(THREADS >= NUM_WORDS, "Not enough threads to load DevRing");
static_assert(sizeof(DevRing<char>) == sizeof(DevRing<T>), "DevRing size mismatch");
long long* lldst = reinterpret_cast<long long*>(dst);
const long long* llsrc = reinterpret_cast<const long long*>(src);
if (threadIdx.x < NUM_WORDS) {
lldst[threadIdx.x] = llsrc[threadIdx.x];
}
}
#endif // COMMON_KERNEL_H_

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/*************************************************************************
* Copyright (c) 2015, NVIDIA CORPORATION. All rights reserved.
*
* See LICENSE.txt for license information
************************************************************************/
#ifndef COPY_KERNEL_H_
#define COPY_KERNEL_H_
#include "common_kernel.h"
template<typename T>
struct FuncPassA {
__device__ T operator()(const T x, const T y) const {
return x;
}
};
#ifdef CUDA_HAS_HALF
template <>
struct FuncPassA<half> {
__device__ half2 operator()(const half2 x, const half2 y) const {
return x;
}
__device__ half operator()(const half x, const half y) const {
half r;
r.x = x.x;
return r;
}
};
#endif
// Assumptions:
// - there is exactly 1 block
// - THREADS is the number of producer threads
// - this function is called by all producer threads
template<int UNROLL, int THREADS, typename T>
__device__ void Copy(volatile T * __restrict__ const dest,
const volatile T * __restrict__ const src, const int N) {
ReduceOrCopy<UNROLL, THREADS, FuncPassA<T>, T, false, false>(threadIdx.x,
dest, nullptr, src, nullptr, N);
}
// Assumptions:
// - there is exactly 1 block
// - THREADS is the number of producer threads
// - this function is called by all producer threads
template<int UNROLL, int THREADS, typename T>
__device__ void DoubleCopy(volatile T * __restrict__ const dest0,
volatile T * __restrict__ const dest1,
const volatile T * __restrict__ const src, const int N) {
ReduceOrCopy<UNROLL, THREADS, FuncPassA<T>, T, true, false>(threadIdx.x,
dest0, dest1, src, nullptr, N);
}
#endif // COPY_KERNEL_H_

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