e261ecea18
Add scalable init API * Add new ncclCommInitRankScalable to allow for passing multiple unique IDs to the init function. * Spreads the load onto multiple bootstrap roots, allowing for constant bootstrap time. * Requires multiple ranks to create a unique ID, and the CPU-side ID exchange code to call allgather[v] instead of broadcast. Accelerate init bootstrap operations * Reduce the number of calls to allgather. * Allow roots to reply early to ranks when information is already available. * Add an option to use ncclNet instead of sockets to perform bootstrap allgather operations. Add PAT algorithms for Allgather and ReduceScatter * Parallel Aggregated Trees, variation of Bruck algorithm. * Logarithmic number of network steps for small sizes at scale. * Only supports one rank per node at the moment. Add support for registered buffers for intra-node communication. * Allow registered user buffers to be accessed directly intra-node * Avoids extra copies in algorithms which permit it, saving memory bandwidth and helping with compute overlap. Add profiler plugin API * New plugin API for profiling * Supports various levels of profiling, with a hierarchy. Asynchronous graph allocation * Make calls to cudaMalloc and cudaMemcpy during graph allocation asynchronous. * Significantly speeds up graph capture. Use fatal IB asynchronous events to stop network operation * Avoids many other error messages * Only fatal errors are affected; potentially transient errors (e.g. port down) do not cause an immediate stop. Set P2P level to PXB on AMD CPUs when using more than 2 GPUs per node * P2P would cause a significant performance degradation when using many GPUs, and therefore many interleaved data flows. * Disable P2P through the CPU when we have 3+ GPUs per node; keep it enabled when we only have 2 GPUs. Improve the init logs to report the real NCCL function. * Make the log report ncclCommInitRank or ncclCommSplit, rather than the generic ncclCommInitRankFunc. Add a parameter to set the location of the user configuration file. * Add NCCL_CONF_FILE environment variable to set where the user's configuration file resides. Increase default IB timeout * Increase IB timeout value from 18 to 20. * Should help avoid fatal errors on large RoCE systems. Add new check for nvidia peermem * On linux kernels 6.6+, /sys/kernel/mm/memory_peers is no longer present; check for /sys/module/nvidia_peermem/version instead. Fix old performance regression when mixing small and large operations. * Improves distribution of work on channels. Fix crash when NUMA IDs are equal to -1. * Can happen when a NIC is a virtual NIC, or when linux doesn't know which NUMA node a device is attached to * Issue NVIDIA/nccl-tests#233 Fix tree graph search when NCCL_CROSS_NIC is set to 1. * Would force NCCL to use the balanced_tree pattern, thereby disabling LL128 on platforms with 1 GPU+1 NIC per PCI switch. * Would also try to use alternate rings even though it was not needed. Compiler tweaks and fixes * PR #1177 * PR #1228 Fix stack smash * PR #1325 Fixes for multi-node NVLink + IB operation Coverity fixes and comments.
NCCL
Optimized primitives for inter-GPU communication.
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.
For more information on NCCL usage, please refer to the NCCL documentation.
Build
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.
To build the library :
$ cd nccl
$ make -j src.build
If CUDA is not installed in the default /usr/local/cuda path, you can define the CUDA path with :
$ make src.build CUDA_HOME=<path to cuda install>
NCCL will be compiled and installed in build/ unless BUILDDIR is set.
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 :
$ make -j src.build NVCC_GENCODE="-gencode=arch=compute_70,code=sm_70"
Install
To install NCCL on the system, create a package then install it as root.
Debian/Ubuntu :
$ # 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 :
$ # 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 :
$ make pkg.txz.build
$ ls build/pkg/txz/
Tests
Tests for NCCL are maintained separately at https://github.com/nvidia/nccl-tests.
$ 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.