Add support for IB SHARP 1PPN operation with user buffers. Improve support for MNNVL, add NVLS support and multi-clique support. * Detect the NVLS clique through NVML * Exchange XML between peers in the same NVLS clique and fuse XMLs before creating the topology graph. * Rework bootstrap allgather algorithms to allow for large allgather operations intra-node (XML exchange). Net/IB: add support for dynamic GID detection. * Automatically select RoCEv2/IPv4 interface by default. Allow to select IPv6 or even the network/mask. Reduce NVLS memory usage. * Add stepSize as property of a connection to allow for different sizes on different peers; set it to 128K for NVLink SHARP. Improve tuner loading * Look for more paths, be more consistent with the network device plugin. * Also search for tuner support inside the net plugin. Improve tuner API * Add context to support multi-device per process. Add magic number around comm object to detect comm corruption. * Add some basic check around communicators so that we can report a problem when a communicator gets corrupted or a wrong comm pointer is passed to NCCL. Fix net/IB error path. Github PR #1164 Fix collnet rail mapping with split comm. Fix packet reordering issue causing bootstrap mismatch * Use a different tag in ncclTransportP2pSetup for the connectInfo exchange and the following barrier. Fix hang when crossNic is inconsistent between ranks. Fix minCompCap/maxCompCap computation. Github issue #1184
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.