3ea7eedf3b
Improvements for GB200 systems * Optimize the network performance by alternating the direction of the rings and the NIC to GPU assignment across communicators to limit unnecessary sharing. * Fix the detection of C2C links in case GPU Direct RDMA is disabled between a GPU and a NIC. * Fix PXN support on MNNVL systems, where NCCL would try (and fail) to share regular host memory across multiple nodes. * Fix P2C (PXN over C2C), which is now preferred over regular PXN. This support is currently preliminary and is disabled by default; use NCCL_PXN_C2C=1 to enable. Further reduce the overheads of CUDA graph capturing, which increased in NCCL 2.26.2 for large graphs. Optimize the network performance on DGX B200 systems by adjusting the bandwidths provided to the graph search algorithm. Enable fp8 reductions in symmetric kernels on Blackwell with CUDA 12.8. Restore the plugin name handling logic to make it possible to specify a path to the plugin (Issue #1732). Restore the ability to change NCCL_COLLNET_ENABLE during execution (Issue #1741). Add an example tuner plugin with CSV-based overrides. Remove an x86 dependency from the example profiler.
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.