Ke Wen 7e51592129 2.10.3-1
Add support for bfloat16.
Add ncclAvg reduction operation.
Improve performance for aggregated operations.
Improve performance for tree.
Improve network error reporting.
Add NCCL_NET parameter to force a specific network.
Add NCCL_IB_QPS_PER_CONNECTION parameter to split IB traffic onto multiple queue pairs.
Fix topology detection error in WSL2.
Fix proxy memory elements affinity (improve alltoall performance).
Fix graph search on cubemesh topologies.
Fix hang in cubemesh during NVB connections.
2021-07-08 14:30:14 -07:00
2021-07-08 14:30:14 -07:00
2021-07-08 14:30:14 -07:00
2021-07-08 14:30:14 -07:00
2021-07-08 14:30:14 -07:00
2018-09-25 14:12:01 -07:00
2021-02-09 15:36:48 -08:00
2019-04-05 13:05:45 -07:00
2021-02-09 15:36:48 -08:00

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>

All source code and accompanying documentation is copyright (c) 2015-2020, NVIDIA CORPORATION. All rights reserved.

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