Optimized primitives for inter-GPU communication.

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.

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_90,code=sm_90"

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/

Python wheel :

$ # Install uv to create the Python wheel (uv manages Python deps in a venv)
$ # See: https://docs.astral.sh/uv/getting-started/installation/
$ curl -LsSf https://astral.sh/uv/install.sh | sh
$ # Build NCCL Python wheel (this also builds the .txz archive as an intermediate)
$ make pkg.python_wheel.build
$ ls build/pkg/python_wheel/

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.