Sylvain Jeaugey
60240fec77
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.
[ROCm/rccl commit: 68b542363f]
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.