Optimized primitives for collective multi-GPU communication migrated to Mac OS X (10.13 - 10.13.6). In order to make library and nccl-test compatible, each nccl library version will be unique mapped to nccl-test on macOS version. When you start your work from my version, please also keep in mind.
- nccl-2.5.7-1-release should be tested by nccl-tests-v2.0.0 on mac.
- nccl-2.5.8-release should be tested by nccl-tests-v2.0.0 on mac. This is the first major release which fixes IPv6 address issue on macOS and enables nccl to run for TF/JAX. You can also use Jax pmap notebook for further client testing.
- nccl-2.9.6-release should be tested by nccl-tests-v2.11.0 on mac, as new operations of nccl have been added for nvidia nccl-2.9.6-1. Currently, orlando still has to use a workaround in MIGRATION.md to create /sys/class/0000:c4:00.0/, /sys/class/0000:45:00.0/ and /sys/devices/system/node/node in local computer.
Why do we need NCCL on Mac OS X?
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Because when using pytorch-osx-build, Tom found some objection detection frameworks use distributed GPU training, which requires at least one distributed GPU backend functional. GPU backends of Pytorch consists of NCCL and GLOO. GLOO is dependent of NCCL. Thus, we need NCCL. With the NCCL migration, GLOO can be compiled on Mac OS X and works fine as a distributed GPU backend of Pytorch. However, using of NCCL backend of Pytorch will fail at "unhandled system error" and I figured out the cause is many nccl codes heavily coupled with ubuntu system. That is why I was motivated to migrate nccl library fully support for macOS, because it's a very good opportunity to learn how topo system of GPU resources are managed by NCCL framework and also understand dependencies between CUDA and NCCL.
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Another drive behind this project is supporting XLA in JAX on macOS cuda. One of the essential functionalities of JAX is to leverage XLA to bridge with NCCL under GPU. Currently Tom already finished one ported Tensorflow version in tensorflow-osx-build and the provided XLA in this module could greatly reduce my effort to enable XLA for JAX on macOS cuda. But I have to let XLA bridge on macOS cuda working through, which is another reason why I forked this repository for continuing with Tom's work.
This migration is fully functional after Orlando's effort for adding functionalities required on macOS. The relevant works are mainly listed as follow:
- construct libnvidia-smi.dylib and drafts basic functionalities. Fortunately, as my devices is GeoForce 1080 which can't support NVLink and Cuda exposes peer2peer API, I can manage to construct a simple wrapper library without including events monitor logic in NVML. One more clarification also raised up to Nvidia team, refer to https://forums.developer.nvidia.com/t/how-to-use-cuda-api-to-implement-nvlink-query/189191.
- migrate thread synchronization and shared memory allocation on macOS
- disable system check for /sys/dev check on macOS [in NCCL version > 2.5.7, the team has already started work to eliminate such dependency. I will follow-up later]
With built nccl library that passed all nccl test in nccl-test on mac, we can utilize it on macOS 10.13.6.ß
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It helps enable distributed GPU training for pytorch-osx-build through GLOO backend.
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Orlando could enable JAX working with XLA fully
Another important information is that Orlando changed several implementation of https://github.com/TomHeaven/nccl-osx and my next step is to migrate the latest release version v2.11.4.-1. Anyone who tries to refine my work please checkout my master, which is ahead of tom's original version. About next release for v2.11.4-1, I may create a new branch develop checked-out from nccl main repository. As originated from different source code, the compatibility between 2.5.7 and 2.11.4 may not be guaranteed.
NCCL (pronounced "Nickel") is a stand-alone library of standard collective communication routines for GPUs, implementing all-reduce, all-gather, reduce, broadcast, and reduce-scatter. 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.
At present, the library implements the following collectives operations, which have all passed nccl-test migrated from linux:
- all-reduce 【passed】
- all-gather 【passed】
- reduce-scatter 【passed】
- reduce 【passed】
- broadcast 【passed】
- sendrecv 【passed】
- scatter 【passed】
- hypercube 【passed】
- alltoall 【passed】
You can easily reproduce my result from https://github.com/llv22/nccl-tests-macOS-cuda. The latest test results with my two GeForce 1080, macOS 10.13.6, cuda10.1 + cudnn7.3.6 as follow:
These operations are implemented using ring algorithms and have been optimized for throughput and latency. For best performance, small operations can be either batched into larger operations or aggregated through the API.
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NCCL-macOS 2.5.7.1 release full is the first milestone which finishes the migration from linux to ubuntu system.
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NCCL-macOS 2.5.7.2 release full fixes the issue of initializing nccl library on macOS during setup of the socket server.
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NCCL-macOS 2.9.6 release full supports ncclSend, SendRecv, HpyerCube and other new operations since 2.7.x. This library fully support for jax 0.2.21 and TF 2.4.
- NCCL 2.9.7 aims at removing workaround for devices detection during construction of topo system.
NCCL requires at least CUDA 7.0 and Kepler or newer GPUs. For PCIe based platforms, best performance is achieved when all GPUs are located on a common PCIe root complex, but multi-socket configurations are also supported.
To install NCCL on Mac OS X 10.13, first ensure Homebrew, XCode 9(.4.1) and CUDA-SDK (10.0 or 10.1) are properly installed.
Currently I used XCode 10.1, CUDA-SDK(10.1), makefile installed by Homebrew for compiling. Base line version: https://github.com/NVIDIA/nccl/blob/v2.5.7-1
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.
1, For generating libraries based on gcc
To build the library :
cd nccl
make -j src.buildIf 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"2, For generating libraries based on clang++
To build the fat library compatible with -std=c++14, as this version is consistent with JAX:
version=2.9.6.1
make src.build CXX=clang++ -j8
make src.build CXX=clang++ PREFIX=/Users/llv23/Documents/05_machine_learning/dl_gpu_mac/drivers_mac/nccl-osx/nccl-${version} -j12
make src.build CXX=clang++ PREFIX=/Users/llv23/Documents/05_machine_learning/dl_gpu_mac/drivers_mac/nccl-osx/nccl-${version} TRACE=1 -j12 #enable trace for compiling, about 16 minutes
make src.build CXX=clang++ PREFIX=/Users/llv23/Documents/05_machine_learning/dl_gpu_mac/drivers_mac/nccl-osx/nccl-${version} TRACE=1 NVCC_GENCODE="-gencode=arch=compute_61,code=sm_61" -j12 #enable trace for compiling only for GTX1080, refer to https://arnon.dk/matching-sm-architectures-arch-and-gencode-for-various-nvidia-cards/, about 5 minutesCurrent release version: nccl-2.9.6, refer to the branch dev-2.9.6-for-jax
1, install on default local /usr/local
make install2, install on customized location $PREFIX
version=2.9.6
make install PREFIX=/usr/local/nccl-${version}Install on local folder
version=2.9.6
make install PREFIX=/Users/llv23/Documents/05_machine_learning/dl_gpu_mac/drivers_mac/nccl-osx/nccl-${version}1, why additional dylib for macOS is needed?
2, NCCL 2.9.6 development guideline (Currently Orlando used 2.9.6-1 as based line, but documentation should keep consistent with 2.9.6)
- As the first milestone aims at migration of source code of 2.9.6 and fully support nccl-tests, going through the development documentation of 2.9.6 becomes a must-have
- General development guide, refer to https://docs.nvidia.com/deeplearning/nccl/archives/nccl_296/user-guide/docs/index.html
- About enabling tracing and logging for debugging purposes, refer to NVIDIA/nccl#197 and environment variable for controlling log of nccl 2.9.6 in https://docs.nvidia.com/deeplearning/nccl/archives/nccl_296/user-guide/docs/env.html. Currently logging mechanism is clear but TRACE isn't still not working.
All source code and accompanying documentation is copyright (c) 2015-2019, NVIDIA CORPORATION. All rights reserved.
Migration to Mac OS X is done by Orlando.
I sincerely appreciate the effort of TomHeaven, but the implementations between Tom and I already diverged after 10-15 commits, especially when deciding to implement a simplified version of libnvidia-ml.dylib from scratch.