ROCm Communication Collectives Library
RCCL (pronounced "Rickle") is a stand-alone library of standard collective communication routines for GPUs, implementing all-reduce, all-gather, reduce, broadcast, reduce-scatter, gather, scatter, and all-to-all. There is also initial support for direct GPU-to-GPU send and receive operations. It has been optimized to achieve high bandwidth on platforms using PCIe, xGMI as well as networking using InfiniBand Verbs or TCP/IP sockets. RCCL supports an arbitrary number of GPUs installed in a single node or multiple nodes, and can be used in either single- or multi-process (e.g., MPI) applications.
The collective operations are implemented using ring and tree 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.
- ROCm supported GPUs
- ROCm stack installed on the system (HIP runtime & HCC or HIP-Clang)
RCCL directly depends on HIP runtime, plus the HCC C++ compiler or the HIP-Clang compiler which are part of the ROCm software stack. For ROCm installation instructions, see https://github.com/RadeonOpenCompute/ROCm.
The root of this repository has a helper script 'install.sh' to build and install RCCL on Ubuntu with a single command. It does not take a lot of options and hard-codes configuration that can be specified through invoking cmake directly, but it's a great way to get started quickly and can serve as an example of how to build/install.
./install.sh-- builds library including rccl unit tests./install.sh -i-- builds and installs the library to /opt/rocm/rccl; installation path can be changed with --prefix argument (see below.)./install.sh -d-- installs all necessary dependencies for RCCL. Should be re-invoked if the build folder is removed../install.sh -h-- shows help./install.sh -t-- builds library including rccl unit tests./install.sh -r-- runs rccl unit tests (must be already built)./install.sh -p-- builds RCCL package./install.sh -s-- builds RCCL as a static library (default: shared)./install.sh -hcc-- builds RCCL with hcc compiler; note that hcc is now deprecated. (default:hip-clang)./install.sh --prefix-- specify custom path to install RCCL to (default:/opt/rocm)./install.sh --npkit-enable-- enable compilation of npkit profiler framework with all options
$ git clone https://github.com/ROCmSoftwarePlatform/rccl.git
$ cd rccl
$ mkdir build
$ cd build
$ CXX=/opt/rocm/bin/hipcc cmake -DCMAKE_PREFIX_PATH=/opt/rocm/ ..
$ make -jYou may substitute an installation path of your own choosing by passing CMAKE_INSTALL_PREFIX. For example:
$ CXX=/opt/rocm/bin/hipcc cmake -DCMAKE_PREFIX_PATH=/opt/rocm/ -DCMAKE_INSTALL_PREFIX=$PWD/rccl-install ..Note: ensure rocm-cmake is installed, apt install rocm-cmake.
Assuming you have already cloned this repository and built the library as shown in the previous section:
$ cd rccl/build
$ make package
$ sudo dpkg -i *.debRCCL package install requires sudo/root access because it creates a directory called "rccl" under /opt/rocm/. This is an optional step and RCCL can be used directly by including the path containing librccl.so.
Please follow the instructions below to build the documentation.
cd docs
pip3 install -r .sphinx/requirements.txt
python3 -m sphinx -T -E -b html -d _build/doctrees -D language=en . _build/htmlIn order to enable peer-to-peer access on machines with PCIe-connected GPUs, the HSA environment variable HSA_FORCE_FINE_GRAIN_PCIE=1 is required to be set, on top of requiring GPUs that support peer-to-peer access and proper large BAR addressing support.
There are rccl unit tests implemented with the Googletest framework in RCCL. The rccl unit tests require Googletest 1.10 or higher to build and execute properly (installed with the -d option to install.sh). To invoke the rccl unit tests, go to the build folder, then the test subfolder, and execute the appropriate rccl unit test executable(s).
rccl unit test names are now of the format:
CollectiveCall.[Type of test]
Filtering of rccl unit tests should be done with environment variable and by passing the --gtest_filter command line flag, for example:
UT_DATATYPES=ncclBfloat16 UT_REDOPS=prod ./rccl-UnitTests --gtest_filter="AllReduce.C*"will run only AllReduce correctness tests with float16 datatype. A list of available filtering environment variables appears at the top of every run. See "Running a Subset of the Tests" at https://chromium.googlesource.com/external/github.com/google/googletest/+/HEAD/googletest/docs/advanced.md for more information on how to form more advanced filters.
There are also other performance and error-checking tests for RCCL. These are maintained separately at https://github.com/ROCmSoftwarePlatform/rccl-tests. See the rccl-tests README for more information on how to build and run those tests.
RCCL integrates NPKit, a profiler framework that enables collecting fine-grained trace events in RCCL components, especially in giant collective GPU kernels.
Please check NPKit sample workflow for RCCL as a fully automated usage example. It also provides good templates for the following manual instructions.
To manually build RCCL with NPKit enabled, pass -DNPKIT_FLAGS="-DENABLE_NPKIT -DENABLE_NPKIT_...(other NPKit compile-time switches)" with cmake command. All NPKit compile-time switches are declared in the RCCL code base as macros with prefix ENABLE_NPKIT_, and they control which information will be collected. Also note that currently NPKit only supports collecting non-overlapped events on GPU, and -DNPKIT_FLAGS should follow this rule.
To manually run RCCL with NPKit enabled, environment variable NPKIT_DUMP_DIR needs to be set as the NPKit event dump directory. Also note that currently NPKit only supports 1 GPU per process.
To manually analyze NPKit dump results, please leverage npkit_trace_generator.py.
Please refer to the Library documentation for current documentation.
Run the steps below to build documentation locally.
cd docs
pip3 install -r .sphinx/requirements.txt
python3 -m sphinx -T -E -b html -d _build/doctrees -D language=en . _build/html
All source code and accompanying documentation is copyright (c) 2015-2022, NVIDIA CORPORATION. All rights reserved.
All modifications are copyright (c) 2019-2022 Advanced Micro Devices, Inc. All rights reserved.