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.
The root of this repository has a helper script `install.sh` to build and install RCCL with a single command. It hard-codes configurations 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 RCCL.
### To build the library using the install script:
--amdgpu_targets Only compile for specified GPU architecture(s). For multiple targets, seperate by ';'(builds for all supported GPU architectures by default)
RCCL 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.
In 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).
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.
RCCL integrates [NPKit](https://github.com/microsoft/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](https://github.com/microsoft/NPKit/tree/main/rccl_samples) 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](https://github.com/microsoft/NPKit/blob/main/rccl_samples/npkit_trace_generator.py).
RCCL integrates MSCCL(https://github.com/microsoft/msccl) and MSCCL++ (https://github.com/microsoft/mscclpp) to leverage the highly efficient GPU-GPU communication primitives for collective operations. Thanks to Microsoft Corporation for collaborating with us in this project.
MSCCL uses XMLs for different collective algorithms on different architectures. RCCL collectives can leverage those algorithms once the corresponding XML has been provided by the user. The XML files contain the sequence of send-recv and reduction operations to be executed by the kernel. On MI300X, MSCCL is enabled by default. On other platforms, the users may have to enable this by setting `RCCL_MSCCL_FORCE_ENABLE=1`.
On the other hand, RCCL allreduce and allgather collectives can leverage the efficient MSCCL++ communication kernels for certain message sizes. MSCCL++ support is available whenever MSCCL support is available. Users need to set the RCCL environment variable `RCCL_ENABLE_MSCCLPP=1` to run RCCL workload with MSCCL++ support. It is also possible to set the message size threshold for using MSCCL++ by using the environment variable `RCCL_MSCCLPP_THRESHOLD`. Once `RCCL_MSCCLPP_THRESHOLD` (the default value is 1MB) is set, RCCL will invoke MSCCL++ kernels for all message sizes less than or equal to the specified threshold.
