Jeffrey Novotny
bf7c130631
* Refactor RCCL install guide into several pages * Changes from code review and new docker guide * Add missing entries to ToC * Minor fixes * Fix help strings * Edits after review and remove extra white space
103 строки
5.0 KiB
ReStructuredText
103 строки
5.0 KiB
ReStructuredText
.. meta::
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:description: Usage tips for the RCCL library of collective communication primitives
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:keywords: RCCL, ROCm, library, API, peer-to-peer, transport
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.. _rccl-usage-tips:
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*****************************************
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RCCL usage tips
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*****************************************
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This topic describes some of the more common RCCL extensions, such as NPKit and MSCCL, and provides tips on how to
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configure and customize the application.
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NPKit
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=====
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RCCL integrates `NPKit <https://github.com/microsoft/npkit>`_, a profiler framework that
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enables the collection of fine-grained trace events in RCCL components, especially in giant collective GPU kernels.
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See the `NPKit sample workflow for RCCL <https://github.com/microsoft/NPKit/tree/main/rccl_samples>`_ for
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a fully-automated usage example. It also provides useful templates for the following manual instructions.
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To manually build RCCL with NPKit enabled, pass ``-DNPKIT_FLAGS="-DENABLE_NPKIT -DENABLE_NPKIT_...(other NPKit compile-time switches)"`` to the ``cmake`` command.
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All NPKit compile-time switches are declared in the RCCL code base as macros with the prefix ``ENABLE_NPKIT_``.
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These switches control the information that is collected.
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.. note::
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NPKit only supports the collection of non-overlapped events on the GPU.
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The ``-DNPKIT_FLAGS`` settings must follow this rule.
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To manually run RCCL with NPKit enabled, set the environment variable ``NPKIT_DUMP_DIR``
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to the NPKit event dump directory. NPKit only supports one GPU per process.
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To manually analyze the NPKit dump results, use `npkit_trace_generator.py <https://github.com/microsoft/NPKit/blob/main/rccl_samples/npkit_trace_generator.py>`_.
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MSCCL/MSCCL++
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=============
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RCCL integrates `MSCCL <https://github.com/microsoft/msccl>`_ and `MSCCL++ <https://github.com/microsoft/mscclpp>`_ to
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leverage these highly efficient GPU-GPU communication primitives for collective operations.
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Microsoft Corporation collaborated with AMD for this project.
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MSCCL uses XMLs for different collective algorithms on different architectures.
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RCCL collectives can leverage these algorithms after the user provides the corresponding XML.
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The XML files contain sequences of send-recv and reduction operations for the kernel to run.
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MSCCL is enabled by default on the AMD Instinct™ MI300X accelerator. On other platforms, users might have to enable it
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using the setting ``RCCL_MSCCL_FORCE_ENABLE=1``. By default, MSCCL is only used if every rank belongs
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to a unique process. To disable this restriction for multi-threaded or single-threaded configurations,
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use the setting ``RCCL_MSCCL_ENABLE_SINGLE_PROCESS=1``.
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RCCL allreduce and allgather collectives can leverage the efficient MSCCL++ communication kernels
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for certain message sizes. MSCCL++ support is available whenever MSCCL support is available.
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To run a RCCL workload with MSCCL++ support, set the following RCCL environment variable:
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.. code-block:: shell
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RCCL_MSCCLPP_ENABLE=1
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To set the message size threshold for using MSCCL++, use the environment variable ``RCCL_MSCCLPP_THRESHOLD``,
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which has a default value of 1MB. After ``RCCL_MSCCLPP_THRESHOLD`` has been set,
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RCCL invokes MSCCL++ kernels for all message sizes less than or equal to the specified threshold.
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The following restrictions apply when using MSCCL++. If these restrictions are not met,
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operations fall back to using MSCCL or RCCL.
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* The message size must be a non-zero multiple of 32 bytes
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* It does not support ``hipMallocManaged`` buffers
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* Allreduce only supports the ``float16``, ``int32``, ``uint32``, ``float32``, and ``bfloat16`` data types
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* Allreduce only supports the sum operation
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Enabling peer-to-peer transport
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===============================
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To enable peer-to-peer access on machines with PCIe-connected GPUs,
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set the HSA environment variable as follows:
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.. code-block:: shell
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HSA_FORCE_FINE_GRAIN_PCIE=1
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This feature requires GPUs that support peer-to-peer access along with
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proper large BAR addressing support.
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Improving performance on the MI300X accelerator when using fewer than 8 GPUs
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============================================================================
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On a system with 8\*MI300X accelerators, each pair of accelerators is connected with dedicated XGMI links
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in a fully-connected topology. For collective operations, this can achieve good performance when
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all 8 accelerators (and all XGMI links) are used. When fewer than 8 GPUs are used, however, this can only achieve a fraction
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of the potential bandwidth on the system.
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However, if your workload warrants using fewer than 8 MI300X accelerators on a system,
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you can set the run-time variable ``NCCL_MIN_NCHANNELS`` to increase the number of channels. For example:
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.. code-block:: shell
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export NCCL_MIN_NCHANNELS=32
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Increasing the number of channels can benefit performance, but it also increases
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GPU utilization for collective operations.
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Additionally, RCCL pre-defines a higher number of channels when only 2 or
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4 accelerators are in use on a 8\*MI300X system. In this situation, RCCL uses 32 channels with two MI300X accelerators
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and 24 channels for four MI300X accelerators. |