275da176ba
- Rewrite GPU demos. notebook is converted to script to avoid committing additional png plots. - Add GPU demos into the sphinx gallery. - Add RMM demos into the sphinx gallery. - Test for firing threads with different device ordinals.
52 lines
2.3 KiB
ReStructuredText
52 lines
2.3 KiB
ReStructuredText
Using XGBoost with RAPIDS Memory Manager (RMM) plugin (EXPERIMENTAL)
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====================================================================
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`RAPIDS Memory Manager (RMM) <https://github.com/rapidsai/rmm>`__ library provides a
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collection of efficient memory allocators for NVIDIA GPUs. It is now possible to use
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XGBoost with memory allocators provided by RMM, by enabling the RMM integration plugin.
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The demos in this directory highlights one RMM allocator in particular: **the pool
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sub-allocator**. This allocator addresses the slow speed of ``cudaMalloc()`` by
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allocating a large chunk of memory upfront. Subsequent allocations will draw from the pool
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of already allocated memory and thus avoid the overhead of calling ``cudaMalloc()``
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directly. See `this GTC talk slides
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<https://on-demand.gputechconf.com/gtc/2015/presentation/S5530-Stephen-Jones.pdf>`_ for
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more details.
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Before running the demos, ensure that XGBoost is compiled with the RMM plugin enabled. To do this,
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run CMake with option ``-DPLUGIN_RMM=ON`` (``-DUSE_CUDA=ON`` also required):
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.. code-block:: sh
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cmake .. -DUSE_CUDA=ON -DUSE_NCCL=ON -DPLUGIN_RMM=ON
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make -j$(nproc)
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CMake will attempt to locate the RMM library in your build environment. You may choose to build
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RMM from the source, or install it using the Conda package manager. If CMake cannot find RMM, you
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should specify the location of RMM with the CMake prefix:
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.. code-block:: sh
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# If using Conda:
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cmake .. -DUSE_CUDA=ON -DUSE_NCCL=ON -DPLUGIN_RMM=ON -DCMAKE_PREFIX_PATH=$CONDA_PREFIX
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# If using RMM installed with a custom location
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cmake .. -DUSE_CUDA=ON -DUSE_NCCL=ON -DPLUGIN_RMM=ON -DCMAKE_PREFIX_PATH=/path/to/rmm
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********************************
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Informing XGBoost about RMM pool
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********************************
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When XGBoost is compiled with RMM, most of the large size allocation will go through RMM
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allocators, but some small allocations in performance critical areas are using a different
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caching allocator so that we can have better control over memory allocation behavior.
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Users can override this behavior and force the use of rmm for all allocations by setting
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the global configuration ``use_rmm``:
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.. code-block:: python
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with xgb.config_context(use_rmm=True):
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clf = xgb.XGBClassifier(tree_method="hist", device="cuda")
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Depending on the choice of memory pool size or type of allocator, this may have negative
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performance impact.
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