Using XGBoost with RAPIDS Memory Manager (RMM) plugin
RAPIDS Memory Manager (RMM) library provides a collection of efficient memory allocators for NVIDIA GPUs. It is now possible to use XGBoost with memory allocators provided by RMM, by enabling the RMM integration plugin.
The demos in this directory highlights one RMM allocator in particular: the pool
sub-allocator. This allocator addresses the slow speed of cudaMalloc() by
allocating a large chunk of memory upfront. Subsequent allocations will draw from the pool
of already allocated memory and thus avoid the overhead of calling cudaMalloc()
directly. See this GTC talk slides for
more details.
Before running the demos, ensure that XGBoost is compiled with the RMM plugin enabled. To do this,
run CMake with option -DPLUGIN_RMM=ON (-DUSE_CUDA=ON also required):
cmake -B build -S . -DUSE_CUDA=ON -DUSE_NCCL=ON -DPLUGIN_RMM=ON
cmake --build build -j$(nproc)
CMake will attempt to locate the RMM library in your build environment. You may choose to build RMM from the source, or install it using the Conda package manager. If CMake cannot find RMM, you should specify the location of RMM with the CMake prefix:
# If using Conda:
cmake -B build -S . -DUSE_CUDA=ON -DUSE_NCCL=ON -DPLUGIN_RMM=ON -DCMAKE_PREFIX_PATH=$CONDA_PREFIX
# If using RMM installed with a custom location
cmake -B build -S . -DUSE_CUDA=ON -DUSE_NCCL=ON -DPLUGIN_RMM=ON -DCMAKE_PREFIX_PATH=/path/to/rmm
Informing XGBoost about RMM pool
When XGBoost is compiled with RMM, most of the large size allocation will go through RMM
allocators, but some small allocations in performance critical areas are using a different
caching allocator so that we can have better control over memory allocation behavior.
Users can override this behavior and force the use of rmm for all allocations by setting
the global configuration use_rmm:
with xgb.config_context(use_rmm=True):
clf = xgb.XGBClassifier(tree_method="hist", device="cuda")
Depending on the choice of memory pool size and the type of the allocator, this can add more consistency to memory usage but with slightly degraded performance impact.
No Device Ordinal for Multi-GPU
Since with RMM the memory pool is pre-allocated on a specific device, changing the CUDA
device ordinal in XGBoost can result in memory error cudaErrorIllegalAddress. Use the
CUDA_VISIBLE_DEVICES environment variable instead of the device="cuda:1" parameter
for selecting device. For distributed training, the distributed computing frameworks like
dask-cuda are responsible for device management. For Scala-Spark, see
XGBoost4J-Spark-GPU Tutorial for more info.
Memory Over-Subscription
Warning
This feature is still experimental and is under active development.
The newer NVIDIA platforms like Grace-Hopper use NVLink-C2C, which allows the CPU and GPU to have a coherent memory model. Users can use the SamHeadroomMemoryResource in the latest RMM to utilize system memory for storing data. This can help XGBoost utilize memory from the host for GPU computation, but it may reduce performance due to slower CPU memory speed and page migration overhead.
