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RAPIDS Memory Manager
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 RMM: RAPIDS Memory Manager

RAPIDS Memory Manager (RMM) is:

  • A replacement allocator for CUDA Device Memory (and CUDA Managed Memory).
  • A pool allocator to make CUDA device memory allocation / deallocation faster and asynchronous.
  • A central place for all device memory allocations in cuDF (C++ and Python) and other RAPIDS libraries.

RMM is not:

  • A replacement allocator for host memory (malloc, new, cudaMallocHost, cudaHostRegister).

NOTE: For the latest stable ensure you are on the master branch.

Install RMM

RMM currently must be built from source. This happens automatically in a submodule when you build or install cuDF or RAPIDS containers.

Building from Source

Get RMM Dependencies

Compiler requirements:

  • gcc version 4.8 or higher recommended
  • nvcc version 9.0 or higher recommended
  • cmake version 3.12 or higher

CUDA/GPU requirements:

  • CUDA 9.0+
  • NVIDIA driver 396.44+
  • Pascal architecture or better

You can obtain CUDA from

Script to build RMM from source

To install RMM from source, ensure the dependencies are met and follow the steps below:

  • Clone the repository and submodules
$ git clone --recurse-submodules
$ cd rmm

Follow the instructions under "Create the conda development environment cudf_dev" in the cuDF README.

  • Create the conda development environment cudf_dev
# create the conda environment (assuming in base `cudf` directory)
$ conda env create --name cudf_dev --file conda/environments/dev_py35.yml
# activate the environment
$ source activate cudf_dev
  • Build and install librmm. CMake depends on the nvcc executable being on your path or defined in $CUDACXX.
$ mkdir build                                       # make a build directory
$ cd build                                          # enter the build directory
$ cmake .. -DCMAKE_INSTALL_PREFIX=/install/path     # configure cmake ... use $CONDA_PREFIX if you're using Anaconda
$ make -j                                           # compile the library ... '-j' will start a parallel job using the number of physical cores available on your system
$ make install                                      # install the library to '/install/path'
  • To run tests (Optional):
$ make test
  • Build, install, and test cffi bindings:
$ make rmm_python_cffi                              # build CFFI bindings for
$ make rmm_install_python                           # build & install CFFI python bindings. Depends on cffi package from PyPi or Conda
$ cd python && pytest -v                            # optional, run python tests on low-level python bindings

Done! You are ready to develop for the RMM OSS project.

Using RMM in C/C++ code

Using RMM in CUDA C++ code is straightforward. Include rmm.h and replace calls to cudaMalloc() and cudaFree() with calls to the RMM_ALLOC() and RMM_FREE() macros, respectively.

Note that RMM_ALLOC and RMM_FREE take an additional parameter, a stream identifier. This is necessary to enable asynchronous allocation and deallocation; however, the default (also known as null) stream (or 0) can be used. For example:

// old
cudaError_t result = cudaMalloc(&myvar, size_in_bytes) );
// ...
cudaError_t result = cudaFree(myvar) );

// new
rmmError_t result = RMM_ALLOC(&myvar, size_in_bytes, stream_id);
// ...
rmmError_t result = RMM_FREE(myvar, stream_id);

Note that RMM_ALLOC and RMM_FREE are wrappers around rmm::alloc() and rmm::free(), respectively. The lower-level functions also take a file name and a line number for tracking the location of RMM allocations and deallocations. The macro versions use the C preprocessor to automatically specify these params.

Using RMM with Thrust

RAPIDS and other CUDA libraries make heavy use of Thrust. Thrust uses CUDA device memory in two situations:

  1. As the backing store for thrust::device_vector, and
  2. As temporary storage inside some algorithms, such as thrust::sort.

RMM includes a custom Thrust allocator in the file thrust_rmm_allocator.h. This defines the template class rmm_allocator, and a custom Thrust CUDA device execution policy called rmm::exec_policy(stream).

Thrust Device Vectors

Instead of creating device vectors like this:

thrust::device_vector<size_type> permuted_indices(column_length);

You can tell Thrust to use rmm_allocator like this:

thrust::device_vector<size_type, rmm_allocator<T>> permuted_indices(column_length);

For convenience, you can use the alias rmm::device_vector<T> defined in thrust_rmm_allocator.h that can be used as if it were a thrust::device_vector<T>.

Thrust Algorithms

To instruct Thrust to use RMM to allocate temporary storage, you can use the custom Thrust CUDA device execution policy: rmm::exec_policy(stream). This instructs Thrust to use the rmm_allocator on the specified stream for temporary memory allocation.

rmm::exec_policy(stream) returns a std::unique_ptr to a Thrust execution policy that uses rmm_allocator for temporary allocations. In order to specify that the Thrust algorithm be executed on a specific stream, the usage is:

thrust::sort(rmm::exec_policy(stream)->on(stream), ...);

The first stream argument is the stream to use for rmm_allocator. The second stream argument is what should be used to execute the Thrust algorithm. These two arguments must be identical.

Using RMM in Python Code

cuDF and other Python libraries typically create arrays of CUDA device memory by using Numba's cuda.device_array interfaces. Until Numba provides a plugin interface for using an external memory manager, RMM provides an API compatible with cuda.device_array constructors that cuDF (also cuDF C++ API pytests) should use to ensure all CUDA device memory is allocated via the memory manager. RMM provides:

  • librmm.device_array()
  • librmm.device_array_like()
  • librmm.to_device()
  • librmm.auto_device()

Which are compatible with their Numba cuda.* equivalents. They return a Numba NDArray object whose memory is allocated in CUDA device memory using RMM.

Following is an example from cuDF that copies from a numpy array to an equivalent CUDA device_array using to_device(), and creates a device array using device_array, and then runs a Numba kernel (group_mean) to compute the output values.

    dev_begins = rmm.to_device(np.asarray(begin))
    dev_out = rmm.device_array(size, dtype=np.float64)
    if size > 0:
    values[newk] = dev_out

In another example from cuDF, fillna uses device_array_like to construct a CUDA device array with the same shape and data type as another.

def fillna(data, mask, value):
    out = rmm.device_array_like(data)
    configured = gpu_fill_masked.forall(data.size)
    configured(value, mask, out)
    return out

librmm also provides get_ipc_handle() for getting the IPC handle associated with a Numba NDArray, which accounts for the case where the data for the NDArray is suballocated from some larger pool allocation by the memory manager.

To use librmm NDArray functions you need to import librmm like this:

from librmm_cffi import librmm or from librmm_cffi import librmm as rmm

Handling RMM Options in Python Code

RMM currently defaults to just calling cudaMalloc, but you can enable the experimental pool allocator using the librmm_config module.

from librmm_cffi import librmm_config as rmm_cfg

rmm_cfg.use_pool_allocator = True  # default is False
rmm_cfg.initial_pool_size = 2<<30  # set to 2GiB. Default is 1/2 total GPU memory
rmm_cfg.use_managed_memory = False # default is false
rmm_cfg.enable_logging = True      # default is False -- has perf overhead

To configure RMM options to be used in cuDF before loading, simply do the above before you import cudf. You can re-initialize the memory manager with different settings at run time by calling librmm.finalize(), then changing the above options, and then calling librmm.initialize().

You can also optionally use the internal functions in cuDF which call these functions. Here are some example configuration functions that can be used in a notebook to initialize the memory manager in each Dask worker.

from librmm_cffi import librmm_config as rmm_cfg

def initialize_rmm_pool():
    rmm_cfg.use_pool_allocator = True
    return pygdf._gdf.rmm_initialize()

def initialize_rmm_no_pool():
    rmm_cfg.use_pool_allocator = False
    return pygdf._gdf.rmm_initialize()

def finalize_rmm():
    return pygdf._gdf.rmm_finalize()

Given the above, typically you would initialize RMM in the notebook process to not use the pool initialize_rmm_no_pool(), and then run ` to initialize a memory pool in each worker process.

Remember that while the pool is in use memory is not freed. So if you follow cuDF operations with device-memory-intensive computations that don't use RMM (such as XGBoost), you will need to move the data to the host and then finalize RMM. The Mortgage E2E workflow notebook uses this technique. We are working on better ways to reclaim memory, as well as making RAPIDS machine learning libraries use the same RMM memory pool.

CUDA Managed Memory

RMM can be set to allocate all memory as managed memory (cudaMallocManaged underlying allocator). This is enabled in C++ by setting the allocation_mode member of the struct rmmOptions_t to include the flag CudaManagedMemory (the flags are ORed), and passing it to rmmInitialize(). If the flag PoolAllocation is also set, then RMM will allocate from a pool of managed memory.

In Python, use the librmm_config.use_managed_memory Boolean setting as shown previously.

When the allocation mode is both CudaManagedMemory and PoolAllocation, RMM allocates the initial pool (and any expansion allocations) using cudaMallocManaged and then prefetches the pool to the GPU using cudaMemPrefetchAsync so all pool memory that will fit is initially located on the device.

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