Achieving optimal performance in GPU-centric workflows frequently requires customizing how host and device memory are allocated. For example, using "pinned" host memory for asynchronous host <-> device memory transfers, or using a device memory pool sub-allocator to reduce the cost of dynamic device memory allocation.

The goal of the RAPIDS Memory Manager (RMM) is to provide:

• A common interface that allows customizing device and host memory allocation
• A collection of implementations of the interface
• A collection of data structures that use the interface for memory allocation

For information on the interface RMM provides and how to use RMM in your C code, see below.

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

RMM can be installed with conda (miniconda, or the full Anaconda distribution) from the rapidsai channel:

# for CUDA 10.2
conda install -c nvidia -c rapidsai -c conda-forge -c defaults \
    rmm cudatoolkit=10.2
# for CUDA 10.1
conda install -c nvidia -c rapidsai -c conda-forge -c defaults \
    rmm cudatoolkit=10.1
# for CUDA 10.0
conda install -c nvidia -c rapidsai -c conda-forge -c defaults \
    rmm cudatoolkit=10.0

We also provide nightly conda packages built from the tip of our latest development branch.

Note: RMM is supported only on Linux, and with Python versions 3.6 or 3.7.

See the Get RAPIDS version picker for more OS and version info.

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 https://developer.nvidia.com/cuda-downloads

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

• Clone the repository and submodules

$ git clone --recurse-submodules https://github.com/rapidsai/rmm.git
$ 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 using cmake & make. 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 librmm.so ... '-j' will start a parallel job using the number of physical cores available on your system
$ make install                                      # install the library librmm.so to '/install/path'

• Building and installing librmm and rmm using build.sh. Build.sh creates build dir at root of git repository. build.sh depends on the nvcc executable being on your path or defined in $CUDACXX.

$ ./build.sh -h                                     # Display help and exit
$ ./build.sh -n librmm                              # Build librmm without installing
$ ./build.sh -n rmm                                 # Build rmm without installing
$ ./build.sh -n librmm rmm                          # Build librmm and rmm without installing
$ ./build.sh librmm rmm                             # Build and install librmm and rmm

• To run tests (Optional):

$ cd build (if you are not already in build directory)
$ make test

• Build, install, and test the rmm python package, in the python folder:

$ python setup.py build_ext --inplace
$ python setup.py install
$ pytest -v

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

The first goal of RMM is to provide a common interface for device and host memory allocation. This allows both users and implementers of custom allocation logic to program to a single interface.

To this end, RMM defines two abstract interface classes:

• rmm::mr::device_memory_resource for device memory allocation
• rmm::mr::host_memory_resource for host memory allocation

These classes are based on the std::pmr::memory_resource interface class introduced in C 17 for polymorphic memory allocation.

rmm::mr::device_memory_resource is the base class that defines the interface for allocating and freeing device memory.

It has two key functions:

1. void* device_memory_resource::allocate(std::size_t bytes, cudaStream_t s)

   • Returns a pointer to an allocation of at least bytes bytes.

2. void device_memory_resource::deallocate(void* p, std::size_t bytes, cudaStream_t s)

   • Reclaims a previous allocation of size bytes pointed to by p.
   • p must have been returned by a previous call to allocate(bytes), otherwise behavior is undefined

It is up to a derived class to provide implementations of these functions. See available resources for example device_memory_resource derived classes.

Unlike std::pmr::memory_resource, rmm::mr::device_memory_resource does not allow specifying an alignment argument. All allocations are required to be aligned to at least 256B. Furthermore, device_memory_resource adds an additional cudaStream_t argument to allow specifying the stream on which to perform the (de)allocation.

RMM provides several device_memory_resource derived classes to satisfy various user requirements. For more detailed information about these resources, see their respective documentation.

Allocates and frees device memory using cudaMalloc and cudaFree.

Allocates and frees device memory using cudaMallocManaged and cudaFree.

A coalescing, best-fit pool sub-allocator.

Uses the CNMeM pool sub-allocator to satisfy (de)allocations. These resources are deprecated as of RMM 0.15.

A memory resource that can only allocate a single fixed size. Average allocation and deallocation cost is constant.

Configurable to use multiple upstream memory resources for allocations that fall within different bin sizes. Often configured with multiple bins backed by fixed_size_memory_resources and a single pool_memory_resource for allocations larger than the largest bin size.

RMM users commonly need to configure a device_memory_resource object to use for all allocations where another resource has not explicitly been provided. A common example is configuring a pool_memory_resource to use for all allocations to get fast dynamic allocation.

To enable this use case, RMM provides the concept of a "default" device_memory_resource. This resource is used when another is not explicitly provided.

Accessing and modifying the default resource is done through two functions:

• device_memory_resource* get_current_device_resource()

  • Returns a pointer to the default resource for the current CUDA device.
  • The initial default memory resource is an instance of cuda_memory_resource.
  • This function is thread safe with respect to concurrent calls to it and set_current_device_resource().
  • For more explicit control, you can use get_per_device_resource(), which takes a device ID.
  • Replaces the deprecated get_default_resource()

• device_memory_resource* set_current_device_resource(device_memory_resource* new_mr)

  • Updates the default memory resource pointer for the current CUDA device to new_resource
  • Returns the previous default resource pointer
  • If new_resource is nullptr, then resets the default resource to cuda_memory_resource
  • This function is thread safe with respect to concurrent calls to it and get_current_device_resource()
  • For more explicit control, you can use set_per_device_resource(), which takes a device ID.
  • Replaces the deprecated set_default_resource()

rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource(); // Points to `cuda_memory_resource`
// Construct a resource that uses a coalescing best-fit pool allocator
rmm::mr::pool_memory_resource<rmm::mr::cuda_memory_resource>> pool_mr{mr}; 
rmm::mr::set_current_device_resource(&pool_mr); // Updates the current device resource pointer to `pool_mr`
rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource(); // Points to `pool_mr`

A device_memory_resource should only be used when the active CUDA device is the same device that was active when the device_memory_resource was created. Otherwise behavior is undefined.

Creating a device_memory_resource for each device requires care to set the current device before creating each resource, and to maintain the lifetime of the resources as long as they are set as per-device resources. Here is an example loop that creates unique_ptrs to pool_memory_resource objects for each device and sets them as the per-device resource for that device.

std::vector<unique_ptr<pool_memory_resource>> per_device_pools;
for(int i = 0; i < N;   i) {
    cudaSetDevice(i); // set device i before creating MR
    // Use a vector of unique_ptr to maintain the lifetime of the MRs
    per_device_pools.push_back(std::make_unique<pool_memory_resource>());
    // Set the per-device resource for device i
    set_per_device_resource(cuda_device_id{i}, &per_device_pools.back());
}

An untyped, unintialized RAII class for stream ordered device memory allocation.

cudaStream_t s;
rmm::device_buffer b{100,s}; // Allocates at least 100 bytes on stream `s` using the *default* resource
void* p = b.data();          // Raw, untyped pointer to underlying device memory

kernel<<<..., s>>>(b.data()); // `b` is only safe to use on `s`

rmm::mr::device_memory_resource * mr = new my_custom_resource{...};
rmm::device_buffer b2{100, s, mr}; // Allocates at least 100 bytes on stream `s` using the explicitly provided resource

A typed, unintialized RAII class for allocation of a contiguous set of elements in device memory. Similar to a thrust::device_vector, but as an optimization, does not default initialize the contained elements. This optimization restricts the types T to trivially copyable types.

cudaStream_t s;
rmm::device_uvector<int32_t> v(100, s); /// Allocates uninitialized storage for 100 `int32_t` elements on stream `s` using the default resource
thrust::uninitialized_fill(thrust::cuda::par.on(s), v.begin(), v.end(), int32_t{0}); // Initializes the elements to 0

rmm::mr::device_memory_resource * mr = new my_custom_resource{...};
rmm::device_vector<int32_t> v2{100, s, mr}; // Allocates uninitialized storage for 100 `int32_t` elements on stream `s` using the explicitly provided resource

A typed, RAII class for allocation of a single element in device memory. This is similar to a device_uvector with a single element, but provides convenience functions like modifying the value in device memory from the host, or retrieving the value from device to host.

cudaStream_t s;
rmm::device_scalar<int32_t> a{s}; // Allocates uninitialized storage for a single `int32_t` in device memory
a.set_value(42, s); // Updates the value in device memory to `42` on stream `s`

kernel<<<...,s>>>(a.data()); // Pass raw pointer to underlying element in device memory

int32_t v = a.value(s); // Retrieves the value from device to host on stream `s`

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 provides rmm::mr::thrust_allocator as a conforming Thrust allocator that uses device_memory_resources.

To instruct a Thrust algorithm to use rmm::mr::thrust_allocator to allocate temporary storage, you can use the custom Thrust CUDA device execution policy: rmm::exec_policy(stream).

rmm::exec_policy(stream) returns a std::unique_ptr to a Thrust execution policy that uses rmm::mr::thrust_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::mr::thrust_allocator. The second stream argument is what should be used to execute the Thrust algorithm. These two arguments must be identical.

rmm::mr::host_memory_resource is the base class that defines the interface for allocating and freeing host memory.

Similar to device_memory_resource, it has two key functions for (de)allocation:

1. void* device_memory_resource::allocate(std::size_t bytes, std::size_t alignment)

   • Returns a pointer to an allocation of at least bytes bytes aligned to the specified alignment

2. void device_memory_resource::deallocate(void* p, std::size_t bytes, std::size_t alignment)

   • Reclaims a previous allocation of size bytes pointed to by p.

Unlike device_memory_resource, the host_memory_resource interface and behavior is identical to std::pmr::memory_resource.

Uses the global operator new and operator delete to allocate host memory.

Allocates "pinned" host memory using cuda(Malloc/Free)Host.

RMM does not currently provide any data structures that interface with host_memory_resource. In the future, RMM will provide a similar host-side structure like device_buffer and an allocator that can be used with STL containers.

There are two ways to use RMM in Python code:

1. Using the rmm.DeviceBuffer API to explicitly create and manage device memory allocations
2. Transparently via external libraries such as CuPy and Numba

RMM provides a MemoryResource abstraction to control how device memory is allocated in both the above uses.

A DeviceBuffer represents an untyped, uninitialized device memory allocation. DeviceBuffers can be created by providing the size of the allocation in bytes:

>>> import rmm
>>> buf = rmm.DeviceBuffer(size=100)

The size of the allocation and the memory address associated with it can be accessed via the .size and .ptr attributes respectively:

>>> buf.size
100
>>> buf.ptr
140202544726016

DeviceBuffers can also be created by copying data from host memory:

>>> import rmm
>>> import numpy as np
>>> a = np.array([1, 2, 3], dtype='float64')
>>> buf = rmm.to_device(a.tobytes())
>>> buf.size
24

Conversely, the data underlying a DeviceBuffer can be copied to the host:

>>> np.frombuffer(buf.tobytes())
array([1., 2., 3.])

MemoryResources are used to configure how device memory allocations are made by RMM.

By default, i.e., if you don't set a MemoryResource explicitly, RMM uses the CudaMemoryResource, which uses cudaMalloc for allocating device memory.

rmm.reinitialize() provides an easy way to initialize RMM with specific memory resource options across multiple devices. See `help(rmm.reinitialize) for full details.

For lower-level control, rmm.mr.set_current_device_resource() function can be used to set a different MemoryResource for the current CUDA device. For example, enabling the ManagedMemoryResource tells RMM to use cudaMallocManaged instead of cudaMalloc for allocating memory:

>>> import rmm
>>> rmm.mr.set_current_device_resource(rmm.mr.ManagedMemoryResource())

⚠️ The default resource must be set for any device before allocating any device memory on that device. Setting or changing the resource after device allocations have been made can lead to unexpected behaviour or crashes. See Multiple Devices

As another example, PoolMemoryResource allows you to allocate a large "pool" of device memory up-front. Subsequent allocations will draw from this pool of already allocated memory. The example below shows how to construct a PoolMemoryResource with an initial size of 1 GiB and a maximum size of 4 GiB. The pool uses CudaMemoryResource as its underlying ("upstream") memory resource:

>>> import rmm
>>> pool = rmm.mr.PoolMemoryResource(
...     upstream=rmm.mr.CudaMemoryResource(),
...     initial_pool_size=2**30,
...     maximum_pool_size=2**32
... )
>>> rmm.mr.set_current_device_resource(pool)

Other MemoryResources include:

• FixedSizeMemoryResource for allocating fixed blocks of memory
• BinningMemoryResource for allocating blocks within specified "bin" sizes from different memory resources

MemoryResources are highly configurable and can be composed together in different ways. See help(rmm.mr) for more information.

You can configure CuPy to use RMM for memory allocations by setting the CuPy CUDA allocator to rmm_cupy_allocator:

>>> import rmm
>>> import cupy
>>> cupy.cuda.set_allocator(rmm.rmm_cupy_allocator)

You can configure Numba to use RMM for memory allocations using the Numba EMM Plugin.

This can be done in two ways:

1. Setting the environment variable NUMBA_CUDA_MEMORY_MANAGER:

$ NUMBA_CUDA_MEMORY_MANAGER=rmm python (args)

2. Using the set_memory_manager() function provided by Numba:

>>> from numba import cuda
>>> import rmm
>>> cuda.set_memory_manager(rmm.RMMNumbaManager)