.. _cuda-array-interface: ================================ CUDA Array Interface (Version 2) ================================ The *cuda array interface* is created for interoperability between different implementation of GPU array-like objects in various projects. The idea is borrowed from the `numpy array interface`_. .. note:: Currently, we only define the Python-side interface. In the future, we may add a C-side interface for efficient exchange of the information in compiled code. Python Interface Specification ============================== .. note:: Experimental feature. Specification may change. The ``__cuda_array_interface__`` attribute returns a dictionary (``dict``) that must contain the following entries: - **shape**: ``(integer, ...)`` A tuple of ``int`` (or ``long``) representing the size of each dimension. - **typestr**: ``str`` The type string. This has the same definition as ``typestr`` in the `numpy array interface`_. - **data**: ``(integer, boolean)`` The **data** is a 2-tuple. The first element is the data pointer as a Python ``int`` (or ``long``). The data must be device-accessible. For zero-size arrays, use ``0`` here. The second element is the read-only flag as a Python ``bool``. Because the user of the interface may or may not be in the same context, the most common case is to use ``cuPointerGetAttribute`` with ``CU_POINTER_ATTRIBUTE_DEVICE_POINTER`` in the CUDA driver API (or the equivalent CUDA Runtime API) to retrieve a device pointer that is usable in the currently active context. - **version**: ``integer`` An integer for the version of the interface being exported. The current version is *2*. The following are optional entries: - **strides**: ``None`` or ``(integer, ...)`` If **strides** is not given, or it is ``None``, the array is in C-contiguous layout. Otherwise, a tuple of ``int`` (or ``long``) is explicitly given for representing the number of bytes to skip to access the next element at each dimension. - **descr** This is for describing more complicated types. This follows the same specification as in the `numpy array interface`_. - **mask**: ``None`` or object exposing the ``__cuda_array_interface__`` If ``None`` then all values in **data** are valid. All elements of the mask array should be interpreted only as true or not true indicating which elements of this array are valid. This has the same definition as ``mask`` in the `numpy array interface`_. .. note:: Numba does not currently support working with masked CUDA arrays and will raise a ``NotImplementedError`` exception if one is passed to a GPU function. Lifetime management ------------------- Obtaining the value of the ``__cuda_array_interface__`` property of any object has no effect on the lifetime of the object from which it was created. In particular, note that the interface has no slot for the owner of the data. It is therefore imperative for a consumer to retain a reference to the object owning the data for as long as they make use of the data. Lifetime management in Numba ---------------------------- Numba provides two mechanisms for creating device arrays. Which to use depends on whether the created device array should maintain the life of the object from which it is created: - ``as_cuda_array``: This creates a device array that holds a reference to the owning object. As long as a reference to the device array is held, its underlying data will also be kept alive, even if all other references to the original owning object have been dropped. - ``from_cuda_array_interface``: This creates a device array with no reference to the owning object by default. The owning object, or some other object to be considered the owner can be passed in the ``owner`` parameter. The interfaces of these functions are: .. automethod:: numba.cuda.as_cuda_array .. automethod:: numba.cuda.from_cuda_array_interface Pointer Attributes ------------------ Additional information about the data pointer can be retrieved using ``cuPointerGetAttribute`` or ``cudaPointerGetAttributes``. Such information include: - the CUDA context that owns the pointer; - is the pointer host-accessible? - is the pointer a managed memory? .. _numpy array interface: https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.interface.html#__array_interface__ Differences with CUDA Array Interface (Version 0) ------------------------------------------------- Version 0 of the CUDA Array Interface did not have the optional **mask** attribute to support masked arrays. Differences with CUDA Array Interface (Version 1) ------------------------------------------------- Versions 0 and 1 of the CUDA Array Interface neither clarified the **strides** attribute for C-contiguous arrays nor specified the treatment for zero-size arrays. Interoperability ---------------- The following Python libraries have adopted the CUDA Array Interface: - Numba - `CuPy `_ - `PyTorch `_ - `PyArrow `_ - `mpi4py `_ - `ArrayViews `_ - `JAX `_ - The RAPIDS stack: - `cuDF `_ - `cuML `_ - `cuSignal `_ - `RMM `_ If your project is not on this list, please feel free to report it on the `Numba issue tracker `_.