3.15. CUDA Array Interface

The cuda array inteface 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.

3.15.1. Python Interface Specification

Note

Experimental feature. Specification may change.

The __cuda_array_interface__ attribute is a dictionary-like object 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 data pointer as a Python int (or long). The data must be device-accessible. 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 1.

The following are optional entries:

  • strides: None or (integer, ...)

    A tuple of int (or long) representing the number of bytes to skip to access the next element at each dimension. If it is None, the array is assumed to be in C-contiguous layout.

  • 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.

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?

3.15.1.1. Differences with CUDA Array Interface (Version 0)

The version 0 CUDA Array Interface did not have the optional mask attribute to support masked arrays.