CUDA Ufuncs and Generalized Ufuncs — Anaconda Enterprise 4.4 documentation

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# CUDA Ufuncs and Generalized Ufuncs

This page describes the CUDA ufunc-like object.

To support the programming pattern of CUDA programs, CUDA Vectorize and GUVectorize cannot produce a conventional ufunc. Instead, a ufunc-like object is returned. This object is a close analog but not fully compatible with a regular NumPy ufunc. The CUDA ufunc adds support for passing intra-device arrays (already on the GPU device) to reduce traffic over the PCI-express bus. It also accepts a stream keyword for launching in asynchronous mode.

## Example: Basic Example

    import math
    from numbapro import vectorize, cuda
    import numpy as np

    @vectorize(['float32(float32, float32, float32)',
                'float64(float64, float64, float64)'],
               target='gpu')
    def cu_discriminant(a, b, c):
        return math.sqrt(b ** 2 - 4 * a * c)

    N = 1e+4
    dtype = np.float32

    # prepare the input
    A = np.array(np.random.sample(N), dtype=dtype)
    B = np.array(np.random.sample(N) + 10, dtype=dtype)
    C = np.array(np.random.sample(N), dtype=dtype)

    D = cu_discriminant(A, B, C)

    print(D)  # print result

## Example: Calling Device Functions

All CUDA ufunc kernels have the ability to call other CUDA device functions:

    from numbapro import vectorize, cuda

    # define a device function
    @cuda.jit('float32(float32, float32, float32)', device=True, inline=True)
    def cu_device_fn(x, y, z):
        return x ** y / z

    # define a ufunc that calls our device function
    @vectorize(['float32(float32, float32, float32)'], target='gpu')
    def cu_ufunc(x, y, z):
        return cu_device_fn(x, y, z)

## Generalized CUDA ufuncs

Generalized ufuncs may be executed on the GPU using CUDA, analogous to the CUDA ufunc functionality. This may be accomplished as follows:

    from numbapro import guvectorize

    @guvectorize(['void(float32[:,:], float32[:,:], float32[:,:])'],
                 '(m,n),(n,p)->(m,p)', target='gpu')
    def matmulcore(A, B, C):
        ...

There are times when the gufunc kernel uses too many of a GPU’s resources, which can cause the kernel launch to fail. The user can explicitly control the maximum size of the thread block by setting the max_blocksize attribute on the compiled gufunc object.

    from numbapro import guvectorize

    @guvectorize(..., target='gpu')
    def very_complex_kernel(A, B, C):
        ...

    very_complex_kernel.max_blocksize = 32  # limits to 32 threads per block