问NumPy/SciPy中的多线程整数矩阵乘法

EN

import numpy as np
from numpy.testing import assert_array_equal
import threading
from time import time


def blockshaped(arr, nrows, ncols):
    """
    Return an array of shape (nrows, ncols, n, m) where
    n * nrows, m * ncols = arr.shape.
    This should be a view of the original array.
    """
    h, w = arr.shape
    n, m = h // nrows, w // ncols
    return arr.reshape(nrows, n, ncols, m).swapaxes(1, 2)


def do_dot(a, b, out):
    #np.dot(a, b, out)  # does not work. maybe because out is not C-contiguous?
    out[:] = np.dot(a, b)  # less efficient because the output is stored in a temporary array?


def pardot(a, b, nblocks, mblocks, dot_func=do_dot):
    """
    Return the matrix product a * b.
    The product is split into nblocks * mblocks partitions that are performed
    in parallel threads.
    """
    n_jobs = nblocks * mblocks
    print('running {} jobs in parallel'.format(n_jobs))

    out = np.empty((a.shape[0], b.shape[1]), dtype=a.dtype)

    out_blocks = blockshaped(out, nblocks, mblocks)
    a_blocks = blockshaped(a, nblocks, 1)
    b_blocks = blockshaped(b, 1, mblocks)

    threads = []
    for i in range(nblocks):
        for j in range(mblocks):
            th = threading.Thread(target=dot_func, 
                                  args=(a_blocks[i, 0, :, :], 
                                        b_blocks[0, j, :, :], 
                                        out_blocks[i, j, :, :]))
            th.start()
            threads.append(th)

    for th in threads:
        th.join()

    return out


if __name__ == '__main__':
    a = np.ones((4, 3), dtype=int)
    b = np.arange(18, dtype=int).reshape(3, 6)
    assert_array_equal(pardot(a, b, 2, 2), np.dot(a, b))

    a = np.random.randn(1500, 1500).astype(int)

    start = time()
    pardot(a, a, 2, 4)
    time_par = time() - start
    print('pardot: {:.2f} seconds taken'.format(time_par))

    start = time()
    np.dot(a, a)
    time_dot = time() - start
    print('np.dot: {:.2f} seconds taken'.format(time_dot))

running 8 jobs in parallel
pardot: 5.45 seconds taken
np.dot: 22.30 seconds taken

import numpy as np
import time

def timeit(callable):
    start = time.time()
    callable()
    end = time.time()
    return end - start

a = np.random.random_integers(0, 9, size=(1000, 1000)).astype(np.int8)

timeit(lambda: a.dot(a))  # ≈0.9 sec
timeit(lambda: a.astype(np.float32).dot(a.astype(np.float32)).astype(np.int8) )  # ≈0.01 sec