Continuum Analytics: Documentation

# Examples

## Matrix multiplication

Here is a naive implementation of matrix multiplication using a HSA kernel:

    @hsa.jit
    def matmul(A, B, C):
        i = hsa.get_global_id(0)
        j = hsa.get_global_id(1)

        if i >= C.shape[0] or j >= C.shape[1]:
            return

        tmp = 0

        for k in range(A.shape[1]):
            tmp += A[i, k] * B[k, j]

        C[i, j] = tmp

This implementation is straightforward and intuitive but performs poorly, because the same matrix elements will be loaded multiple times from device memory, which is slow (some devices may have transparent data caches, but they may not be large enough to hold the entire inputs at once).

It will be faster if we use a blocked algorithm to reduce accesses to the device memory. HSA provides a fast shared memory for workitems in a group to cooperately compute on a task. The following implements a faster version of the square matrix multiplication using shared memory:

    import numpy as np
    from numba import hsa

    blocksize = 20
    gridsize = 20

    @hsa.jit
    def matmulfast(A, B, C):
        x = hsa.get_global_id(0)
        y = hsa.get_global_id(1)

        tx = hsa.get_local_id(0)
        ty = hsa.get_local_id(1)

        sA = hsa.shared.array(shape=(blocksize, blocksize), dtype=float32)
        sB = hsa.shared.array(shape=(blocksize, blocksize), dtype=float32)

        if x >= C.shape[0] or y >= C.shape[1]:
            return

        tmp = 0

        for i in range(gridsize):
            # preload
            sA[tx, ty] = A[x, ty + i * blocksize]
            sB[tx, ty] = B[tx + i * blocksize, y]
            # wait for preload to end
            hsa.barrier(1)
            # compute loop
            for j in range(blocksize):
                tmp += sA[tx, j] * sB[j, ty]
            # wait for compute to end
            hsa.barrier(1)

        C[x, y] = tmp

    N = gridsize * blocksize
    A = np.random.random((N, N)).astype(np.float32)
    B = np.random.random((N, N)).astype(np.float32)
    C = np.zeros_like(A)

    griddim = gridsize, gridsize
    blockdim = blocksize, blocksize

    with hsa.register(A, B, C):
        ts = timer()
        matmulfast[griddim, blockdim](A, B, C)
        te = timer()
        print("1st GPU time:", te - ts)

    with hsa.register(A, B, C):
        ts = timer()
        matmulfast[griddim, blockdim](A, B, C)
        te = timer()
        print("2nd GPU time:", te - ts)

    ts = timer()
    ans = np.dot(A, B)
    te = timer()
    print("CPU time:", te - ts)
    np.testing.assert_allclose(ans, C, rtol=1e-5)

Because the shared memory is a limited resources, the code preloads small block at a time from the input arrays. Then, it calls `barrier()` to wait until all threads have finished preloading and before doing the computation on the shared memory. It synchronizes again after the computation to ensure all threads have finished with the data in shared memory before overwriting it in the next loop iteration.