python - 将函数转换为 NumbaPro CUDA

Question

我正在比较几个 Python 模块/扩展或方法来实现以下目标:

import numpy as np

def fdtd(input_grid, steps):
    grid = input_grid.copy()
    old_grid = np.zeros_like(input_grid)
    previous_grid = np.zeros_like(input_grid)

    l_x = grid.shape[0]
    l_y = grid.shape[1]

    for i in range(steps):
        np.copyto(previous_grid, old_grid)
        np.copyto(old_grid, grid)

        for x in range(l_x):
            for y in range(l_y):
                grid[x,y] = 0.0
                if 0 < x+1 < l_x:
                    grid[x,y] += old_grid[x+1,y]
                if 0 < x-1 < l_x:
                    grid[x,y] += old_grid[x-1,y]
                if 0 < y+1 < l_y:
                    grid[x,y] += old_grid[x,y+1]
                if 0 < y-1 < l_y:
                    grid[x,y] += old_grid[x,y-1]

                grid[x,y] /= 2.0
                grid[x,y] -= previous_grid[x,y]

    return grid

此函数是时域有限差分 (FDTD) 方法的一个非常基本的实现。我已经通过多种方式实现了这个功能:

• 使用更多 NumPy 例程
• 在 Cython 中
• 使用 Numba(自动)jit。

现在我想比较 NumbaPro CUDA 的性能。

这是我第一次为 CUDA 编写代码，我想出了下面的代码。

from numbapro import cuda, float32, int16
import numpy as np

@cuda.jit(argtypes=(float32[:,:], float32[:,:], float32[:,:], int16, int16, int16))
def kernel(grid, old_grid, previous_grid, steps, l_x, l_y):

    x,y = cuda.grid(2)

    for i in range(steps):
        previous_grid[x,y] = old_grid[x,y]
        old_grid[x,y] = grid[x,y]  

    for i in range(steps):

        grid[x,y] = 0.0

        if 0 < x+1 and x+1 < l_x:
            grid[x,y] += old_grid[x+1,y]
        if 0 < x-1 and x-1 < l_x:
            grid[x,y] += old_grid[x-1,y]
        if 0 < y+1 and y+1 < l_x:
            grid[x,y] += old_grid[x,y+1]
        if 0 < y-1 and y-1 < l_x:
            grid[x,y] += old_grid[x,y-1]

        grid[x,y] /= 2.0
        grid[x,y] -= previous_grid[x,y]


def fdtd(input_grid, steps):

    grid = cuda.to_device(input_grid)
    old_grid = cuda.to_device(np.zeros_like(input_grid))
    previous_grid = cuda.to_device(np.zeros_like(input_grid))

    l_x = input_grid.shape[0]
    l_y = input_grid.shape[1]

    kernel[(16,16),(32,8)](grid, old_grid, previous_grid, steps, l_x, l_y)

    return grid.copy_to_host()

不幸的是，我收到以下错误:

  File ".../fdtd_numbapro.py", line 98, in fdtd
    return grid.copy_to_host()
  File "/opt/anaconda1anaconda2anaconda3/lib/python2.7/site-packages/numbapro/cudadrv/devicearray.py", line 142, in copy_to_host
  File "/opt/anaconda1anaconda2anaconda3/lib/python2.7/site-packages/numbapro/cudadrv/driver.py", line 1702, in device_to_host
  File "/opt/anaconda1anaconda2anaconda3/lib/python2.7/site-packages/numbapro/cudadrv/driver.py", line 772, in check_error
numbapro.cudadrv.error.CudaDriverError: CUDA_ERROR_LAUNCH_FAILED
Failed to copy memory D->H

我也使用过 grid.to_host() ，但这两者都不起作用。CUDA 肯定可以在此系统上使用 NumbaPro。

Answer 1

问题由用户解决。对于这个问题，我正在交叉引用 Anaconda 邮件列表上的讨论:https://groups.google.com/a/continuum.io/forum/#!searchin/anaconda/fdtd/anaconda/VgiN4h37UrA/18tAc60EIkcJ