Question

我正在尝试使用Anaconda Accelerate编写一个在2d数组上执行逐元素加法，减法，乘法或除法的函数，但我写的函数一直得到错误的答案。不知道发生了什么。

import numpy as np
from numba import cuda

#Define functions
ADD, SUB, MUL, DIV = 1, 2, 3, 4
@cuda.jit('void(complex64[:,:], complex64[:,:], int8)')
def math_inplace_2d_cuda(a, b, operation):
    m, n = a.shape[0], a.shape[1]
    i, j = cuda.grid(2)
    if i < m and j < n:
        if operation == ADD: a[i, j] += b[i, j]
        if operation == SUB: a[i, j] -= b[i, j]
        if operation == MUL: a[i, j] *= b[i, j]
        if operation == DIV: a[i, j] /= b[i, j]

def math_inplace_2d_host(a, b, operation):
    m, n = a.shape[0], a.shape[1]
    for i in range(m):
        for j in range(n):
            if operation == ADD: a[i, j] += b[i, j]
            if operation == SUB: a[i, j] -= b[i, j]
            if operation == MUL: a[i, j] *= b[i, j]
            if operation == DIV: a[i, j] /= b[i, j]

#Create arrays
a = np.array([[1., 2], [3, 4]])
b = a.copy()*2

a_dev = cuda.to_device(a)
b_dev = cuda.to_device(b)

#Threading
threadperblock = 32, 8
def best_grid_size(size, tpb):
    bpg = np.ceil(np.array(size, dtype=np.float) / tpb).astype(np.int).tolist()
    return tuple(bpg)
blockpergrid = best_grid_size(a_dev.shape, threadperblock)
stream = cuda.stream()

#Do operation
op = ADD
math_inplace_2d_host(a, b, op)
math_inplace_2d_cuda[blockpergrid, threadperblock, stream](a_dev, b_dev, op)
print '\nhost\n', a
print '\ndevice\n', a_dev.copy_to_host()

此程序带有a和b数组的提供值，导致此输出（主机和设备阵列应该相同）：

host
[[  3.   6.]
 [  9.  12.]]

device
[[  384.   768.]
 [ 1024.  1536.]]

当我尝试减去时，我明白了：

host
[[-1. -2.]
 [-3. -4.]]

device
[[ -4.65661287e-10  -1.19209290e-07]
 [ -1.19209290e-07  -1.19209290e-07]]

用于乘法：

host
[[  2.   8.]
 [ 18.  32.]]

device
[[  1.59512330e-314   1.59615943e-314]
 [  1.59672607e-314   1.59732508e-314]]

对于师：

host
[[ 0.5  0.5]
 [ 0.5  0.5]]

device
[[  5.25836359e-315   5.25433420e-315]
 [  5.25481893e-315   5.25525520e-315]]

Answer 1

如果我将jit签名更改为：

，您的代码将适用于我

@cuda.jit('void(float64[:,:], float64[:,:], int64)')

或者如果我将a和op的定义更改为：

a = np.array([[1., 2], [3, 4]]).astype(np.complex64)
...
op = np.int8(ADD)

在后一种情况下，op为ADD，我得到：

host
[[  3.+0.j   6.+0.j]
 [  9.+0.j  12.+0.j]]

device
[[  3.+0.j   6.+0.j]
 [  9.+0.j  12.+0.j]]

我原本期望从Numba发出类型错误，但它似乎默默地投射并做错了什么。也许在Numba google小组上提出这个问题。

使用GPU的阵列操作输出错误

1 个答案: