【发布时间】：2018-06-16 15:52:53
【问题描述】：
我打算在 PyCUDA 中编写一个内核来生成 2d 高斯补丁。但是，我在主机中定义的值在将它们复制到设备后会发生变化。下面是代码。
import numpy as np
import matplotlib.pyplot as plt
import pycuda.driver as cuda
from pycuda.compiler import SourceModule
import pycuda.autoinit
# kernel
kernel = SourceModule("""
#include <stdio.h>
__global__ void gaussian2D(float *output, float x, float y, float sigma, int
n_rows, int n_cols)
{
int i = threadIdx.x + blockIdx.x * blockDim.x;
int j = threadIdx.y + blockIdx.y * blockDim.y;
printf("%d ", n_cols);
if (i < n_cols && j < n_rows) {
size_t idx = j*n_cols +i;
//printf("%d ", idx);
}
}
""")
# host code
def gpu_gaussian2D(point, sigma, shape):
# Convert parameters into numpy array
x, y = np.array(point, dtype=np.float32)
sigma = np.float32(sigma)
n_rows, n_cols = np.array(shape, dtype=np.int)
print(n_rows)
output = np.empty((1, shape[0]*shape[1]), dtype= np.float32)
# Get kernel function
gaussian2D = kernel.get_function("gaussian2D")
# Define block, grid and compute
blockDim = (32, 32, 1) # 1024 threads in total
dx, mx = divmod(shape[1], blockDim[0])
dy, my = divmod(shape[0], blockDim[1])
gridDim = ((dx + (mx>0)), (dy + (my>0)), 1)
# Kernel function
gaussian2D (
cuda.Out(output), cuda.In(x), cuda.In(y), cuda.In(sigma),
cuda.In(n_rows), cuda.In(n_cols),
block=blockDim, grid=gridDim)
return output
point = (5, 5)
sigma = 3.0
shape = (10, 10)
result = gpu_gaussian2D(point, sigma, shape)
检查n_cols的打印值后，它不是预期的10。任何人都可以帮助我，我无法弄清楚这里出了什么问题。
【问题讨论】：