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OpenCL将数组从内存copy到显存

2018-06-17 23:12:51来源：未知阅读 ()

本来想对上一篇博客做优化，优化效果不明显。但知识点还是要记一下。

初衷是想把上一篇博客中定义域的计算搬到CPU来计算，因为定义域的计算对于每一个kernel都是一样的，所以直接读取应该是可以进一步减小kernel的执行时间的。

我的思路的初衷是将这块的数据送到显存之后再送到寄存器中，从寄存器读取的时间应该是很快的，通过这样把计算的时间改为读取的时间。当然，读取寄存器的时间是否比计算更短，这个确实应该质疑，但是对于比较复杂的计算，我觉得直接读应该是比计算更快的。而对于这部分数据，CPU计算应该会比GPU更快。当然，还应当考虑数据量的大小，从内存搬到显存也是需要时间的。

1.C++代码

..................

int ksize = 11;
float sigma_d = 3.0;
float *dkl = new float[ksize*ksize];
for (int i = -ksize/2; i <= ksize/2; i++){
    for (int j = -ksize/2; j <= ksize/2; j++){
        dkl[(i+ksize/2)*ksize + (j+ksize/2)] = -(i*i + j*j) / (2 * sigma_d*sigma_d);
    }
}

cl_mem d_dkl;
d_dkl = clCreateBuffer(context, CL_MEM_READ_ONLY, ksize*ksize*sizeof(float), NULL,NULL);
clEnqueueWriteBuffer(commandQueue, d_dkl, CL_TRUE, 0, ksize*ksize*sizeof(float), dkl, 0, NULL, NULL);

........................


errNum |= clSetKernelArg(kernel, 2, sizeof(cl_mem), &d_dkl);
errNum |= clSetKernelArg(kernel, 3, sizeof(cl_mem), &ksize);

........................

delete[] dkl;

...................

主要就是clCreateBuffer函数和clEnqueueWriteBuffer函数的用法。

2.kernel代码

const sampler_t sampler = CLK_ADDRESS_CLAMP_TO_EDGE | CLK_FILTER_NEAREST;

kernel void bilateralBlur(read_only image2d_t src, write_only image2d_t dst, __constant float* dkl, int ksize)  
{
    int x = (int)get_global_id(0);  
    int y = (int)get_global_id(1);  
    if (x >= get_image_width(src) || y >= get_image_height(src))  
        return;  

    float sigma_d = 3.0;
    float sigma_r = 0.1;

    float4 fij = read_imagef(src, sampler, (int2)(x, y));

    float alpha = 0.2;
    float4 fkl;
    float4 rkl;
    float4 wkl;

    int index = 0;

    float4 numerator = (float4)(0.0f,0.0f,0.0f,0.0f);
    float4 denominator = (float4)(1.0f, 1.0f, 1.0f, 1.0f);
    for (int K = -ksize / 2; K <= ksize / 2; K++)
    {
        for (int L = -ksize / 2; L <= ksize / 2; L++)
        {
            fkl = read_imagef(src, sampler, (int2)(x + K, y + L));

            rkl.x = -(fij.x - fkl.x)*(fij.x - fkl.x) / (2 * sigma_r*sigma_r);
            rkl.y = -(fij.y - fkl.y)*(fij.y - fkl.y) / (2 * sigma_r*sigma_r);
            rkl.z = -(fij.z - fkl.z)*(fij.z - fkl.z) / (2 * sigma_r*sigma_r);

            wkl.x = exp(-dkl[index] + rkl.x);
            wkl.y = exp(-dkl[index] + rkl.y);
            wkl.z = exp(-dkl[index] + rkl.z);
            index++;

            numerator.x += fkl.x * wkl.x;
            numerator.y += fkl.y * wkl.y;
            numerator.z += fkl.z * wkl.z;

            denominator.x += wkl.x;
            denominator.y += wkl.y;
            denominator.z += wkl.z;
        }
    }
    
    float4 gij = (float4)(0.0f, 0.0f, 0.0f, 1.0f);
    if (denominator.x > 0 && denominator.y > 0 && denominator.z)
    {
        gij.x = numerator.x / denominator.x;
        gij.y = numerator.y / denominator.y;
        gij.z = numerator.z / denominator.z;

        gij.x = fij.x*alpha + gij.x*(1.0 - alpha);
        gij.y = fij.y*alpha + gij.y*(1.0 - alpha);
        gij.z = fij.z*alpha + gij.z*(1.0 - alpha);
    }

    write_imagef(dst, (int2)(x, y), gij);
}