如何在matlab中将我的两个矩阵点积的cpu代码转换为GPU

Question

我想在 GPUarray 中取两个矩阵的加权和以加快速度。例如我在 cpu 上的代码如下：

mat1 = rand(19,19);

mat2= rand(19,19);

Receptive_fieldsize = [4,3]; 

overlap = 1;

Output = GetweightedSum(mat1,mat2, Receptive_fieldsize,overlap); %this will output in an 6x6 matrix

我的函数体在哪里：

function Output = GetweightedSum(mat1,mat2, RF,overlap)

gap = RF(1) - overlap;
size_mat = size(mat1);
output_size=[6,6];
for u=1: output_size(1)
    for v=1: output_size(2)
        min_u = (u - 1) * gap + 1;
        max_u = (u - 1) * gap + RF(1);
        min_v = (v - 1) * gap + 1;
        max_v = (v - 1) * gap + RF(2);

       input1 = mat1(min_u:max_u,min_v:max_v);
       input2 = mat2(min_u:max_u,min_v:max_v); 
       Output(u,v) = sum(sum(input1 .*input2));

   end
end

如何将其转换为 GPUfunciton。我可以直接做吗，或者我可以在GPU代码中使用for循环。我对 GPU 完全陌生，所以对此一无所知。 如果有人指导我，或者将上面的代码更改为 GPU 功能的参考，我将不胜感激，以便我可以从中学习。 问候

Answer 1

看看代码和它们旁边的 cmets 是否对你有意义 -

function Output = GetweightedSumGPU(mat1,mat2, RF,overlap)

%// Create parameters
gap = RF(1) - overlap;
output_size=[6,6];
sz1 = output_size(1);
sz2 = output_size(2);

nrows = size(mat1,1); %// get number of rows in mat1

%// Copy data to GPU
gmat1 = gpuArray(mat1);
gmat2 = gpuArray(mat2);

start_row_ind = gpuArray([1:RF(1)]'); %//' starting row indices for each block
col_offset = gpuArray([0:RF(2)-1]*nrows); %// column offset for each block

%// Linear indices for each block
ind = bsxfun(@plus,start_row_ind,col_offset);

%// Linear indices along rows and columns respectively
ind_rows = bsxfun(@plus,ind(:),[0:sz1-1]*gap);
ind_rows_cols = bsxfun(@plus,ind_rows,permute([0:sz2-1]*gap*nrows,[1 3 2]));

%// Elementwise multiplication, summing and gathering back result to CPU
Output = gather(reshape(sum(gmat1(ind_rows_cols).*gmat2(ind_rows_cols),1),sz1,sz2));

return;