Question

I’m using CUDA to run a problem where I need a complex equation with many input matrices. Each matrix has an ID depending on its set (between 1 and 30, there are 100,000 matrices) and the result of each matrix is stored in a float[N] array where N is the number of input matrices.

After this, the result I want is the sum of every float in this array for each ID, so with 30 IDs there are 30 result floats.

Any suggestions on how I should do this?

Right now, I read the float array (400kb) back to the host from the device and run this on the host:

// Allocate result_array for 100,000 floats on the device
// CUDA process input matrices
// Read from the device back to the host into result_array
float result[10] = { 0 };
for (int i = 0; i < N; i++)
{
    result[input[i].ID] += result_array[i];
}

But I’m wondering if there’s a better way.

Answer 1

You could use cublasSasum() to do this – this is a bit easier than adapting one of the SDK reductions (but less general of course). Check out the CUBLAS examples in the CUDA SDK.