## "Hello World" in C++ AMP

Sun, June 26, 2011, 06:02 PM under GPGPU | ParallelComputing

**UPDATE**: I encourage you to visit a newer and better post with a C++ AMP matrix multiplication example.

Some say that the equivalent of "hello world" code in the data parallel world is matrix multiplication :)

Below is the before C++ AMP and after C++ AMP code. For more on what it all means, watch the recording of my C++ AMP introduction (the example below is part of the session).

void MatrixMultiply(vector<float>& vC, const vector<float>& vA, const vector<float>& vB, int M, int N, int W ) { for (int y = 0; y < M; y++) { for (int x = 0; x < N; x++) { float sum = 0; for(int i = 0; i < W; i++) { sum += vA[y * W + i] * vB[i * N + x]; } vC[y * N + x] = sum; } } }Change the function to use C++ AMP and hence offload the computation to the GPU, and now the calling code (which I am not showing) needs no changes and the overall operation gives you really nice speed up for large datasets…

#include <amp.h> using namespace concurrency; void MatrixMultiply(vector<float>& vC, const vector<float>& vA, const vector<float>& vB, int M, int N, int W ) { array_view<const float,2> a(M, W, vA); array_view<const float,2> b(W, N, vB); array_view<writeonly<float>,2> c(M, N, vC); parallel_for_each( c.grid, [=](index<2> idx) mutable restrict(direct3d) { float sum = 0; for(int i = 0; i < a.x; i++) { sum += a(idx.y, i) * b(i, idx.x); } c[idx] = sum; } ); }

Again, you can understand the elements above, by using my C++ AMP presentation slides and recording…

Stay tuned for more…

Tanveer, if you have really large matrices, then strassen would be a good option (you'd partition the data on the CPU and make multiple gpu kernel invocations). That is obviously not a Hello World example (the title of this blog post). When we ship bits, I'll be sure to include an example like that... thanks for the idea.

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While 3 nested for-loops multiplication is the easiest, it is not the most efficient.