12. The C++ compiler is obsessed with optimization: In this case, it will auto-vectorize the loop

13. B[0] B[1] B[2] B[3] A[0] A[1] A[2] A[3] A[0] + B[0] A[1] + B[1] A[2] + B[2] A[3] + B[3] + xmm1 xmm0 “addps xmm1, xmm0 “ xmm1 for (i = 0; i < 1000; i++) { C[i] = A[i]+B[i] } for (i = 0; i < 1000; i+=4) { C[i:i+3] = A[i:i+3]+B[i:i+3] }

15. info C5002: loop not vectorized due to reason ‘501’

17. info C5001: loop vectorized

20. Lots of triangles: we have less than 15ms to “turn a page” in real time; we need to parallelize this algorithm C++ AMP is a good candidate, since the data size is pretty large

21. We’re looping over each triangle This set of operations is safe, because it works on a single triangle at each time, no races But here we’re updating vertexes which are shared between triangles -> race! This algorithm only works on a single thread

22. for each triangle for each vertex

23. We use C++ AMP Same as before, we calculate the normals for each triangle We collect the normals into a temporary array, which stay in GPU memory

24. We go over each vertex, so no races In sumTriangleNormals, we fetch the normals from tempTriangleNormals, i.e., the temporary we kept on the GPU memory

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