High Performance Computing with CUDA, Part of Supercomputing

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CUDA In One Slide Thread © NVIDIA Corporation 2009 per-thread local memory Global barrier Local barrier Kernel foo() . . . Kernel bar() . . . Block per-device global memory per-block shared memory
CUDA C Example void saxpy_serial(int n, float a, float *x, float *y) { for (int i = 0; i < n; ++i) y[i] = a*x[i] + y[i]; } // Invoke serial SAXPY kernel saxpy_serial(n, 2.0, x, y); __global__ void saxpy_parallel(int n, float a, float *x, float *y) { int i = blockIdx.x*blockDim.x + threadIdx.x; if (i < n) y[i] = a*x[i] + y[i]; } // Invoke parallel SAXPY kernel with 256 threads/block int nblocks = (n + 255) / 256; saxpy_parallel(n, 2.0, x, y); Serial C Code Parallel C Code
Page 2 and 3: Welcome! GPUs have become a major f
Page 4 and 5: GFLOPS Motivation CPU Intel Core i7
Page 6 and 7: CUDA is everywhere Over 270 univers
Page 8 and 9: GPUs in high-performance computing
Page 10 and 11: Emerging HPC Products New class of
Page 12 and 13: Tutorial prerequisites Tutorial int
Page 14 and 15: Schedule 8:30 Introduction Welcome,
Page 16: Schedule: case studies 2:30 Molecul
Page 21 and 22: Example: Parallel Reduction Summing
Page 23: Parallel Reduction for 1 Block // I
Page 26 and 27: Time Hardware Thread Scheduling Con
Page 28: Conclusion GPUs are massively paral

High Performance Computing with CUDA, Part of Supercomputing

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