Getting Started with CUDA

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Increment Array Example CPU program CUDA program void inc_cpu(int *a, int N) { int idx; } for (idx = 0; idx
Host Synchronization All kernel launches are asynchronous control returns to CPU immediately kernel executes after all previous CUDA calls have completed cudaMemcpy() is synchronous control returns to CPU after copy completes copy starts after all previous CUDA calls have completed cudaThreadSynchronize() blocks until all previous CUDA calls complete © 2008 NVIDIA Corporation.
Page 1 and 2: Getting Started with CUDA Greg Ruet
Page 3 and 4: Outline CUDA programming model Basi
Page 5 and 6: CUDA Kernels and Threads Parallel p
Page 7 and 8: Thread Cooperation The Missing Piec
Page 9 and 10: Transparent Scalability Hardware is
Page 11 and 12: 10-Series Architecture 240 thread p
Page 13 and 14: Physical Memory Layout Host “Loca
Page 15 and 16: Key Parallel Abstractions in CUDA T
Page 17 and 18: CUDA Installation CUDA installation
Page 19 and 20: Compiling CUDA Code C/C++ CUDA Appl
Page 23 and 24: GPU Memory Allocation / Release cud
Page 25 and 26: Data Movement Example int main(void
Page 35 and 36: Executing Code on the GPU Kernels a
Page 37 and 38: Launching Kernels Modified C functi
Page 39 and 40: CUDA Built-in Device Variables All
Page 41: Minimal Kernels __global__ void min
Page 45 and 46: Variable Qualifiers (GPU code) __de
Page 47 and 48: GPU Thread Synchronization void __s
Page 49 and 50: Built-in Vector Types Can be used i
Page 51 and 52: CUDA Programming Resources We only
Page 53 and 54: CUBLAS Implementation of BLAS (Basi
Page 55 and 56: Using CUBLAS Interface to CUBLAS li
Page 57 and 58: sgemmExample.c #include #include
Page 61 and 62: More on CUFFT For 2D and 3D transfo
Page 63 and 64: CUFFT Example #include #include #
Page 65: Getting Started with CUDA Greg Ruet

Getting Started with CUDA

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