Getting Started with CUDA

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CUFFT Types and Definitions cufftHandle Type used to store and access CUFFT plans cufftResults Enumeration of API function return values cufftReal single-precision, real datatype cufftComplex single-precision, complex datatype Real and complex transforms CUFFT_C2C, CUFFT_C2R, CUFFT_R2C Directions CUFFT_FORWARD, CUFFT_INVERSE © 2008 NVIDIA Corporation.
CUFFT Example #include #include #include "cufft.h" int main(int argc, char *argv[]) { cufftComplex *a_h, *a_d; cufftHandle plan; int N = 1024, batchSize = 10; int i, nBytes; double maxError; nBytes = sizeof(cufftComplex)*N*batchSize; a_h = (cufftComplex *)malloc(nBytes); for (i=0; i < N*batchSize; i++) { } a_h[i].x = sinf(i); a_h[i].y = cosf(i); cudaMalloc((void **)&a_d, nBytes); cudaMemcpy(a_d, a_h, nBytes, cudaMemcpyHostToDevice); } cufftPlan1d(&plan, N, CUFFT_C2C, batchSize); cufftExecC2C(plan, a_d, a_d, CUFFT_FORWARD); cufftExecC2C(plan, a_d, a_d, CUFFT_INVERSE); cudaMemcpy(a_h, a_d, nBytes, // check error - normalize cudaMemcpyDeviceToHost); for (maxError = 0.0, i=0; i < N*batchSize; i++) { } maxError = max(fabs(a_h[i].x/N-sinf(i)), maxError); maxError = max(fabs(a_h[i].y/N-cosf(i)), maxError); printf("Max fft error = %g\n", maxError); cufftDestroy(plan); free(a_h); cudaFree(a_d); return 0; © 2008 NVIDIA Corporation.
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Getting Started with CUDA Greg Ruet
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Outline CUDA programming model Basi
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CUDA Kernels and Threads Parallel p
Page 7 and 8:
Thread Cooperation The Missing Piec
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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 21 and 22: Outline CUDA programming model Basi
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 and 42: Minimal Kernels __global__ void min
Page 43 and 44: Host Synchronization All kernel lau
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 59 and 60: Outline CUDA programming model Basi
Page 61: More on CUFFT For 2D and 3D transfo
Page 65: Getting Started with CUDA Greg Ruet
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Getting Started with CUDA

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