>(ad, bd, cd, N); cudaMemcpy(c, cd, size, cudaMemcpyDeviceToHost); for (i = 0; i Download presentation Presentation is loading. Please wait. Published byRoy Ross
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Lecture 6: Shared-memory Computing with GPU
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Free download NVIDIA CUDA https://developer.nvidia.com/cud a-downloads CUDA programming on visual studio 2010 START: download NVIDIA CUDA
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#include "cuda_runtime.h" #include "device_launch_parameters.h" #include const int N = 1024; const int blocksize = 16; __global__ void add_matrix(float* a, float *b, float *c, int N) { int i = blockIdx.x * blockDim.x + threadIdx.x; int j = blockIdx.y * blockDim.y + threadIdx.y ; int index = i+j*N; if (i< N && j <N) c[index] = a[index] + b[index]; } int main() { float *a = new float[N*N]; float *b = new float[N*N]; float *c = new float[N*N]; int i, j; for (int i = 0; i < N*N; ++i) { a[i] = 1.0f; b[i] = 3.5f; } float *ad, *bd, *cd; const int size = N*N*sizeof(float); cudaMalloc( (void**)&ad, size); cudaMalloc( (void**)&bd, size); cudaMalloc( (void**)&cd, size); cudaMemcpy(ad, a, size, cudaMemcpyHostToDevice); cudaMemcpy(bd, b, size, cudaMemcpyHostToDevice); dim3 dimBlock(blocksize, blocksize); dim3 dimGrid(N/dimBlock.x, N/dimBlock.y); add_matrix >>(ad, bd, cd, N); cudaMemcpy(c, cd, size, cudaMemcpyDeviceToHost); for (i = 0; i<N; i++) { for (j=0; j<N; j++) printf("%f", c[i,j]); printf("\n"); }; delete[] a; delete b; delete [] c; return EXIT_SUCCESS; } START: Matrix Addition Global memory (i,j ) height dimBlock.y width dimBlock.x threadIdx.x threadIdy.y
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Memory Allocation Example
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(xIdx,yIdy ) height dimBlock.y width dimBlock.x threadIdx.x threadIdy.y
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Memory Allocation Example
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(3) Read from the shared memory & write to global memory (1) Read from global memory & write to block shared memory (2) Transposed address (X,Y) height yBlock width xBlock Global memory (threadIDx.y, threadIDx.x) shared memory (threadIDx.x, threadIDx.y) (1) (2)
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Memory Allocation Example (X,Y) height yBlock width xBlock (threadIDx.x, threadIDx.y) (threadIDx.y, threadIDx.x) (y,x) height yBlock width xBlock Global memory shared memory Global memory (1) (2) (3) (1) (2) (3)
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Exercise (1) Compile and execute program Matrix Addition. (2) Write a complete version of the program for Memory Allocation. (3) Write a program for calculate π, where the number of intervals =.
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Lecture 6: Shared-memory Computing with GPU. Free download NVIDIA CUDA https://developer.nvidia.com/cud a-downloads CUDA programming on visual studio.
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Presentation on theme: "Lecture 6: Shared-memory Computing with GPU. Free download NVIDIA CUDA https://developer.nvidia.com/cud a-downloads CUDA programming on visual studio."— Presentation transcript:
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