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This paper presents a performance modeling and optimization analysis tool to predict and optimize the performance of sparse matrix-vector multiplication (SpMV) on GPUs. We make the following contributions: 1) We present an integrated analytical and profile-based performance modeling to accurately predict the kernel execution times of CSR, ELL, COO, and HYB SpMV kernels. Our proposed approach is general,...
This paper presents an integrated analytical and profile-based CUDA performance modeling approach to accurately predict the kernel execution times of sparse matrix-vector multiplication for CSR, ELL, COO, and HYB SpMV CUDA kernels. Based on our experiments conducted on a collection of 8 widely-used testing matrices on NVIDIA Tesla C2050, the execution times predicted by our model match the measured...
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