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Published in:
2013 | OriginalPaper | Chapter
Optimization of Sparse Matrix-Vector Multiplication for CRS Format on NVIDIA Kepler Architecture GPUs
Authors : Daichi Mukunoki, Daisuke Takahashi
Published in: Computational Science and Its Applications – ICCSA 2013
Publisher: Springer Berlin Heidelberg
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Sparse matrix-vector multiplication (SpMV) is an important operation in scientific and engineering computing. This paper presents optimization techniques for SpMV for the Compressed Row Storage (CRS) format on NVIDIA Kepler architecture GPUs using CUDA. Our implementation is based on an existing method proposed for the Fermi architecture, an earlier generation of the GPU, and takes advantage of some of the new features of the Kepler architecture. On a Tesla K20 Kepler architecture GPU on double precision operations, our implementation is, on average, approximately 1.29 times faster than that the Fermi optimized implementation for 200 different types of matrices. As a result, our implementation outperforms the NVIDIA cuSPARSE library’s CRS format SpMV in CUDA 5.0 on 174 of the 200 matrices, and the average speedup compared to the cuSPARSE SpMV routine across all 200 matrices is approximately 1.45.