17107

Sparse Matrix-Vector Multiplication on GPGPUs

Salvatore Filippone, Valeria Cardellini, Davide Barbieri, Alessandro Fanfarillo
Cranfield University
ACM Transactions on Mathematical Software, Volume 43, Issue 4, 2017

@article{filippone2017sparse,

   title={Sparse Matrix-Vector Multiplication on GPGPUs},

   author={Filippone, Salvatore and Cardellini, Valeria and Barbieri, Davide and Fanfarillo, Alessandro},

   journal={ACM Transactions on Mathematical Software (TOMS)},

   volume={43},

   number={4},

   pages={30},

   year={2017},

   publisher={ACM}

}

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The multiplication of a sparse matrix by a dense vector (SpMV) is a centerpiece of scientific computing applications: it is the essential kernel for the solution of sparse linear systems and sparse eigenvalue problems by iterative methods. The efficient implementation of the sparse matrixvector multiplication is therefore crucial and has been the subject of an immense amount of research, with interest renewed with every major new trend in high performance computing architectures. The introduction of General Purpose Graphics Processing Units (GPGPUs) is no exception, and many articles have been devoted to this problem. With this paper we provide a review of the techniques for implementing the SpMV kernel on GPGPUs that have appeared in the literature of the last few years. We discuss the issues and tradeoffs that have been encountered by the various researchers, and a list of solutions, organized in categories according to common features. We also provide a performance comparison across different GPGPU models and on a set of test matrices coming from various application domains.
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