Sparse Matrix-Vector Multiplication on GPGPUs
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 matrix-vector mul...
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| Published in | ACM transactions on mathematical software Vol. 43; no. 4; pp. 1 - 49 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
31.12.2017
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| Online Access | Get full text |
| ISSN | 0098-3500 1557-7295 1557-7295 |
| DOI | 10.1145/3017994 |
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| Abstract | 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 matrix-vector 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 article, 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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| AbstractList | 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 matrix-vector 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 article, 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. |
| Author | Fanfarillo, Alessandro Cardellini, Valeria Filippone, Salvatore Barbieri, Davide |
| Author_xml | – sequence: 1 givenname: Salvatore surname: Filippone fullname: Filippone, Salvatore organization: Cranfield University, Cranfield, United Kingdom – sequence: 2 givenname: Valeria surname: Cardellini fullname: Cardellini, Valeria organization: Università degli Studi di Roma “Tor Vergata”, Roma, Italy – sequence: 3 givenname: Davide surname: Barbieri fullname: Barbieri, Davide organization: Università degli Studi di Roma “Tor Vergata”, Roma, Italy – sequence: 4 givenname: Alessandro surname: Fanfarillo fullname: Fanfarillo, Alessandro organization: Università degli Studi di Roma “Tor Vergata”, Roma, Italy |
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