STM-Multifrontal QR: Streaming Task Mapping Multifrontal QR Factorization Empowered by GCN
Multifrontal QR algorithm, which consists of symbolic analysis and numerical factorization, is a high-performance algorithm for orthogonal factorizing sparse matrix. In this work, a graph convolutional network (GCN) for adaptively selecting the optimal reordering algorithm is proposed in symbolic an...
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| Vydáno v: | SC21: International Conference for High Performance Computing, Networking, Storage and Analysis s. 01 - 14 |
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| Jazyk: | angličtina |
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14.11.2021
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| ISSN: | 2167-4337 |
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| Abstract | Multifrontal QR algorithm, which consists of symbolic analysis and numerical factorization, is a high-performance algorithm for orthogonal factorizing sparse matrix. In this work, a graph convolutional network (GCN) for adaptively selecting the optimal reordering algorithm is proposed in symbolic analysis. Using our GCN adaptive classifier, the average numerical factorization time is reduced by 20.78% compared with the default approach, and the additional memory overhead is approximately 4% higher than that of prior work. Moreover, for numerical factorization, an optimized tasks stream parallel processing strategy is proposed and a more efficient computing task mapping framework for NUMA architecture is adopted in this paper, which called STM-Multifrontal QR factorization. Numerical experiments on the TaiShan Server show average 1.22x performance gains over the original SuiteSparseQR. Nearly 80% of datasets have achieved better performance compared with the MKL sparse QR on Intel Xeon 6248. |
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| AbstractList | Multifrontal QR algorithm, which consists of symbolic analysis and numerical factorization, is a high-performance algorithm for orthogonal factorizing sparse matrix. In this work, a graph convolutional network (GCN) for adaptively selecting the optimal reordering algorithm is proposed in symbolic analysis. Using our GCN adaptive classifier, the average numerical factorization time is reduced by 20.78% compared with the default approach, and the additional memory overhead is approximately 4% higher than that of prior work. Moreover, for numerical factorization, an optimized tasks stream parallel processing strategy is proposed and a more efficient computing task mapping framework for NUMA architecture is adopted in this paper, which called STM-Multifrontal QR factorization. Numerical experiments on the TaiShan Server show average 1.22x performance gains over the original SuiteSparseQR. Nearly 80% of datasets have achieved better performance compared with the MKL sparse QR on Intel Xeon 6248. |
| Author | Li, Kenli Wang, Haotian Lin, Shengle Tsai, Qinyun Yang, Wangdong |
| Author_xml | – sequence: 1 givenname: Shengle surname: Lin fullname: Lin, Shengle email: lsl036@hnu.edu.cn organization: Hunan University,Changsha,China – sequence: 2 givenname: Wangdong surname: Yang fullname: Yang, Wangdong email: yangwangdong@hnu.edu.cn organization: Hunan University,Changsha,China – sequence: 3 givenname: Haotian surname: Wang fullname: Wang, Haotian email: wanghaotian@hnu.edu.cn organization: Hunan University,Changsha,China – sequence: 4 givenname: Qinyun surname: Tsai fullname: Tsai, Qinyun email: hnutsai@hnu.edu.cn organization: Hunan University,Changsha,China – sequence: 5 givenname: Kenli surname: Li fullname: Li, Kenli email: lkl@hnu.edu.cn organization: Hunan University,Changsha,China |
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| Snippet | Multifrontal QR algorithm, which consists of symbolic analysis and numerical factorization, is a high-performance algorithm for orthogonal factorizing sparse... |
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| SubjectTerms | Approximation algorithms Classification algorithms Computer architecture Graph Convolutional Network High performance computing Multicore processing Multifrontal QR Factorization NUMA Multicore Architecture Parallel processing Performance gain Task Mapping Optimization Task Stream Processing |
| Title | STM-Multifrontal QR: Streaming Task Mapping Multifrontal QR Factorization Empowered by GCN |
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