A mixed precision Jacobi SVD algorithm
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915278524252160 |
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| author | Gao, Weiguo Ma, Yuxin Shao, Meiyue |
| author_facet | Gao, Weiguo Ma, Yuxin Shao, Meiyue |
| contents | We propose a mixed precision Jacobi algorithm for computing the singular value decomposition (SVD) of a dense matrix. After appropriate preconditioning, the proposed algorithm computes the SVD in a lower precision as an initial guess, and then performs one-sided Jacobi rotations in the working precision as iterative refinement. By carefully transforming a lower precision solution to a higher precision one, our algorithm achieves about 2 times speedup on the x86-64 architecture compared to the usual one-sided Jacobi SVD algorithm in LAPACK, without sacrificing the accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_04626 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | A mixed precision Jacobi SVD algorithm Gao, Weiguo Ma, Yuxin Shao, Meiyue Numerical Analysis 65F15 We propose a mixed precision Jacobi algorithm for computing the singular value decomposition (SVD) of a dense matrix. After appropriate preconditioning, the proposed algorithm computes the SVD in a lower precision as an initial guess, and then performs one-sided Jacobi rotations in the working precision as iterative refinement. By carefully transforming a lower precision solution to a higher precision one, our algorithm achieves about 2 times speedup on the x86-64 architecture compared to the usual one-sided Jacobi SVD algorithm in LAPACK, without sacrificing the accuracy. |
| title | A mixed precision Jacobi SVD algorithm |
| topic | Numerical Analysis 65F15 |
| url | https://arxiv.org/abs/2209.04626 |