Electronic Transactions on Numerical Analysis | |
Porting an aggregation-based algebraic multigrid method to GPUs | |
article | |
Abdeselam El Haman Abdeselam1  Artem Napov1  Yvan Notay1  | |
[1] Université Libre de Bruxelles | |
关键词: multigrid; linear systems; iterative methods; AMG; preconditioning; parallel computing; GPU; | |
DOI : 10.1553/etna_vol55s687 | |
学科分类:数学(综合) | |
来源: Kent State University * Institute of Computational Mathematics | |
【 摘 要 】
We present a hybrid GPU-CPU version of the AGMG software, a popular algebraic multigrid (AMG) solver which implements an aggregation-based AMG method. With the new implementation, the solution stage runs on a GPU, except operations on the coarsest grid, which are executed on a CPU. To maximize the speedup, two novel %new features are introduced. On the one hand, $\ell_1$-Jacobi smoothing is combined with polynomial acceleration (or polynomial smoothing), leading to improved performance compared with standard $\ell_1$-Jacobi smoothing, while not requiring to compute eigenvalue estimates as standard polynomial smoothing does. On the other hand, besides the K-cycle used in standard AGMG, we introduce the relaxed W-cycle, which tends to combine the advantages of the K-cycle and the standard W-cycle. Numerical results show that the new implementation inherits the robustness of the original AGMG software, while bringing significant speedups on GPUs. A comparison with AmgX, a reference AMG solver from NVIDIA, suggests that the presented hybrid GPU-CPU version of AGMG is more robust and often significantly faster in the solution stage.
【 授权许可】
Unknown
【 预 览 】
Files | Size | Format | View |
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RO202307010000604ZK.pdf | 493KB | download |