A multi-GPU enabled solver in Kronecker product form for multiphysics problems.
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| Název: | A multi-GPU enabled solver in Kronecker product form for multiphysics problems. |
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| Autoři: | Ma, Wenpeng, Zhao, Siyuan, Le, Xiaofan, Yuan, Wu |
| Zdroj: | Scientific Reports; 12/10/2025, Vol. 15 Issue 1, p1-19, 19p |
| Témata: | KRONECKER products, GRAPHICS processing units, PARALLEL programming, FLUID dynamics, ALGORITHMS, SPARSE matrices, SCIENTIFIC computing, MATHEMATICAL optimization |
| Abstrakt: | Modern engineering and scientific computing often requires solving sparse linear systems containing point-block matrix to model multiphysics problems. The space-time parallel method is popular and attractive in fluid dynamics, fitting parallel computers very well. In this paper, we design and implement a parallel, multi-GPU enabled GMRES solver for solving linear systems in the Kronecker product form arising from the domain decomposition based space-time parallel methods. To improve the efficiency of the solver, we also design a set of optimization strategies for Sparse Matrix-Vector Multiplication (SpMV) in Kronecker product form. These include: (1) enhancing the Compute-to-Memory Access Ratio (CMAR) to fully utilize the high bandwidth nature of the GPU during the computation phase and (2) introducing a parallel buffering scheme and a pre-mapping algorithm to enable the use of GPU-Direct for accelerating the communication phase. We conducted experiments on 1, 2, 4, and 8 GPUs and compared the performance of OKP-Solver with the cuSPARSE based implementation. On the V100 platform, the Kronecker product based SpMV computation () achieves speedups of , , , and on 1, 2, 4, and 8 GPUs, respectively, while the communication time () achieves , , and on 2, 4, and 8 GPUs, respectively. On the A100 platform, achieves speedups of , , , and , while achieves , , and. The overall solver runtime () achieves speedups of , , , and on V100, and , , , and on A100, for 1, 2, 4, and 8 GPUs, respectively. [ABSTRACT FROM AUTHOR] |
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| Databáze: | Complementary Index |
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