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arXiv 2412.16638math.NAcs.NAcs.PF
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混合精度Runge-Kutta方法求解偏微分方程的性能评估

Performance evaluation of mixed-precision Runge-Kutta methods for the solution of partial differential equations

Ivo Dravins, Marcel Koch, Victoria Griehl, Katharina Kormann

AI总结:

本文数值研究了专为混合精度算术设计的Runge-Kutta方法在偏微分方程求解中的性能,利用Ginkgo库在GPU和CPU架构上进行3D问题测试,发现可取得显著加速但性能取决于求解器参数和软件内核。

摘要:

本工作聚焦于对一类最近发表的、专为混合精度算术设计的Runge-Kutta方法进行数值研究。我们在现代硬件上采用这些方法求解偏微分方程。特别地,我们研究了使用混合精度可实现的加速效果,以及方法的算法兼容性与计算硬件之间的依赖关系。我们使用最先进的软件,利用专为融入混合精度算术而设计的Ginkgo库,并在GPU和CPU架构上对3D问题进行数值测试。我们表明可以取得显著的加速效果,但性能取决于求解器参数和软件内核的性能。

英文摘要:

This work focuses on the numerical study of a recently published class of Runge-Kutta methods designed for mixed-precision arithmetic. We employ the methods in solving partial differential equations on modern hardware. In particular we investigate what speedups are achievable by the use of mixed precision and the dependence of the methods algorithmic compatibility with the computational hardware. We use state-of-the-art software, utilizing the Ginkgo library, which is designed to incorporate mixed precision arithmetic, and perform numerical tests of 3D problems on both GPU and CPU architectures. We show that significant speedups can be achieved but that performance depends on solver parameters and performance of software kernels.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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