共享内存并行谱延迟校正的并行性能
Parallel performance of shared memory parallel spectral deferred corrections
AI总结：
本文研究了共享内存中并行谱延迟校正方法的性能，通过OpenMP实现并对比了不同积分器的速度提升。
摘要：
我们研究了并行谱延迟校正方法的并行性能，这是一种为初始值问题的数值解提供小规模并行性的数值方法。该方案应用于浅水方程，并使用隐式-显式拆分，为了提高效率，将快速模式隐式积分，慢模式显式积分。我们描述了基于OpenMP的并行谱延迟校正方法的实现，用于两个已建立的模拟代码：基于有限体积的运营海洋模型ICON和基于球谐分析的科研代码SWEET。我们还开发了一个性能模型，并在Jülich Supercomputing Centre的JUSUF（SWEET）和JUWELS（ICON）系统单节点上对我们的实现进行了基准测试。在一系列精度范围内展示了求解时间的减少。对于ICON，我们展示了在使用OpenMP循环并行化的情况下，相对于当前使用的Adams-Bashforth-2积分器的速度提升。对于SWEET，我们展示了相对于串行谱延迟校正和一个二阶隐式-显式积分器的速度提升。
英文摘要：
We investigate the parallel performance of Parallel Spectral Deferred corrections, a numerical approach that provides small-scale parallelism for the numerical solution of initial value problems. The scheme is applied to the shallow-water equation and uses an implicit-explicit splitting that, in order to be efficient, integrates fast modes implicitly and slow modes explicitly. We describe parallel \OpenMP-based implementations of parallel Spectral Deferred Corrections for two well established simulation codes: the finite volume based operational ocean model \ICON and the spherical harmonics based research code \SWEET. We also develop a performance model and benchmark our implementations on a single node of the JUSUF (\SWEET) and JUWELS (\ICON) system at Jülich Supercomputing Centre. A reduction of time-to-solution across a range of accuracies is demonstrated. For \ICON, we show speedup over the currently used Adams--Bashforth-2 integrator with \OpenMP loop parallelization. For \SWEET, we show speedup over serial Spectral Deferred Corrections and a second order implicit-explicit integrator.
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