Asynchronous iterations are more and more investigated for both scaling and fault-resilience purpose on high performance computing platforms. While so far, they have been exclusively applied within space domain decomposition frameworks, this paper advocates a novel application direction targeting time-decomposed time-parallel approaches. Specifically, an asynchronous iterative model is derived from the Parareal scheme, for which convergence and speedup analysis are then conducted. It turned out that Parareal and async-Parareal feature very close convergence conditions, asymptotically equivalent, including the finite-time termination property. Based on a computational cost model aware of unsteady communication delays, our speedup analysis shows the potential performance gain from asynchronous iterations, which is confirmed by some experimental case of heat evolution on a homogeneous supercomputer. This primary work clearly suggests possible further benefits from asynchronous iterations.
翻译:在高性能计算平台上,对非同步迭代进行了越来越多的调查,以进行缩放和故障恢复功能,尽管迄今为止,这些迭代完全用于空间域分解框架,但本文主张针对时间分解时间相隔法采用新的应用方向,具体地说,一个非同步迭代模式源自于Parareal计划,随后对该计划进行了趋同和加速分析。结果发现,Parayreal和Async-Parareal具有非常密切的趋同条件,在时间终止属性等同的状态下,不时效。根据一个计算成本模型,我们意识到不稳定的通信延迟,我们的加速分析显示了从不同步的迭代法中获得的潜在性能收益,这一点得到了同质超级计算机热演进实验性案例的证实。这一主要工作清楚地表明,从无同步迭代法的迭代法中可能进一步获益。