1 Computational Science Laboratory , Institute for Informatics , Faculty of Science , University of Amsterdam , The Netherlands.
2 High Performance Computing Department , ITMO University , St Petersburg , Russia.
Philos Trans A Math Phys Eng Sci. 2019 Apr 8;377(2142):20180144. doi: 10.1098/rsta.2018.0144.
In this position paper, we discuss two relevant topics: (i) generic multiscale computing on emerging exascale high-performing computing environments, and (ii) the scaling of such applications towards the exascale. We will introduce the different phases when developing a multiscale model and simulating it on available computing infrastructure, and argue that we could rely on it both on the conceptual modelling level and also when actually executing the multiscale simulation, and maybe should further develop generic frameworks and software tools to facilitate multiscale computing. Next, we focus on simulating multiscale models on high-end computing resources in the face of emerging exascale performance levels. We will argue that although applications could scale to exascale performance relying on weak scaling and maybe even on strong scaling, there are also clear arguments that such scaling may no longer apply for many applications on these emerging exascale machines and that we need to resort to what we would call multi-scaling. This article is part of the theme issue 'Multiscale modelling, simulation and computing: from the desktop to the exascale'.
在本立场文件中，我们将讨论两个相关主题：（i）新兴的百亿亿次级高性能计算环境中的通用多尺度计算，以及（ii）此类应用程序向百亿亿次级的扩展。我们将介绍开发多尺度模型并在可用计算基础设施上对其进行模拟的不同阶段，并认为我们可以在概念建模层面以及实际执行多尺度模拟时都依赖于它，也许还应该进一步开发通用框架和软件工具以促进多尺度计算。接下来，我们将重点讨论在新兴的百亿亿次级性能水平下如何在高端计算资源上模拟多尺度模型。我们将认为，尽管应用程序可以依靠弱扩展甚至强扩展来扩展到百亿亿次级的性能，但也有明确的观点认为，对于这些新兴的百亿亿次级机器上的许多应用程序，这种扩展可能不再适用，我们需要采用所谓的多尺度扩展。本文是主题为“多尺度建模、模拟和计算：从桌面到百亿亿次级”的特刊的一部分。
