Patterns for High Performance Multiscale Computing
Patterns for High Performance Multiscale Computing
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DOI：
10.1016/j.future.2018.08.045
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发表时间：
2019-02-01
影响因子：
7.5
通讯作者：
Hoekstra, A. G.
中科院分区：
文献类型：
--
作者：
Alowayyed, S.;Piontek, T.;Hoekstra, A. G.
我们描述了我们的多尺度计算模式软件的高性能多尺度计算。本文在简要回顾多尺度计算模式的基础上，介绍了多尺度计算模式软件，该软件由描述、优化和执行三部分组成。首先，描述组件将表示多尺度模拟的任务图转换为特定类型的多尺度计算模式。其次，优化组件选择并应用算法来找到子模型和可用HPC资源之间的最合适的映射。第三，执行组件，中间件层根据来自优化部分的建议以及特定于基础设施的度量（如维护时间和资源可用性），将子模型映射到物理资源的数量和类型。多尺度计算模式软件的主要目的是利用多尺度计算模式来简化和自动化在高性能计算机上执行复杂的多尺度模拟，并提供特定于应用程序和特定于模式的性能优化。我们测试了三个多尺度模型的性能和资源使用情况，这是表示在两个多尺度计算模式。在此过程中，我们演示了该软件如何自动化资源选择和负载平衡，并从最终用户和HPC系统级别的角度提供性能优势。(C)2018作者由爱思唯尔公司出版
We describe our Multiscale Computing Patterns software for High Performance Multiscale Computing. Following a short review of Multiscale Computing Patterns, this paper introduces the Multiscale Computing Patterns Software, which consists of description, optimisation and execution components. First, the description component translates the task graph, representing a multiscale simulation, to a particular type of multiscale computing pattern. Second, the optimisation component selects and applies algorithms to find the most suitable mapping between submodels and available HPC resources. Third, the execution component which a middleware layer maps submodels to the number and type of physical resources based on the suggestions emanating from the optimisation part together with infrastructure-specific metrics such as queueing time and resource availability. The main purpose of the Multiscale Computing Patterns software is to leverage the Multiscale Computing Patterns to simplify and automate the execution of complex multiscale simulations on high performance computers, and to provide both application specific and pattern-specific performance optimisation. We test the performance and the resource usage for three multiscale models, which are expressed in terms of two Multiscale Computing Patterns. In doing so, we demonstrate how the software automates resource selection and load balancing, and delivers performance benefits from both the end-user and the HPC system level perspectives. (C) 2018 The Authors. Published by Elsevier B.V.