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Finding, analysing and solving MPI communication bottlenecks in Earth System models

机译:在地球系统模型中查找,分析和解决MPI通信瓶颈

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It is a matter of consensus that the ability to efficiently use current and future high performance computing systems is crucial for science, however, the reality is that the performance currently achieved by most of the parallel scientific applications is far from desired. Despite inter-process communication has already been a matter of study in many different works, it is a fact that their recommendations are not taken into account in most of computational model development processes, at least in the case of Earth Science. This work presents a methodology that aims to help scientists working with computational models using inter-process communication, to deal with the difficulties they face when trying to understand their applications behaviour. Following a series of steps that are presented here, both users and developers will learn how to identify performance issues by characterizing applications scalability, identifying which parts present a bad performance and understand the role that inter-process communication plays. In this work, the Nucleus for European Modelling of the Ocean (NEMO), the state-of-the-art European global ocean circulation model, will be used as an example of success. It is a community code widely used in Europe, to the extent that more than a hundred million core hours are used every year in experiments involving NEMO. In the analysis exercise, it is shown how to answer the questions of where, why and what is degrading model's scalability, and how this information can help developers in finding solutions that will mitigate their eventual issues. This document also demonstrates how performance analysis carried out with small size experiments, using limited resources, can lead to optimizations that will impact bigger experiments running on thousands of cores, making it easier to deal with the exascale challenge. (C) 2018 The Authors. Published by Elsevier B.V.
机译:达成共识的是,有效利用当前和未来的高性能计算系统的能力对于科学至关重要,但是,现实是,大多数并行科学应用程序目前所达到的性能都远远不够。尽管进程间通信已经在许多不同的著作中进行了研究,但事实是,至少在地球科学的情况下,大多数计算模型开发过程并未考虑它们的建议。这项工作提出了一种方法,旨在帮助科学家使用进程间通信来处理计算模型,以解决他们在试图理解其应用程序行为时面临的困难。按照此处介绍的一系列步骤,用户和开发人员都将学习如何通过表征应用程序的可伸缩性,识别哪些部分的性能不佳以及了解进程间通信所扮演的角色来识别性能问题。在这项工作中,将以欧洲最新的全球海洋环流模型-欧洲海洋建模核心(NEMO)为例。它是在欧洲广泛使用的社区代码,在涉及NEMO的实验中,每年使用的核心小时数超过一亿。在分析练习中,它显示了如何回答降低模型可伸缩性的位置,原因和原因,以及这些信息如何帮助开发人员找到可以减轻最终问题的解决方案。本文档还演示了使用有限的资源,通过小规模实验进行的性能分析如何能够导致优化,从而影响在数千个内核上运行的大型实验,从而更轻松地应对万亿级挑战。 (C)2018作者。由Elsevier B.V.发布

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