R. Al Jahdali , S. Kortas , M. Shaikh , L. Dalcin , M. Parsani
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SSDC is a prototype of the next generation computational fluid dynamics frameworks developed following the road map established by the NASA CFD vision 2030. We simulate complex flow problems using high-order accurate fully-discrete entropy stable algorithms. In terms of time-to-solution, the Amazon Elastic Compute Cloud delivers the best performance, with the Graviton2 processors based on the Arm architecture being the fastest. However, the results also indicate that the Ibex nodes based on the AMD Rome architecture deliver good performance, close to those observed for the Amazon Elastic Compute Cloud. Furthermore, we observed that computations performed on the Ibex on-premises cluster are currently less expensive than those performed in the cloud. 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Thus, in many contexts and realities, high-performance computing grids and cloud resources on demand should be evaluated as viable alternatives to conventional computing clusters. In this work, we present the analysis of the time-to-solution and cost of an entropy stable collocated discontinuous Galerkin (SSDC) compressible computational fluid dynamics framework on Ibex, the on-premises cluster at KAUST, and the Amazon Web Services Elastic Compute Cloud for complex compressible flows. SSDC is a prototype of the next generation computational fluid dynamics frameworks developed following the road map established by the NASA CFD vision 2030. We simulate complex flow problems using high-order accurate fully-discrete entropy stable algorithms. In terms of time-to-solution, the Amazon Elastic Compute Cloud delivers the best performance, with the Graviton2 processors based on the Arm architecture being the fastest. However, the results also indicate that the Ibex nodes based on the AMD Rome architecture deliver good performance, close to those observed for the Amazon Elastic Compute Cloud. Furthermore, we observed that computations performed on the Ibex on-premises cluster are currently less expensive than those performed in the cloud. Our findings could be used to develop guidelines for selecting high-performance computing cloud resources to simulate realistic fluid flow problems.</p></div>\",\"PeriodicalId\":8417,\"journal\":{\"name\":\"Array\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2023-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Array\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2590005622001011\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Array","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590005622001011","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 1
摘要
工业相关的计算流体动力学模拟经常需要大量的计算资源,而这些资源只有政府、富有的公司和富有的机构才能获得。因此,在许多环境和现实中,应将高性能计算网格和按需云资源作为传统计算集群的可行替代方案进行评估。在这项工作中,我们分析了Ibex上熵稳定并配不连续Galerkin (SSDC)可压缩计算流体动力学框架的求解时间和成本,KAUST的本地集群和Amazon Web Services弹性计算云用于复杂的可压缩流。SSDC是下一代计算流体动力学框架的原型,是根据NASA CFD愿景2030建立的路线图开发的。我们使用高阶精确的全离散熵稳定算法来模拟复杂的流动问题。在解决方案的时间方面,亚马逊弹性计算云提供了最好的性能,基于Arm架构的gravon2处理器是最快的。然而,结果也表明,基于AMD Rome架构的Ibex节点提供了良好的性能,接近亚马逊弹性计算云的性能。此外,我们观察到,在Ibex本地集群上执行的计算目前比在云中执行的计算更便宜。我们的研究结果可用于制定选择高性能计算云资源来模拟现实流体流动问题的指南。
Evaluation of next-generation high-order compressible fluid dynamic solver on cloud computing for complex industrial flows
Industrially relevant computational fluid dynamics simulations frequently require vast computational resources that are only available to governments, wealthy corporations, and wealthy institutions. Thus, in many contexts and realities, high-performance computing grids and cloud resources on demand should be evaluated as viable alternatives to conventional computing clusters. In this work, we present the analysis of the time-to-solution and cost of an entropy stable collocated discontinuous Galerkin (SSDC) compressible computational fluid dynamics framework on Ibex, the on-premises cluster at KAUST, and the Amazon Web Services Elastic Compute Cloud for complex compressible flows. SSDC is a prototype of the next generation computational fluid dynamics frameworks developed following the road map established by the NASA CFD vision 2030. We simulate complex flow problems using high-order accurate fully-discrete entropy stable algorithms. In terms of time-to-solution, the Amazon Elastic Compute Cloud delivers the best performance, with the Graviton2 processors based on the Arm architecture being the fastest. However, the results also indicate that the Ibex nodes based on the AMD Rome architecture deliver good performance, close to those observed for the Amazon Elastic Compute Cloud. Furthermore, we observed that computations performed on the Ibex on-premises cluster are currently less expensive than those performed in the cloud. Our findings could be used to develop guidelines for selecting high-performance computing cloud resources to simulate realistic fluid flow problems.