Parallel Optimisation Strategies for Fusion Codes

A. Jackson, Fiona Reid, S. Booth, J. Hein, J. Westerholm, M. Aspnäs, M. Catala, A. Soba
{"title":"Parallel Optimisation Strategies for Fusion Codes","authors":"A. Jackson, Fiona Reid, S. Booth, J. Hein, J. Westerholm, M. Aspnäs, M. Catala, A. Soba","doi":"10.1109/PDP.2011.15","DOIUrl":null,"url":null,"abstract":"We have previously documented the on-going work in the EUFORIA project to parallelise and optimise European fusion simulation codes. This involves working with a wide range of codes to try and address any performance and scaling issues that these codes have. However, as no two simulation codes are exactly the same, it is very hard to apply exactly the same approach to optimising a disparate range of codes. Indeed, the codes investigated range in terms of performance and ability from well-optimised, highly parallelised codes, to serial or poorly performing codes. After analysing, optimising, and parallelising a range of codes it is, actually, possible to discern a number of distinct optimisation techniques or approaches/strategies that can be used to improve the performance or scaling of a parallel simulation code. This paper outlines the distinct approaches that we have identified, highlighting their benefits and drawbacks, giving an overview of the type of work that is often attempted for fusion simulation code optimisation. performing codes. After analysing, optimising, parallelising, and scaling a range of codes it is, actually, possible to discern a number of distinctoptimisation techniques or approaches/strategies that can be used to improve the performance or scaling of a parallel simulation code. This paper outlines the distinct approaches that we have identified, highlighting their benefits and drawbacks, giving an overview of the type of work that is often attempted for fusion simulation code optimisation.","PeriodicalId":341803,"journal":{"name":"2011 19th International Euromicro Conference on Parallel, Distributed and Network-Based Processing","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 19th International Euromicro Conference on Parallel, Distributed and Network-Based Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PDP.2011.15","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

We have previously documented the on-going work in the EUFORIA project to parallelise and optimise European fusion simulation codes. This involves working with a wide range of codes to try and address any performance and scaling issues that these codes have. However, as no two simulation codes are exactly the same, it is very hard to apply exactly the same approach to optimising a disparate range of codes. Indeed, the codes investigated range in terms of performance and ability from well-optimised, highly parallelised codes, to serial or poorly performing codes. After analysing, optimising, and parallelising a range of codes it is, actually, possible to discern a number of distinct optimisation techniques or approaches/strategies that can be used to improve the performance or scaling of a parallel simulation code. This paper outlines the distinct approaches that we have identified, highlighting their benefits and drawbacks, giving an overview of the type of work that is often attempted for fusion simulation code optimisation. performing codes. After analysing, optimising, parallelising, and scaling a range of codes it is, actually, possible to discern a number of distinctoptimisation techniques or approaches/strategies that can be used to improve the performance or scaling of a parallel simulation code. This paper outlines the distinct approaches that we have identified, highlighting their benefits and drawbacks, giving an overview of the type of work that is often attempted for fusion simulation code optimisation.
融合码的并行优化策略
我们之前已经记录了EUFORIA项目中正在进行的工作,以并行化和优化欧洲融合模拟代码。这涉及到使用各种各样的代码来尝试解决这些代码所具有的任何性能和可伸缩性问题。然而,因为没有两个模拟代码是完全相同的,所以很难应用完全相同的方法来优化不同范围的代码。实际上,所调查的代码在性能和能力方面的范围从良好优化,高度并行化的代码,到串行或性能较差的代码。在分析、优化和并行一系列代码之后,实际上可以辨别出许多不同的优化技术或方法/策略,这些技术或方法/策略可用于提高并行模拟代码的性能或扩展。本文概述了我们已经确定的不同方法,突出了它们的优点和缺点,并概述了经常尝试进行融合模拟代码优化的工作类型。执行代码。在分析、优化、并行化和扩展一系列代码之后,实际上可以辨别出许多不同的优化技术或方法/策略,这些技术或方法/策略可用于提高并行模拟代码的性能或扩展。本文概述了我们已经确定的不同方法,突出了它们的优点和缺点,并概述了经常尝试进行融合模拟代码优化的工作类型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信