MPI-IO在平行粒子输运蒙特卡罗模拟中的应用

Mo Ze-yao, Huang Zhengfeng
{"title":"MPI-IO在平行粒子输运蒙特卡罗模拟中的应用","authors":"Mo Ze-yao, Huang Zhengfeng","doi":"10.1080/10637190412331295166","DOIUrl":null,"url":null,"abstract":"Parallel computers are increasingly being used to run large-scale applications that also have huge input/output (I/O) requirements. However, many applications usually obtain poor I/O performance on parallel machines. In this paper, we will address the parallel I/O of a parallel particle transport Monte-Carlo simulation code (PTMC) on a parallel computer. This paper shows that, without careful treatments, the I/O overheads will ultimately dominate the elapsed simulation time. Fortunately, we have successfully designed the parallel MPI I/O methods for it. In particular, for a benchmark application MAP6 with 105 steps of 100,000 samples, we have elevated the speedup from 10 with 64 processors to 56 with 90 processors. Moreover, our method is scalable for a larger number of CPUs and a larger number of samples.","PeriodicalId":406098,"journal":{"name":"Parallel Algorithms and Applications","volume":"67 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Application of MPI-IO in Parallel Particle Transport Monte-Carlo Simulation\",\"authors\":\"Mo Ze-yao, Huang Zhengfeng\",\"doi\":\"10.1080/10637190412331295166\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Parallel computers are increasingly being used to run large-scale applications that also have huge input/output (I/O) requirements. However, many applications usually obtain poor I/O performance on parallel machines. In this paper, we will address the parallel I/O of a parallel particle transport Monte-Carlo simulation code (PTMC) on a parallel computer. This paper shows that, without careful treatments, the I/O overheads will ultimately dominate the elapsed simulation time. Fortunately, we have successfully designed the parallel MPI I/O methods for it. In particular, for a benchmark application MAP6 with 105 steps of 100,000 samples, we have elevated the speedup from 10 with 64 processors to 56 with 90 processors. Moreover, our method is scalable for a larger number of CPUs and a larger number of samples.\",\"PeriodicalId\":406098,\"journal\":{\"name\":\"Parallel Algorithms and Applications\",\"volume\":\"67 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Parallel Algorithms and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/10637190412331295166\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Parallel Algorithms and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/10637190412331295166","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

并行计算机越来越多地用于运行具有大量输入/输出(I/O)需求的大规模应用程序。然而,许多应用程序通常在并行机器上获得较差的I/O性能。在本文中,我们将讨论并行粒子输运蒙特卡罗模拟代码(PTMC)在并行计算机上的并行I/O。本文表明,如果不仔细处理,I/O开销将最终支配所消耗的模拟时间。幸运的是,我们已经成功地为它设计了并行的MPI I/O方法。特别是,对于具有105个步骤的100,000个样本的基准应用程序MAP6,我们将加速从64个处理器的10个提高到90个处理器的56个。此外,我们的方法可扩展到更大数量的cpu和更多数量的样本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of MPI-IO in Parallel Particle Transport Monte-Carlo Simulation
Parallel computers are increasingly being used to run large-scale applications that also have huge input/output (I/O) requirements. However, many applications usually obtain poor I/O performance on parallel machines. In this paper, we will address the parallel I/O of a parallel particle transport Monte-Carlo simulation code (PTMC) on a parallel computer. This paper shows that, without careful treatments, the I/O overheads will ultimately dominate the elapsed simulation time. Fortunately, we have successfully designed the parallel MPI I/O methods for it. In particular, for a benchmark application MAP6 with 105 steps of 100,000 samples, we have elevated the speedup from 10 with 64 processors to 56 with 90 processors. Moreover, our method is scalable for a larger number of CPUs and a larger number of samples.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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:481959085
Book学术官方微信