{"title":"工作流关键路径:面向数据的整体HPC工作流关键路径度量","authors":"Daniel D. Nguyen, Karen L. Karavanic","doi":"10.1016/j.tbench.2021.100001","DOIUrl":null,"url":null,"abstract":"<div><p>Current trends in HPC, such as the push to exascale, convergence with Big Data, and growing complexity of HPC applications, have created gaps that traditional performance tools do not cover. One example is Holistic HPC Workflows — HPC workflows comprising multiple codes, paradigms, or platforms that are not developed using a workflow management system. To diagnose the performance of these applications, we define a new metric called Workflow Critical Path (WCP), a data-oriented metric for Holistic HPC Workflows. WCP constructs graphs that span across the workflow codes and platforms, using data states as vertices and data mutations as edges. Using cloud-based technologies, we implement a prototype called Crux, a distributed analysis tool for calculating and visualizing WCP. Our experiments with a workflow simulator on Amazon Web Services show Crux is scalable and capable of correctly calculating WCP for common Holistic HPC workflow patterns. We explore the use of WCP and discuss how Crux could be used in a production HPC environment.</p></div>","PeriodicalId":100155,"journal":{"name":"BenchCouncil Transactions on Benchmarks, Standards and Evaluations","volume":"1 1","pages":"Article 100001"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772485921000016/pdfft?md5=b03db3d209b242d8a2f663d25834fd8c&pid=1-s2.0-S2772485921000016-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Workflow Critical Path: A data-oriented critical path metric for Holistic HPC Workflows\",\"authors\":\"Daniel D. Nguyen, Karen L. Karavanic\",\"doi\":\"10.1016/j.tbench.2021.100001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Current trends in HPC, such as the push to exascale, convergence with Big Data, and growing complexity of HPC applications, have created gaps that traditional performance tools do not cover. One example is Holistic HPC Workflows — HPC workflows comprising multiple codes, paradigms, or platforms that are not developed using a workflow management system. To diagnose the performance of these applications, we define a new metric called Workflow Critical Path (WCP), a data-oriented metric for Holistic HPC Workflows. WCP constructs graphs that span across the workflow codes and platforms, using data states as vertices and data mutations as edges. Using cloud-based technologies, we implement a prototype called Crux, a distributed analysis tool for calculating and visualizing WCP. Our experiments with a workflow simulator on Amazon Web Services show Crux is scalable and capable of correctly calculating WCP for common Holistic HPC workflow patterns. We explore the use of WCP and discuss how Crux could be used in a production HPC environment.</p></div>\",\"PeriodicalId\":100155,\"journal\":{\"name\":\"BenchCouncil Transactions on Benchmarks, Standards and Evaluations\",\"volume\":\"1 1\",\"pages\":\"Article 100001\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2772485921000016/pdfft?md5=b03db3d209b242d8a2f663d25834fd8c&pid=1-s2.0-S2772485921000016-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BenchCouncil Transactions on Benchmarks, Standards and Evaluations\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2772485921000016\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BenchCouncil Transactions on Benchmarks, Standards and Evaluations","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772485921000016","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
当前HPC的趋势,如向百亿亿级的推动、与大数据的融合以及HPC应用程序的日益复杂,已经产生了传统性能工具无法覆盖的差距。一个例子是整体HPC工作流——HPC工作流包含多个代码、范例或平台,这些代码、范例或平台不是使用工作流管理系统开发的。为了诊断这些应用程序的性能,我们定义了一个名为工作流关键路径(WCP)的新指标,这是一个面向数据的整体HPC工作流指标。WCP构建跨越工作流代码和平台的图形,使用数据状态作为顶点,使用数据突变作为边。使用基于云的技术,我们实现了一个名为Crux的原型,这是一个用于计算和可视化WCP的分布式分析工具。我们在Amazon Web Services上的工作流模拟器上的实验表明Crux是可伸缩的,并且能够正确计算常见的整体HPC工作流模式的WCP。我们将探索WCP的使用,并讨论如何在生产HPC环境中使用Crux。
Workflow Critical Path: A data-oriented critical path metric for Holistic HPC Workflows
Current trends in HPC, such as the push to exascale, convergence with Big Data, and growing complexity of HPC applications, have created gaps that traditional performance tools do not cover. One example is Holistic HPC Workflows — HPC workflows comprising multiple codes, paradigms, or platforms that are not developed using a workflow management system. To diagnose the performance of these applications, we define a new metric called Workflow Critical Path (WCP), a data-oriented metric for Holistic HPC Workflows. WCP constructs graphs that span across the workflow codes and platforms, using data states as vertices and data mutations as edges. Using cloud-based technologies, we implement a prototype called Crux, a distributed analysis tool for calculating and visualizing WCP. Our experiments with a workflow simulator on Amazon Web Services show Crux is scalable and capable of correctly calculating WCP for common Holistic HPC workflow patterns. We explore the use of WCP and discuss how Crux could be used in a production HPC environment.