D. Ahn, Ned Bass, Albert Chu, J. Garlick, Mark Grondona, Stephen Herbein, Helgi I. Ingólfsson, Joseph Koning, Tapasya Patki, T. Scogland, B. Springmeyer, M. Taufer
{"title":"Flux: Overcoming Scheduling Challenges for Exascale Workflows","authors":"D. Ahn, Ned Bass, Albert Chu, J. Garlick, Mark Grondona, Stephen Herbein, Helgi I. Ingólfsson, Joseph Koning, Tapasya Patki, T. Scogland, B. Springmeyer, M. Taufer","doi":"10.1109/WORKS.2018.00007","DOIUrl":null,"url":null,"abstract":"Many emerging scientific workflows that target high-end HPC systems require complex interplay with the resource and job management software~(RJMS). However, portable, efficient and easy-to-use scheduling and execution of these workflows is still an unsolved problem. We present Flux, a novel, hierarchical RJMS infrastructure that addresses the key scheduling challenges of modern workflows in a scalable, easy-to-use, and portable manner. At the heart of Flux lies its ability to be nested seamlessly within batch allocations created by other schedulers as well as itself. Once a hierarchy of Flux instance is created within each allocation, its consistent and rich set of well-defined APIs portably and efficiently support those workflows that can often feature non-traditional execution patterns such as requirements for complex co-scheduling, massive ensembles of small jobs and coordination among jobs in an ensemble.","PeriodicalId":154317,"journal":{"name":"2018 IEEE/ACM Workflows in Support of Large-Scale Science (WORKS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"57","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE/ACM Workflows in Support of Large-Scale Science (WORKS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WORKS.2018.00007","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 57
Abstract
Many emerging scientific workflows that target high-end HPC systems require complex interplay with the resource and job management software~(RJMS). However, portable, efficient and easy-to-use scheduling and execution of these workflows is still an unsolved problem. We present Flux, a novel, hierarchical RJMS infrastructure that addresses the key scheduling challenges of modern workflows in a scalable, easy-to-use, and portable manner. At the heart of Flux lies its ability to be nested seamlessly within batch allocations created by other schedulers as well as itself. Once a hierarchy of Flux instance is created within each allocation, its consistent and rich set of well-defined APIs portably and efficiently support those workflows that can often feature non-traditional execution patterns such as requirements for complex co-scheduling, massive ensembles of small jobs and coordination among jobs in an ensemble.