Allen R. Sanderson, John A. Schmidt, A. Humphrey, M. Papka, R. Sisneros
{"title":"In Situ Visualization of Performance Metrics in Multiple Domains","authors":"Allen R. Sanderson, John A. Schmidt, A. Humphrey, M. Papka, R. Sisneros","doi":"10.1109/ProTools49597.2019.00014","DOIUrl":null,"url":null,"abstract":"As application scientists develop and deploy simula- tion codes on to leadership-class computing resources, there is a need to instrument these codes to better understand performance to efficiently utilize these resources. This instrumentation may come from independent third-party tools that generate and store performance metrics or from custom instrumentation tools built directly into the application. The metrics collected are then available for visual analysis, typically in the domain in which there were collected. In this paper, we introduce an approach to visualize and analyze the performance metrics in situ in the context of the machine, application, and communication domains (MAC model) using a single visualization tool. This visualization model provides a holistic view of the application performance in the context of the resources where it is executing.","PeriodicalId":418029,"journal":{"name":"2019 IEEE/ACM International Workshop on Programming and Performance Visualization Tools (ProTools)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE/ACM International Workshop on Programming and Performance Visualization Tools (ProTools)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ProTools49597.2019.00014","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
As application scientists develop and deploy simula- tion codes on to leadership-class computing resources, there is a need to instrument these codes to better understand performance to efficiently utilize these resources. This instrumentation may come from independent third-party tools that generate and store performance metrics or from custom instrumentation tools built directly into the application. The metrics collected are then available for visual analysis, typically in the domain in which there were collected. In this paper, we introduce an approach to visualize and analyze the performance metrics in situ in the context of the machine, application, and communication domains (MAC model) using a single visualization tool. This visualization model provides a holistic view of the application performance in the context of the resources where it is executing.