{"title":"On-line application performance monitoring of blood flow simulation in computational grid architectures","authors":"Alfredo Tirado-Ramos, D. Groen, P. Sloot","doi":"10.1109/CBMS.2005.79","DOIUrl":null,"url":null,"abstract":"We report on our findings after running a number of on-line performance monitoring experiments with a biomedical parallel application to investigate levels of performance at hardware resources distributed across a computational Grid network. We use on-line application monitoring for improved computational resource selection and application optimization. We used a number of user-defined performance metrics within the European CrossGrid Project's G-PM tool together with a blood flow simulation application based on the lattice Boltzmann method for fluid dynamics. We found that the performance results observed during our on-line experiments give us a more accurate view of computational resource status than the regular resource information provided by standard information services to resource brokers, and that on-line monitoring has good potential for optimizing our biomedical application for more efficient runs.","PeriodicalId":119367,"journal":{"name":"18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CBMS.2005.79","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
We report on our findings after running a number of on-line performance monitoring experiments with a biomedical parallel application to investigate levels of performance at hardware resources distributed across a computational Grid network. We use on-line application monitoring for improved computational resource selection and application optimization. We used a number of user-defined performance metrics within the European CrossGrid Project's G-PM tool together with a blood flow simulation application based on the lattice Boltzmann method for fluid dynamics. We found that the performance results observed during our on-line experiments give us a more accurate view of computational resource status than the regular resource information provided by standard information services to resource brokers, and that on-line monitoring has good potential for optimizing our biomedical application for more efficient runs.