{"title":"请参阅应用程序运行和吞吐量跳转:HPC中冗余计算的情况","authors":"R. Riesen, Kurt B. Ferreira, Jon Stearley","doi":"10.1109/DSNW.2010.5542625","DOIUrl":null,"url":null,"abstract":"For future parallel-computing systems with as few as twenty-thousand nodes we propose redundant computing to reduce the number of application interrupts. The frequency of faults in exascale systems will be so high that traditional checkpoint/restart methods will break down. Applications will experience interruptions so often that they will spend more time restarting and recovering lost work, than computing the solution. We show that redundant computation at large scale can be cost effective and allows applications to complete their work in significantly less wall-clock time. On truly large systems, redundant computing can increase system throughput by an order of magnitude.","PeriodicalId":124206,"journal":{"name":"2010 International Conference on Dependable Systems and Networks Workshops (DSN-W)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"26","resultStr":"{\"title\":\"See applications run and throughput jump: The case for redundant computing in HPC\",\"authors\":\"R. Riesen, Kurt B. Ferreira, Jon Stearley\",\"doi\":\"10.1109/DSNW.2010.5542625\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For future parallel-computing systems with as few as twenty-thousand nodes we propose redundant computing to reduce the number of application interrupts. The frequency of faults in exascale systems will be so high that traditional checkpoint/restart methods will break down. Applications will experience interruptions so often that they will spend more time restarting and recovering lost work, than computing the solution. We show that redundant computation at large scale can be cost effective and allows applications to complete their work in significantly less wall-clock time. On truly large systems, redundant computing can increase system throughput by an order of magnitude.\",\"PeriodicalId\":124206,\"journal\":{\"name\":\"2010 International Conference on Dependable Systems and Networks Workshops (DSN-W)\",\"volume\":\"59 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"26\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 International Conference on Dependable Systems and Networks Workshops (DSN-W)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DSNW.2010.5542625\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Dependable Systems and Networks Workshops (DSN-W)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DSNW.2010.5542625","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
See applications run and throughput jump: The case for redundant computing in HPC
For future parallel-computing systems with as few as twenty-thousand nodes we propose redundant computing to reduce the number of application interrupts. The frequency of faults in exascale systems will be so high that traditional checkpoint/restart methods will break down. Applications will experience interruptions so often that they will spend more time restarting and recovering lost work, than computing the solution. We show that redundant computation at large scale can be cost effective and allows applications to complete their work in significantly less wall-clock time. On truly large systems, redundant computing can increase system throughput by an order of magnitude.