{"title":"Scalability of Streaming on Migrating Threads","authors":"Brian A. Page, P. Kogge","doi":"10.1109/HPEC43674.2020.9286193","DOIUrl":null,"url":null,"abstract":"Applications where streams of data are passed through large data structures are becoming of increasing importance. Unfortunately, when implemented on conventional architectures such applications become horribly inefficient, especially when attempts are made to scale up performance via some sort of parallelism. This paper discusses the implementation of the Firehose streaming benchmark on a novel parallel architecture with greatly enhanced multi-threading characteristics that avoids the conventional inefficiencies. Results are promising, with both far better scaling and increased performance over previously reported implementations, on a prototype platform with consid-erably less intrinsic hardware computational resources.","PeriodicalId":168544,"journal":{"name":"2020 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE High Performance Extreme Computing Conference (HPEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HPEC43674.2020.9286193","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
Applications where streams of data are passed through large data structures are becoming of increasing importance. Unfortunately, when implemented on conventional architectures such applications become horribly inefficient, especially when attempts are made to scale up performance via some sort of parallelism. This paper discusses the implementation of the Firehose streaming benchmark on a novel parallel architecture with greatly enhanced multi-threading characteristics that avoids the conventional inefficiencies. Results are promising, with both far better scaling and increased performance over previously reported implementations, on a prototype platform with consid-erably less intrinsic hardware computational resources.