异构体系结构的自调谐波前抽象

2012 Third Workshop on Applications for Multi-Core Architecture Pub Date : 2012-10-24 DOI:10.1109/WAMCA.2012.14

S. Mohanty, M. Cole

{"title":"异构体系结构的自调谐波前抽象","authors":"S. Mohanty, M. Cole","doi":"10.1109/WAMCA.2012.14","DOIUrl":null,"url":null,"abstract":"We present our auto tuned heterogeneous parallel programming abstraction for the wave front pattern. An exhaustive search of the tuning space indicates that correct setting of tuning factors can average 37x speedup over a sequential baseline. Our best automated machine learning based heuristic obtains 92% of this ideal speedup, averaged across our full range of wave front examples.","PeriodicalId":288438,"journal":{"name":"2012 Third Workshop on Applications for Multi-Core Architecture","volume":"96 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Autotuning Wavefront Abstractions for Heterogeneous Architectures\",\"authors\":\"S. Mohanty, M. Cole\",\"doi\":\"10.1109/WAMCA.2012.14\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present our auto tuned heterogeneous parallel programming abstraction for the wave front pattern. An exhaustive search of the tuning space indicates that correct setting of tuning factors can average 37x speedup over a sequential baseline. Our best automated machine learning based heuristic obtains 92% of this ideal speedup, averaged across our full range of wave front examples.\",\"PeriodicalId\":288438,\"journal\":{\"name\":\"2012 Third Workshop on Applications for Multi-Core Architecture\",\"volume\":\"96 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-10-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 Third Workshop on Applications for Multi-Core Architecture\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WAMCA.2012.14\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Third Workshop on Applications for Multi-Core Architecture","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WAMCA.2012.14","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 2

摘要

提出了波前图的自调谐异构并行编程抽象。对调优空间的详尽搜索表明，正确设置调优因素可以在连续基线上平均提高37倍的速度。我们最好的基于自动化机器学习的启发式算法获得了92%的理想加速，在我们全范围的波前示例中平均。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Autotuning Wavefront Abstractions for Heterogeneous Architectures

We present our auto tuned heterogeneous parallel programming abstraction for the wave front pattern. An exhaustive search of the tuning space indicates that correct setting of tuning factors can average 37x speedup over a sequential baseline. Our best automated machine learning based heuristic obtains 92% of this ideal speedup, averaged across our full range of wave front examples.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2012 Third Workshop on Applications for Multi-Core Architecture

自引率

0.00%

发文量