避免矢量化

G. Keller, M. Chakravarty, Roman Leshchinskiy, B. Lippmeier, S. Jones
{"title":"避免矢量化","authors":"G. Keller, M. Chakravarty, Roman Leshchinskiy, B. Lippmeier, S. Jones","doi":"10.1145/2364506.2364512","DOIUrl":null,"url":null,"abstract":"Flattening nested parallelism is a vectorising code transform that converts irregular nested parallelism into flat data parallelism. Although the result has good asymptotic performance, flattening thoroughly restructures the code. Many intermediate data structures and traversals are introduced, which may or may not be eliminated by subsequent optimisation. We present a novel program analysis to identify parts of the program where flattening would only introduce overhead, without appropriate gain. We present empirical evidence that avoiding vectorisation in these cases leads to more efficient programs than if we had applied vectorisation and then relied on array fusion to eliminate intermediates from the resulting code.","PeriodicalId":188691,"journal":{"name":"ACM SIGPLAN Symposium/Workshop on Haskell","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":"{\"title\":\"Vectorisation avoidance\",\"authors\":\"G. Keller, M. Chakravarty, Roman Leshchinskiy, B. Lippmeier, S. Jones\",\"doi\":\"10.1145/2364506.2364512\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Flattening nested parallelism is a vectorising code transform that converts irregular nested parallelism into flat data parallelism. Although the result has good asymptotic performance, flattening thoroughly restructures the code. Many intermediate data structures and traversals are introduced, which may or may not be eliminated by subsequent optimisation. We present a novel program analysis to identify parts of the program where flattening would only introduce overhead, without appropriate gain. We present empirical evidence that avoiding vectorisation in these cases leads to more efficient programs than if we had applied vectorisation and then relied on array fusion to eliminate intermediates from the resulting code.\",\"PeriodicalId\":188691,\"journal\":{\"name\":\"ACM SIGPLAN Symposium/Workshop on Haskell\",\"volume\":\"49 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-01-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"17\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM SIGPLAN Symposium/Workshop on Haskell\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2364506.2364512\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM SIGPLAN Symposium/Workshop on Haskell","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2364506.2364512","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 17

摘要

扁平化嵌套并行是一种向量化代码转换,它将不规则的嵌套并行转换为扁平的数据并行。虽然结果具有良好的渐近性能,但扁平化彻底地重构了代码。引入了许多中间数据结构和遍历,这些可能会被后续的优化消除,也可能不会。我们提出了一种新颖的程序分析,以确定程序中扁平化只会引入开销而没有适当增益的部分。我们提出的经验证据表明,在这些情况下,避免向量化比我们应用向量化然后依靠数组融合来消除结果代码中的中间部分更有效。
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
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Vectorisation avoidance
Flattening nested parallelism is a vectorising code transform that converts irregular nested parallelism into flat data parallelism. Although the result has good asymptotic performance, flattening thoroughly restructures the code. Many intermediate data structures and traversals are introduced, which may or may not be eliminated by subsequent optimisation. We present a novel program analysis to identify parts of the program where flattening would only introduce overhead, without appropriate gain. We present empirical evidence that avoiding vectorisation in these cases leads to more efficient programs than if we had applied vectorisation and then relied on array fusion to eliminate intermediates from the resulting code.
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
自引率
0.00%
发文量
0
期刊最新文献
Mio: a high-performance multicore io manager for GHC Understanding idiomatic traversals backwards and forwards An EDSL approach to high performance Haskell programming Causality of optimized Haskell: what is burning our cycles? Splittable pseudorandom number generators using cryptographic hashing
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1