Anatomy of High-Performance Many-Threaded Matrix Multiplication

T. Smith, R. Geijn, M. Smelyanskiy, J. Hammond, F. V. Zee
{"title":"Anatomy of High-Performance Many-Threaded Matrix Multiplication","authors":"T. Smith, R. Geijn, M. Smelyanskiy, J. Hammond, F. V. Zee","doi":"10.1109/IPDPS.2014.110","DOIUrl":null,"url":null,"abstract":"BLIS is a new framework for rapid instantiation of the BLAS. We describe how BLIS extends the \"GotoBLAS approach\" to implementing matrix multiplication (GEMM). While GEMM was previously implemented as three loops around an inner kernel, BLIS exposes two additional loops within that inner kernel, casting the computation in terms of the BLIS micro-kernel so that porting GEMM becomes a matter of customizing this micro-kernel for a given architecture. We discuss how this facilitates a finer level of parallelism that greatly simplifies the multithreading of GEMM as well as additional opportunities for parallelizing multiple loops. Specifically, we show that with the advent of many-core architectures such as the IBM PowerPC A2 processor (used by Blue Gene/Q) and the Intel Xeon Phi processor, parallelizing both within and around the inner kernel, as the BLIS approach supports, is not only convenient, but also necessary for scalability. The resulting implementations deliver what we believe to be the best open source performance for these architectures, achieving both impressive performance and excellent scalability.","PeriodicalId":309291,"journal":{"name":"2014 IEEE 28th International Parallel and Distributed Processing Symposium","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"122","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE 28th International Parallel and Distributed Processing Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPDPS.2014.110","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 122

Abstract

BLIS is a new framework for rapid instantiation of the BLAS. We describe how BLIS extends the "GotoBLAS approach" to implementing matrix multiplication (GEMM). While GEMM was previously implemented as three loops around an inner kernel, BLIS exposes two additional loops within that inner kernel, casting the computation in terms of the BLIS micro-kernel so that porting GEMM becomes a matter of customizing this micro-kernel for a given architecture. We discuss how this facilitates a finer level of parallelism that greatly simplifies the multithreading of GEMM as well as additional opportunities for parallelizing multiple loops. Specifically, we show that with the advent of many-core architectures such as the IBM PowerPC A2 processor (used by Blue Gene/Q) and the Intel Xeon Phi processor, parallelizing both within and around the inner kernel, as the BLIS approach supports, is not only convenient, but also necessary for scalability. The resulting implementations deliver what we believe to be the best open source performance for these architectures, achieving both impressive performance and excellent scalability.
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
高性能多线程矩阵乘法解析
BLIS是一种用于快速实例化BLAS的新框架。我们描述了BLIS如何扩展“GotoBLAS方法”来实现矩阵乘法(GEMM)。虽然GEMM以前被实现为围绕内核的三个循环,但BLIS在内核中公开了两个额外的循环,将计算转换为BLIS微内核,因此移植GEMM就变成了针对给定体系结构定制这个微内核的问题。我们将讨论这如何促进更精细的并行性,从而大大简化GEMM的多线程,并为并行化多个循环提供额外的机会。具体来说,我们表明,随着许多核心架构的出现,如IBM PowerPC A2处理器(由Blue Gene/Q使用)和Intel Xeon Phi处理器,在内核内部和内核周围并行化,正如BLIS方法所支持的那样,不仅方便,而且对于可扩展性也是必要的。最终的实现交付了我们认为是这些体系结构中最好的开源性能,实现了令人印象深刻的性能和出色的可伸缩性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Improving the Performance of CA-GMRES on Multicores with Multiple GPUs Multi-resource Real-Time Reader/Writer Locks for Multiprocessors Energy-Efficient Time-Division Multiplexed Hybrid-Switched NoC for Heterogeneous Multicore Systems Scaling Irregular Applications through Data Aggregation and Software Multithreading Heterogeneity-Aware Workload Placement and Migration in Distributed Sustainable Datacenters
×
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