用于CERN高通量计算的小型SIMD矩阵

F. Lemaitre, Benjamin Couturier, L. Lacassagne
{"title":"用于CERN高通量计算的小型SIMD矩阵","authors":"F. Lemaitre, Benjamin Couturier, L. Lacassagne","doi":"10.1145/3178433.3178434","DOIUrl":null,"url":null,"abstract":"System tracking is an old problem and has been heavily optimized throughout the past. However, in High Energy Physics, many small systems are tracked in real-time using Kalman filtering and no implementation satisfying those constraints currently exists. In this paper, we present a code generator used to speed up Cholesky Factorization and Kalman Filter for small matrices. The generator is easy to use and produces portable and heavily optimized code. We focus on current SIMD architectures (SSE, AVX, AVX512, Neon, SVE, Altivec and VSX). Our Cholesky factorization outperforms any existing libraries: from x3 to x10 faster than MKL. The Kalman Filter is also faster than existing implementations, and achieves 4 · 109 iter/s on a 2x24C Intel Xeon.","PeriodicalId":197479,"journal":{"name":"Proceedings of the 2018 4th Workshop on Programming Models for SIMD/Vector Processing","volume":"354 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Small SIMD Matrices for CERN High Throughput Computing\",\"authors\":\"F. Lemaitre, Benjamin Couturier, L. Lacassagne\",\"doi\":\"10.1145/3178433.3178434\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"System tracking is an old problem and has been heavily optimized throughout the past. However, in High Energy Physics, many small systems are tracked in real-time using Kalman filtering and no implementation satisfying those constraints currently exists. In this paper, we present a code generator used to speed up Cholesky Factorization and Kalman Filter for small matrices. The generator is easy to use and produces portable and heavily optimized code. We focus on current SIMD architectures (SSE, AVX, AVX512, Neon, SVE, Altivec and VSX). Our Cholesky factorization outperforms any existing libraries: from x3 to x10 faster than MKL. The Kalman Filter is also faster than existing implementations, and achieves 4 · 109 iter/s on a 2x24C Intel Xeon.\",\"PeriodicalId\":197479,\"journal\":{\"name\":\"Proceedings of the 2018 4th Workshop on Programming Models for SIMD/Vector Processing\",\"volume\":\"354 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-02-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2018 4th Workshop on Programming Models for SIMD/Vector Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3178433.3178434\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2018 4th Workshop on Programming Models for SIMD/Vector Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3178433.3178434","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

摘要

系统跟踪是一个老问题,在过去已经进行了大量优化。然而,在高能物理中,许多小型系统都是使用卡尔曼滤波进行实时跟踪的,目前还没有满足这些约束的实现。在本文中,我们提出了一种用于加速小矩阵的乔列斯基分解和卡尔曼滤波的代码生成器。该生成器易于使用,并生成可移植且经过大量优化的代码。我们专注于当前的SIMD架构(SSE, AVX, AVX512, Neon, SVE, Altivec和VSX)。我们的Cholesky分解优于任何现有的库:比MKL快3到10倍。卡尔曼滤波器也比现有的实现更快,在2x24C英特尔至强处理器上达到4109 iter/s。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Small SIMD Matrices for CERN High Throughput Computing
System tracking is an old problem and has been heavily optimized throughout the past. However, in High Energy Physics, many small systems are tracked in real-time using Kalman filtering and no implementation satisfying those constraints currently exists. In this paper, we present a code generator used to speed up Cholesky Factorization and Kalman Filter for small matrices. The generator is easy to use and produces portable and heavily optimized code. We focus on current SIMD architectures (SSE, AVX, AVX512, Neon, SVE, Altivec and VSX). Our Cholesky factorization outperforms any existing libraries: from x3 to x10 faster than MKL. The Kalman Filter is also faster than existing implementations, and achieves 4 · 109 iter/s on a 2x24C Intel Xeon.
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
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信