Program slicing based on runtime dataflow measurements

G. Wacha, J. Lazányi, B. Fehér
{"title":"Program slicing based on runtime dataflow measurements","authors":"G. Wacha, J. Lazányi, B. Fehér","doi":"10.1109/CARPATHIANCC.2015.7145149","DOIUrl":null,"url":null,"abstract":"Multicore architectures enable increasing the performance of the system with parallel processing. One of the challenges of a multicore embedded system is the correct usage of the processor cores. It is possible to achieve balanced processor load on the different cores, but the communication bandwidth between the cores is often a bottleneck. Passing large amounts of data between tasks mapped to different processor cores can result in cache misses in the local cache of a processor core. This paper introduces an analyzation method based on runtime generated data flow graphs to find the data paths of an algorithm. It shows that a spectral cluster analysis can help to discover data independent subsets in the algorithm under test. Finding the data independent parts helps to partition the program to multiple slices where the inter-slice communication is kept as low as possible. With our proposed method the communication bottleneck can be evaded in a multicore, multitask implementation, possibly resulting in better performance.","PeriodicalId":187762,"journal":{"name":"Proceedings of the 2015 16th International Carpathian Control Conference (ICCC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2015 16th International Carpathian Control Conference (ICCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CARPATHIANCC.2015.7145149","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Multicore architectures enable increasing the performance of the system with parallel processing. One of the challenges of a multicore embedded system is the correct usage of the processor cores. It is possible to achieve balanced processor load on the different cores, but the communication bandwidth between the cores is often a bottleneck. Passing large amounts of data between tasks mapped to different processor cores can result in cache misses in the local cache of a processor core. This paper introduces an analyzation method based on runtime generated data flow graphs to find the data paths of an algorithm. It shows that a spectral cluster analysis can help to discover data independent subsets in the algorithm under test. Finding the data independent parts helps to partition the program to multiple slices where the inter-slice communication is kept as low as possible. With our proposed method the communication bottleneck can be evaded in a multicore, multitask implementation, possibly resulting in better performance.
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
基于运行时数据流测量的程序切片
多核架构可以通过并行处理来提高系统的性能。多核嵌入式系统的挑战之一是处理器核心的正确使用。在不同的核心上实现均衡的处理器负载是可能的,但是核心之间的通信带宽通常是一个瓶颈。在映射到不同处理器核心的任务之间传递大量数据可能导致处理器核心的本地缓存丢失。本文介绍了一种基于运行时生成的数据流图的分析方法来查找算法的数据路径。结果表明,谱聚类分析有助于发现算法中与数据无关的子集。找到与数据无关的部分有助于将程序划分为多个片,从而使片间通信尽可能低。该方法可以避免多核、多任务实现中的通信瓶颈,从而获得更好的性能。
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
Off-grid telemetry system for hydrate inhibition on gas wells Decision support by dynamic simulation method Application of the particle filters for identification of the non-Gaussian systems Frequency fitting algorithm of control signals based on Hermite curves Improved closed loop performance and control signal using evolutionary algorithms based PID controller
×
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