Kaj Hänninen, Jukka Mäki-Turja, M. Bohlin, Jan Carlson, Mikael Nolin
{"title":"Determining Maximum Stack Usage in Preemptive Shared Stack Systems","authors":"Kaj Hänninen, Jukka Mäki-Turja, M. Bohlin, Jan Carlson, Mikael Nolin","doi":"10.1109/RTSS.2006.18","DOIUrl":null,"url":null,"abstract":"This paper presents a novel method to determine the maximum stack memory used in preemptive, shared stack, real-time systems. We provide a general and exact problem formulation applicable for any preemptive system model based on dynamic (run-time) properties. We also show how to safely approximate the exact stack usage by using static (compile time) information about the system model and the underlying run-time system on a relevant and commercially available system model: a hybrid, statically and dynamically, scheduled system. Comprehensive evaluations show that our technique significantly reduces the amount of stack memory needed compared to existing analysis techniques. For typical task sets a decrease in the order of 70% is typical","PeriodicalId":353932,"journal":{"name":"2006 27th IEEE International Real-Time Systems Symposium (RTSS'06)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"22","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 27th IEEE International Real-Time Systems Symposium (RTSS'06)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RTSS.2006.18","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 22
Abstract
This paper presents a novel method to determine the maximum stack memory used in preemptive, shared stack, real-time systems. We provide a general and exact problem formulation applicable for any preemptive system model based on dynamic (run-time) properties. We also show how to safely approximate the exact stack usage by using static (compile time) information about the system model and the underlying run-time system on a relevant and commercially available system model: a hybrid, statically and dynamically, scheduled system. Comprehensive evaluations show that our technique significantly reduces the amount of stack memory needed compared to existing analysis techniques. For typical task sets a decrease in the order of 70% is typical