In "BUNDLE: Real-Time Multi-Threaded Scheduling to Reduce Cache Contention", Tessler and Fisher propose a scheduling mechanism and combined worst-case execution time calculation method that treats the instruction cache as a beneficial resource shared between threads. Object analysis produces a worst-case execution time bound and separates code segments into regions. Threads are dynamically placed in bundles associated with regions at run time by the BUNDLE scheduling algorithm where they benefit from shared cache values. In the evaluation of the previous work, tasks were created with a predetermined worst-case execution time path through the control flow graph. Apriori knowledge of the worst-case path is an impractical restriction on any analysis. At the time, the only other solution available was an all-paths search of the graph, which is an equally impractical approach due to its complexity. The primary focus of this work is to build upon BUNDLE, expanding its applicability beyond a proof of concept. We present a complete worst-case execution time calculation method that includes thread level context switch costs, operating on real programs, with representative architecture parameters, and compare our results to those produced by Heptane's state of the art method. To these ends, we propose a modification to the BUNDLE scheduling algorithm called BUNDLEP. Bundles are assigned priorities that enforce an ordered flow of threads through the control flow graph – avoiding the need for multiple all-paths searches through the graph. In many cases, our evaluation shows a run-time and analytical benefit for BUNLDEP compared to serialized thread execution and state of the art WCET analysis.
Tessler和Fisher在“BUNDLE: Real-Time Multi-Threaded Scheduling to Reduce Cache Contention”一文中提出了一种将指令缓存作为线程间共享的有益资源的调度机制和联合最坏情况执行时间计算方法。对象分析产生最坏情况下的执行时间限制，并将代码段划分为区域。在运行时，通过BUNDLE调度算法将线程动态地放置在与区域相关联的BUNDLE中，从而使它们受益于共享缓存值。在评估之前的工作时，通过控制流图创建具有预定最坏情况执行时间路径的任务。最坏情况路径的先验知识对任何分析都是不切实际的限制。当时，唯一可用的其他解决方案是对图进行全路径搜索，由于其复杂性，这同样是一种不切实际的方法。这项工作的主要焦点是建立在BUNDLE的基础上，扩展其适用性，而不仅仅是概念验证。我们提出了一个完整的最坏情况执行时间计算方法，其中包括线程级上下文切换成本、在真实程序上操作、具有代表性的体系结构参数，并将我们的结果与Heptane最先进的方法产生的结果进行比较。为此，我们提出了对BUNDLE调度算法的修改，称为BUNDLEP。bundle被分配了优先级，通过控制流图强制执行有序的线程流——避免了在图中进行多次全路径搜索的需要。在许多情况下，我们的评估显示，与序列化线程执行和最先进的WCET分析相比，BUNLDEP在运行时和分析方面具有优势。
{"title":"BUNDLEP: Prioritizing Conflict Free Regions in Multi-threaded Programs to Improve Cache Reuse","authors":"Corey Tessler, N. Fisher","doi":"10.1109/RTSS.2018.00048","DOIUrl":"https://doi.org/10.1109/RTSS.2018.00048","url":null,"abstract":"In \"BUNDLE: Real-Time Multi-Threaded Scheduling to Reduce Cache Contention\", Tessler and Fisher propose a scheduling mechanism and combined worst-case execution time calculation method that treats the instruction cache as a beneficial resource shared between threads. Object analysis produces a worst-case execution time bound and separates code segments into regions. Threads are dynamically placed in bundles associated with regions at run time by the BUNDLE scheduling algorithm where they benefit from shared cache values. In the evaluation of the previous work, tasks were created with a predetermined worst-case execution time path through the control flow graph. Apriori knowledge of the worst-case path is an impractical restriction on any analysis. At the time, the only other solution available was an all-paths search of the graph, which is an equally impractical approach due to its complexity. The primary focus of this work is to build upon BUNDLE, expanding its applicability beyond a proof of concept. We present a complete worst-case execution time calculation method that includes thread level context switch costs, operating on real programs, with representative architecture parameters, and compare our results to those produced by Heptane's state of the art method. To these ends, we propose a modification to the BUNDLE scheduling algorithm called BUNDLEP. Bundles are assigned priorities that enforce an ordered flow of threads through the control flow graph – avoiding the need for multiple all-paths searches through the graph. In many cases, our evaluation shows a run-time and analytical benefit for BUNLDEP compared to serialized thread execution and state of the art WCET analysis.","PeriodicalId":294784,"journal":{"name":"2018 IEEE Real-Time Systems Symposium (RTSS)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130993994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Niklas Ueter, G. V. D. Brüggen, Jian-Jia Chen, Jing Li, Kunal Agrawal
Multicore systems are increasingly utilized in real-time systems in order to address the high computational demands. To fully exploit the advantages of multicore processing, possible intra-task parallelism modeled as a directed acyclic graph (DAG) must be utilized efficiently. This paper considers the scheduling problem for parallel real-time tasks with constrained and arbitrary deadlines. In contrast to prior work in this area, it generalizes federated scheduling and proposes a novel reservation-based approach. Namely, we propose a reservation-based federated scheduling strategy that reduces the problem of scheduling arbitrary-deadline DAG task sets to the problem of scheduling arbitrary-deadline sequential task sets by allocating reservation servers. We provide the general reservation design for sporadic parallel tasks, such that any scheduling algorithm and analysis for sequential tasks with arbitrary deadlines can be used to execute the allocated reservation servers of parallel tasks. Moreover, the proposed reservation-based federated scheduling algorithms provide constant speedup factors with respect to any optimal scheduler for arbitrary-deadline DAG task sets. We demonstrate via numerical and empirical experiments that our algorithms are competitive with the state of the art.
{"title":"Reservation-Based Federated Scheduling for Parallel Real-Time Tasks","authors":"Niklas Ueter, G. V. D. Brüggen, Jian-Jia Chen, Jing Li, Kunal Agrawal","doi":"10.1109/RTSS.2018.00061","DOIUrl":"https://doi.org/10.1109/RTSS.2018.00061","url":null,"abstract":"Multicore systems are increasingly utilized in real-time systems in order to address the high computational demands. To fully exploit the advantages of multicore processing, possible intra-task parallelism modeled as a directed acyclic graph (DAG) must be utilized efficiently. This paper considers the scheduling problem for parallel real-time tasks with constrained and arbitrary deadlines. In contrast to prior work in this area, it generalizes federated scheduling and proposes a novel reservation-based approach. Namely, we propose a reservation-based federated scheduling strategy that reduces the problem of scheduling arbitrary-deadline DAG task sets to the problem of scheduling arbitrary-deadline sequential task sets by allocating reservation servers. We provide the general reservation design for sporadic parallel tasks, such that any scheduling algorithm and analysis for sequential tasks with arbitrary deadlines can be used to execute the allocated reservation servers of parallel tasks. Moreover, the proposed reservation-based federated scheduling algorithms provide constant speedup factors with respect to any optimal scheduler for arbitrary-deadline DAG task sets. We demonstrate via numerical and empirical experiments that our algorithms are competitive with the state of the art.","PeriodicalId":294784,"journal":{"name":"2018 IEEE Real-Time Systems Symposium (RTSS)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121942084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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