Scheduling and analysis of real-time tasks with parallel critical sections

Yang Wang, Xu Jiang*, Nan Guan, Mingsong Lv, Dong Ji, Wang Yi

*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

2 Citations (Scopus)

Abstract

Locks are the most widely used mechanisms to coordinate simultaneous accesses to exclusive shared resources. While locking protocols and associated schedulability analysis techniques have been extensively studied for sequential real-time tasks, work for parallel tasks largely lags behind. In the limited existing work on this topic, a common assumption is that a critical section must execute sequentially. However, this is not necessarily the case with parallel programming languages. In this paper, we study the analysis of parallel heavy real-time tasks (the density of which is greater than 1) with critical sections in parallel structures. We show that applying existing analysis techniques directly could be unsafe or much pessimistic for the considered model, and develop new techniques to address these problems. Comprehensive experiments are conducted to evaluate the performance of our method.
Original languageEnglish
Title of host publicationDAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference
PublisherIEEE
Pages1255-1260
ISBN (Print)9781450391429
DOIs
Publication statusPublished - 2022
Event59th ACM/IEEE Design Automation Conference (DAC 2022) - San Francisco, United States
Duration: 10 Jul 202214 Jul 2022

Publication series

NameProceedings - Design Automation Conference
ISSN (Print)0738-100X

Conference

Conference59th ACM/IEEE Design Automation Conference (DAC 2022)
Abbreviated titleDAC ’22
PlaceUnited States
CitySan Francisco
Period10/07/2214/07/22

Bibliographical note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Funding

This work was partially supported by the National Natural Science Foundation of China (NSFC 62102072) and Research Grants Council of Hong Kong (GRF 15206221)

RGC Funding Information

  • RGC-funded

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