Partitioning-Based Scheduling of OpenMP Task Systems with Tied Tasks

Yang Wang, Xu Jiang, Nan Guan, Zhishan Guo, Xue Liu, Wang Yi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

OpenMP is a popular programming framework in both general and high-performance computing and has recently drawn much interest in embedded and real-time computing. Although the execution semantics of OpenMP are similar to the DAG task model, the constraints posed by the OpenMP specification make them significantly more challenging to analyze. A tied task is an important feature in OpenMP that must execute on the same thread throughout its entire life cycle. A previous work [1] succeeded in analyzing the real-time scheduling of tied tasks by modifying the Task Scheduling Constraints (TSCs) in OpenMP specification. In this article, we also study the real-time scheduling of OpenMP task systems with tied tasks but without changing the original TSCs. In particular, we propose a partitioning-based algorithm, P-EDF-omp, by which the tied constraint can be automatically guaranteed as long as an OpenMP task system can be successfully partitioned to a multiprocessor platform. Furthermore, we conduct comprehensive experiments with both synthetic workloads and established OpenMP benchmarks to show that our approach consistently outperforms the work in [1] - even without modifying the TSCs.

Original languageEnglish
Article number9311807
Pages (from-to)1322-1339
Number of pages18
JournalIEEE Transactions on Parallel and Distributed Systems
Volume32
Issue number6
DOIs
Publication statusPublished - 1 Jun 2021

Keywords

  • Multicore
  • OpenMP
  • parallel tasks
  • partitioning
  • real-time scheduling
  • tied tasks

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Computational Theory and Mathematics

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