Partitioning-Based Scheduling of OpenMP Task Systems with Tied Tasks

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Yang Wang
  • Xu Jiang
  • Zhishan Guo
  • Xue Liu
  • Wang Yi

Detail(s)

Original languageEnglish
Article number9311807
Pages (from-to)1322-1339
Journal / PublicationIEEE Transactions on Parallel and Distributed Systems
Volume32
Issue number6
Online published31 Dec 2020
Publication statusPublished - Jun 2021
Externally publishedYes

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.

Research Area(s)

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

Citation Format(s)

Partitioning-Based Scheduling of OpenMP Task Systems with Tied Tasks. / Wang, Yang; Jiang, Xu; Guan, Nan; Guo, Zhishan; Liu, Xue; Yi, Wang.

In: IEEE Transactions on Parallel and Distributed Systems, Vol. 32, No. 6, 9311807, 06.2021, p. 1322-1339.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review