Optimization towards efficiency and stateful of dispel4py

Liang Liang*, Heting Zhang, Guang Yang, Thomas Heinis, Rosa Filgueira*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Scientific workflows bridge scientific challenges with computational resources. While dispel4py, a stream-based workflow system, offers mappings to parallel enactment engines like MPI or Multiprocessing, its optimization primarily focuses on dynamic process-to-task allocation for improved performance. An efficiency gap persists, particularly with the growing emphasis on conserving computing resources. Moreover, the existing dynamic optimization lacks support for stateful applications and grouping operations.

To address these issues, our work introduces a novel hybrid approach for handling stateful operations and groupings within workflows, leveraging a new Redis mapping. We also propose an auto-scaling mechanism integrated into dispel4py’s dynamic optimization. Our experiments showcase the effectiveness of autoscaling optimization, achieving efficiency while upholding performance. In the best case, auto-scaling reduces dispel4py’s runtime to 87% compared to the baseline, using only 76% of process resources. Importantly, our optimized stateful dispel4py demonstrates a remarkable speedup, utilizing just 32% of the runtime compared to the contender.
Original languageEnglish
Title of host publication18th Workshop on Workflows in Support of Large-Scale Science
Subtitle of host publicationWORKS 2023
PublisherIEEE
Publication statusAccepted/In press - 8 Sept 2023
Event18th Workshop on Workflows in Support of Large-Scale Science (WORKS 2023) - Denver, United States
Duration: 12 Nov 202312 Nov 2023
Conference number: 18
https://works-workshop.org/

Conference

Conference18th Workshop on Workflows in Support of Large-Scale Science (WORKS 2023)
Abbreviated titleWORKS 2023
Country/TerritoryUnited States
CityDenver
Period12/11/2312/11/23
Internet address

Keywords

  • Scientific workflows
  • Stream-based workflow
  • Workflow optimization
  • Auto-scaling
  • Stateful application
  • dispel4py

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