In-situ workflow auto-tuning through combining component models

Tong Shu, Yanfei Guo, Justin Wozniak, Xiaoning Ding, Ian Foster, Tahsin Kurc

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

Abstract

In-situ parallel workflows couple multiple component applications via streaming data transfer to avoid data exchange via shared file systems. Such workflows are challenging to configure for optimal performance due to the huge space of possible configurations. Here, we propose an in-situ workflow auto-tuning method, ALIC, which integrates machine learning techniques with knowledge of in-situ workflow structures to enable automated workflow configuration with a limited number of performance measurements. Experiments with real applications show that ALIC identify better configurations than existing methods given a computer time budget.

Original languageEnglish (US)
Title of host publicationPPoPP 2021 - Proceedings of the 2021 26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
PublisherAssociation for Computing Machinery
Pages467-468
Number of pages2
ISBN (Electronic)9781450382946
DOIs
StatePublished - Feb 17 2021
Event26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2021 - Virtual, Online, Korea, Republic of
Duration: Feb 27 2021Mar 3 2021

Publication series

NameProceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP

Conference

Conference26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2021
CountryKorea, Republic of
CityVirtual, Online
Period2/27/213/3/21

All Science Journal Classification (ASJC) codes

  • Software

Keywords

  • auto-tuning
  • component model
  • in-situ workflow

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