CoreTSAR: Core Task-Size Adapting Runtime

Thomas R.W. Scogland, Wu Chun Feng, Barry Rountree, Bronis R. De Supinski

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Heterogeneity continues to increase at all levels of computing, with the rise of accelerators such as GPUs, FPGAs, and other co-processors into everything from desktops to supercomputers. As a consequence, efficiently managing such disparate resources has become increasingly complex. CoreTSAR seeks to reduce this complexity by adaptively worksharing parallel-loop regions across compute resources without requiring any transformation of the code within the loop. Our results show performance improvements of up to three-fold over a current state-of-the-art heterogeneous task scheduler as well as linear performance scaling from a single GPU to four GPUs for many codes. In addition, CoreTSAR demonstrates a robust ability to adapt to both a variety of workloads and underlying system configurations.

Original languageEnglish
Article number6936921
Pages (from-to)2970-2983
Number of pages14
JournalIEEE Transactions on Parallel and Distributed Systems
Issue number11
Early online date27 Oct 2014
Publication statusPublished - 01 Nov 2015


  • coscheduling
  • GPU
  • Heterogeneous
  • OpenACC
  • OpenMP

ASJC Scopus subject areas

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


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