A Hierarchical Task Scheduler for Heterogeneous Computing

Narasinga Rao Miniskar, Frank Liu, Aaron R. Young, Dwaipayan Chakraborty, Jeffrey S. Vetter

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Heterogeneous computing is one of the future directions of HPC. Task scheduling in heterogeneous computing must balance the challenge of optimizing the application performance and the need for an intuitive interface with the programming run-time to maintain programming portability. The challenge is further compounded by the varying data communication time between tasks. This paper proposes RANGER, a hardware-assisted task-scheduling framework. By integrating RISC-V cores with accelerators, the RANGER scheduling framework divides scheduling into global and local levels. At the local level, RANGER further partitions each task into fine-grained subtasks to reduce the overall makespan. At the global level, RANGER maintains the coarse granularity of the task specification, thereby maintaining programming portability. The extensive experimental results demonstrate that RANGER achieves a 12.7 × performance improvement on average, while only requires 2.7 % of area overhead.

Original languageEnglish
Title of host publicationHigh Performance Computing - 36th International Conference, ISC High Performance 2021, Proceedings
EditorsBradford L. Chamberlain, Bradford L. Chamberlain, Ana-Lucia Varbanescu, Hatem Ltaief, Piotr Luszczek
PublisherSpringer Science and Business Media Deutschland GmbH
Pages57-76
Number of pages20
ISBN (Print)9783030787127
DOIs
StatePublished - 2021
Event36th International Conference on High Performance Computing, ISC High Performance 2021 - Virtual, Online
Duration: Jun 24 2021Jul 2 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12728 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference36th International Conference on High Performance Computing, ISC High Performance 2021
CityVirtual, Online
Period06/24/2107/2/21

Funding

This research was supported in part by the DOE Advanced Scientific Computing Research Program Sawtooth Project and the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle LLC for DOE. This material is based upon work supported by the US Department of Energy (DOE) Office of Science, Office of Advanced Scientific Computing Research under contract number DE-AC05-00OR22725. This research was supported in part by the DOE Advanced Scientific Computing Research Program Sawtooth Project and the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle LLC for DOE. This manuscript has been co-authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan.

Keywords

  • Accelerators
  • Challenges in programming for massive scale
  • Extreme heterogeneity
  • HPC system architecture

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