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Formalizing data locality in task parallel applications
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Architecture and Computer Communication. (Uppsala Architecture Research Team (UART))ORCID iD: 0000-0003-2314-7307
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Architecture and Computer Communication. (Uppsala Architecture Research Team (UART))
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Architecture and Computer Communication. (Uppsala Architecture Research Team (UART))
2016 (English)In: Algorithms and Architectures for Parallel Processing, Springer, 2016, 43-61 p.Conference paper (Refereed)
Abstract [en]

Task-based programming provides programmers with an intuitive abstraction to express parallelism, and runtimes with the flexibility to adapt the schedule and load-balancing to the hardware. Although many profiling tools have been developed to understand these characteristics, the interplay between task scheduling and data reuse in the cache hierarchy has not been explored. These interactions are particularly intriguing due to the flexibility task-based runtimes have in scheduling tasks, which may allow them to improve cache behavior. This work presents StatTask, a novel statistical cache model that can predict cache behavior for arbitrary task schedules and cache sizes from a single execution, without programmer annotations. StatTask enables fast and accurate modeling of data locality in task-based applications for the first time. We demonstrate the potential of this new analysis to scheduling by examining applications from the BOTS benchmarks suite, and identifying several important opportunities for reuse-aware scheduling.

Place, publisher, year, edition, pages
Springer, 2016. 43-61 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10049
National Category
Computer Science
Identifiers
URN: urn:nbn:se:uu:diva-310341DOI: 10.1007/978-3-319-49956-7_4ISI: 000389797000004ISBN: 978-3-319-49955-0 (print)ISBN: 978-3-319-49956-7 (electronic)OAI: oai:DiVA.org:uu-310341DiVA: diva2:1056177
Conference
16th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP), December 14–16, Granada, Spain
Projects
UPMARCResource Sharing Modeling
Funder
Swedish Foundation for Strategic Research , FFL12-0051
Available from: 2016-11-19 Created: 2016-12-14 Last updated: 2017-03-29Bibliographically approved

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Ceballos, GermánHagersten, ErikBlack-Schaffer, David
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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association
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Language
  • de-DE
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  • en-US
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  • nn-NO
  • nn-NB
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Output format
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