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An efficient task-based approach for solving the n-body problem on multicore architectures
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
2010 (English)Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Reykjavík: University of Iceland , 2010. 74:1-4 p.
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:uu:diva-135981OAI: oai:DiVA.org:uu-135981DiVA: diva2:375923
Conference
PARA 2010: State of the Art in Scientific and Parallel Computing
Projects
eSSENCEUPMARC
Available from: 2010-06-07 Created: 2010-12-09 Last updated: 2012-11-26Bibliographically approved
In thesis
1. Leveraging multicore processors for scientific computing
Open this publication in new window or tab >>Leveraging multicore processors for scientific computing
2012 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis deals with how to develop scientific computing software that runs efficiently on multicore processors. The goal is to find building blocks and programming models that increase the productivity and reduce the probability of programming errors when developing parallel software.

In our search for new building blocks, we evaluate the use of hardware transactional memory for constructing atomic floating point operations. Using benchmark applications from scientific computing, we show in which situations this achieves better performance than other approaches.

Driven by the needs of scientific computing applications, we develop a programming model and implement it as a reusable library. The library provides a run-time system for executing tasks on multicore architectures, with efficient and user-friendly management of dependencies. Our results from scientific computing benchmarks show excellent scaling up to at least 64 cores. We also investigate how the execution time depend on the task granularity, and build a model for the performance of the task library.

Place, publisher, year, edition, pages
Uppsala University, 2012
Series
Information technology licentiate theses: Licentiate theses from the Department of Information Technology, ISSN 1404-5117 ; 2012-006
National Category
Software Engineering Computational Mathematics
Research subject
Scientific Computing
Identifiers
urn:nbn:se:uu:diva-181266 (URN)
Supervisors
Projects
UPMARCeSSENCE
Available from: 2012-09-28 Created: 2012-09-20 Last updated: 2017-08-31Bibliographically approved

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Language
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Output format
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