Abstract
We describe using OpenMP to compute δ-hyperbolicity, a quantity of interest in social and information network analysis, at a scale that uses up to 1000 threads. By considering both OpenMP workshare and tasking models to parallelize the computations, we find that multiple task levels permits finer grained tasks at runtime and results in better performance at scale than worksharing constructs. We also characterize effects of task inflation, load balancing, and scheduling overhead in this application, using both GNU and Intel compilers. Finally, we show how OpenMP 3.1 tasking clauses can be used to mitigate overheads at scale.
Original language | English |
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Title of host publication | OpenMP in the Era of Low Power Devices and Accelerators - 9th International Workshop on OpenMP, IWOMP 2013, Proceedings |
Pages | 71-83 |
Number of pages | 13 |
DOIs | |
State | Published - 2013 |
Event | 9th International Workshop on OpenMP in the Era of Low Power Devices and Accelerators, IWOMP 2013 - Canberra, ACT, Australia Duration: Sep 16 2013 → Sep 18 2013 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 8122 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 9th International Workshop on OpenMP in the Era of Low Power Devices and Accelerators, IWOMP 2013 |
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Country/Territory | Australia |
City | Canberra, ACT |
Period | 09/16/13 → 09/18/13 |
Funding
This manuscript has been authored by a contractor of the U.S. Government under Contract No. DE-AC05-00OR22725. Accordingly, the U.S. Government retains a non-exclusive, royalty-free license to publish or reproduce the published form of this contribution, or allow others to do so, for U.S. Government purposes.