Predictive Performance Tuning of OpenACC Accelerated Applications
dc.contributor.author | Siddiqui, Shahzeb | |
dc.contributor.author | Feki, Saber | |
dc.date.accessioned | 2017-06-12T10:24:01Z | |
dc.date.available | 2017-06-12T10:24:01Z | |
dc.date.issued | 2014-05-04 | |
dc.identifier.uri | http://hdl.handle.net/10754/624945 | |
dc.description.abstract | Graphics Processing Units (GPUs) are gradually becoming mainstream in supercomputing as their capabilities to significantly accelerate a large spectrum of scientific applications have been clearly identified and proven. Moreover, with the introduction of high level programming models such as OpenACC [1] and OpenMP 4.0 [2], these devices are becoming more accessible and practical to use by a larger scientific community. However, performance optimization of OpenACC accelerated applications usually requires an in-depth knowledge of the hardware and software specifications. We suggest a prediction-based performance tuning mechanism [3] to quickly tune OpenACC parameters for a given application to dynamically adapt to the execution environment on a given system. This approach is applied to a finite difference kernel to tune the OpenACC gang and vector clauses for mapping the compute kernels into the underlying accelerator architecture. Our experiments show a significant performance improvement against the default compiler parameters and a faster tuning by an order of magnitude compared to the brute force search tuning. | |
dc.title | Predictive Performance Tuning of OpenACC Accelerated Applications | |
dc.type | Poster | |
dc.contributor.department | Computer Science Program | |
dc.contributor.department | Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division | |
dc.contributor.department | KAUST Supercomputing Laboratory (KSL) | |
dc.conference.date | May 4-6, 2014 | |
dc.conference.name | SHAXC-2 Workshop 2014 | |
dc.conference.location | KAUST | |
kaust.person | Siddiqui, Shahzeb | |
kaust.person | Feki, Saber | |
refterms.dateFOA | 2018-06-13T12:35:04Z |
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Posters
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KAUST Supercomputing Laboratory (KSL)
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Computer Science Program
For more information visit: https://cemse.kaust.edu.sa/cs -
Scalable Hierarchical Algorithms for eXtreme Computing (SHAXC-2) Workshop 2014
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Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
For more information visit: https://cemse.kaust.edu.sa/