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dc.contributor.authorSiddiqui, Shahzeb
dc.contributor.authorFeki, Saber
dc.date.accessioned2017-06-12T10:24:01Z
dc.date.available2017-06-12T10:24:01Z
dc.date.issued2014-05-04
dc.identifier.urihttp://hdl.handle.net/10754/624945
dc.description.abstractGraphics 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.titlePredictive Performance Tuning of OpenACC Accelerated Applications
dc.typePoster
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.contributor.departmentKAUST Supercomputing Laboratory (KSL)
dc.contributor.departmentComputer, Electrical and Mathematical Sciences & Engineering (CEMSE)
dc.conference.dateMay 4-6, 2014
dc.conference.nameSHAXC-2 Workshop 2014
dc.conference.locationKAUST
kaust.personSiddiqui, Shahzeb
kaust.personFeki, Saber
refterms.dateFOA2018-06-13T12:35:04Z


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