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    Predictive Performance Tuning of OpenACC Accelerated Applications

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    Poster
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    Type
    Poster
    Authors
    Siddiqui, Shahzeb
    Feki, Saber
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    KAUST Supercomputing Laboratory (KSL)
    Date
    2014-05-04
    Permanent link to this record
    http://hdl.handle.net/10754/624945
    
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    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.
    Conference/Event name
    SHAXC-2 Workshop 2014
    Collections
    Posters; KAUST Supercomputing Laboratory (KSL); Computer Science Program; Scalable Hierarchical Algorithms for eXtreme Computing (SHAXC-2) Workshop 2014; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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