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A Mixed Approach Towards Improving Software Performance Of Compute Unified Device Architecture Applications

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  • Alexandru Pîrjan

    (Romanian-American University, Bucharest, Romania)

Abstract

One of the most important aspects when developing applications that leverage the powerful parallel processing power of graphics processing units (GPUs) that offer support for the Compute Unified Device Architecture (CUDA) is to divide the tasks that are to be processed appropriately into sections that can be processed either serially or in parallel. The paper analyses this important developing issue by proposing a mixed approach to programming efficient software applications. The article makes an in depth analysis of the key aspects that carry significant weight when deciding to parallelize a certain part of an application: the analysis phase of the application that is about to be parallelized; the amount of time involved to achieve the implementation; the feasibility of parallelizing the source code; situations when one should aim for central processing units optimization techniques that yield better performance on sequential source code rather than parallelizing the whole algorithm.

Suggested Citation

  • Alexandru Pîrjan, 2016. "A Mixed Approach Towards Improving Software Performance Of Compute Unified Device Architecture Applications," Romanian Economic Business Review, Romanian-American University, vol. 10(2), pages 448-459, December.
    • Handle: RePEc:rau:journl:v:10:y:2016:i:2:p:448-459
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    File URL: http://www.rebe.rau.ro/RePEc/rau/jisomg/WI16/JISOM-WI16-A17.pdf
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    References listed on IDEAS

    as
    1. Ion LUNGU & Dana-Mihaela PETROSANU & Alexandru PIRJAN, 2012. "Optimization Solutions for Improving the Performance of the Parallel Reduction Algorithm Using Graphics Processing Units," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 16(3), pages 72-86.
    2. Ion LUNGU & Adela BÂRA & George CĂRUTASU & Alexandru PÎRJAN, & Simona-Vasilica OPREA, 2016. "Prediction Intelligent System In The Field Of Renewable Energies Through Neural Networks," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(1), pages 85-102.
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