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Performance predictions for general-purpose computation on GPUs

: Liu, Weiguo; Müller-Wittig, Wolfgang K.; Schmidt, Bertil

Li, J. ; IEEE Computer Society:
International Conference on Parallel Processing 2007. CD-ROM : ICPP 2007, XiAn China, September 10-14, 2007
Los Alamitos, Calif.: IEEE Computer Society Press, 2007
ISBN: 978-0-7695-2933-2
ISBN: 0-7695-2933-X
8 S.
International Conference on Parallel Processing (ICPP) <36, 2007, Xian>
Fraunhofer IGD ()
performance analysis; general purpose computation on graphics processing unit (GPGPU); sequence alignment

Using modern graphics processing units for no-graphics high performance computing is motivated by their enhanced programmability, attractive price/performance ratio and incredible growth in speed. Although the pipeline of a modern graphics processing unit (GPU) permits high throughput and more concurrency, they bring more complexities in analyzing the performance of GPU-based applications.
In this paper, we identify factors that determine performance of GPU-based applications. We then classify them into three categories: data-linear, data-constant and computation-dependent. According to the characteristics of these factors, we propose a performance model for each factor. These models are then used to predict the performance of bio-sequence database scanning application on GPUs. Theoretical analyses and measurements show that our models can achieve precise performance predictions.