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Performance Analysis of General-Purpose Computation on Commodity Graphics Hardware: A Case Study Using Bioinformatics

Weiguo Liu, Bertil Schmidt, Wolfgang Muller-Wittig
School of Computer Engineering, Nanyang Technological University, Singapore, Singapore 639798
The Journal of VLSI Signal Processing, Vol. 48, No. 3. (1 September 2007), pp. 209-221.

@article{liu2007performance,

   title={Performance Analysis of General-Purpose Computation on Commodity Graphics Hardware: A Case Study Using Bioinformatics},

   author={Liu, W. and Schmidt, B. and M{\”u}ller-Wittig, W.},

   journal={The Journal of VLSI Signal Processing},

   volume={48},

   number={3},

   pages={209–221},

   issn={0922-5773},

   year={2007},

   publisher={Springer}

}

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Using modern graphics processing units for no-graphics high performance computing is motivated by their enhanced programmability, attractive cost/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.
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