Liu, WeiguoWeiguoLiuMüller-Wittig, Wolfgang K.Wolfgang K.Müller-WittigSchmidt, BertilBertilSchmidt2022-03-032022-03-032007https://publica.fraunhofer.de/handle/publica/21354510.1007/s11265-007-0064-7Using 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.engraphic hardwaredynamic programmingperformance analysisgeneral purpose computation on graphics processing unit (GPGPU)sequence alignment006004journal article