Linear algebra operators for GPU implementation of numerical algorithms
ACM SIGGRAPH 2003 Papers
Fast computation of database operations using graphics processors
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Merrimac: Supercomputing with Streams
Proceedings of the 2003 ACM/IEEE conference on Supercomputing
ClawHMMER: A Streaming HMMer-Search Implementatio
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Bio-sequence database scanning on a GPU
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
GPU-ClustalW: using graphics hardware to accelerate multiple sequence alignment
HiPC'06 Proceedings of the 13th international conference on High Performance Computing
Initial experiences porting a bioinformatics application to a graphics processor
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
GPU accelerated smith-waterman
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Evaluation of the SUN UltraSparc T2+ Processor for Computational Science
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
Triangular matrix inversion on Graphics Processing Unit
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
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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.