Performance comparison of GPU programming frameworks with the striped Smith-Waterman algorithm

This paper evaluates and discusses how different GPU programming frameworks affect the performance obtained from GPU acceleration of the striped smith-waterman algorithm used for biological sequence alignment. A total of 6 GPU implementations of the algorithm on NVIDIA GT200b and AMD RV870 using the CUDA and the OpenCL frameworks are compared to analyze cons and pros of explicit descriptions for architecture specific hardware mechanisms in the code. The evaluation results show that the primitive descriptions with the CUDA are still efficient especially for small size data, while better instruction scheduling and optimizations are carried out by the OpenCL compiler. On the other hand, the combination of OpenCL and RV870 which provides a relatively simple view of the architecture is efficient for the large data size.

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