Using frequency distance filteration for reducing database search workload on GPU-based cloud service

The Smith-Waterman algorithm is the most widely used algorithm to analyze the similarity between protein and DNA sequences and suitable for the database search due to its high sensitivity. However, Smith-Waterman still is a very time-consuming method. CUDA programming can efficiently improve the computations by using the computing power of the massive computing hardware as GPUs. In this paper, we proposed an efficient frequency based filter method instead of just speed up the Smith-Waterman comparison but waste computing resource to deal with those unnecessary comparisons. We implemented the Smith-Waterman algorithm by introduction of the techniques from earlier researches and add in our real-time filter method on Graphic Processing Units to filter unnecessary comparisons. We also design a user friendly interface to provide the service in the potential clouding computing environment. In our research we choose two data sets, H1N1 VH protein database and Human protein database then compare CUDA-SW and CUDA-SW with filter, we called CUDA-SWf we can obtain up to 41% performance improve from reduce unnecessary sequence alignments.