Fine-grained parallel implementations for SWAMP+ Smith–Waterman alignment

The Smith-Waterman algorithm has a great sensitivity when used for biological sequence-database searches, but at the expense of high computing-power requirements. To overcome this problem, there are implementations in literature that exploit the different hardware-architectures available in a standard PC, such as GPU, CPU, and coprocessors. We introduce an application that splits the original database-search problem into smaller parts, resolves each of them by executing the most efficient implementations of the Smith-Waterman algorithms in different hardware architectures, and finally unifies the generated results. Using non-overlapping hardware allows simultaneous execution, and up to 2.58-fold performance gain, when compared with any other algorithm to search sequence databases. Even the performance of the popular BLAST heuristic is exceeded in 78% of the tests. The application has been tested with standard hardware: Intel i7-4820K CPU, Intel Xeon Phi 31S1P coprocessors, and nVidia GeForce GTX 960 graphics cards. An important increase in performance has been obtained in a wide range of situations, effectively exploiting the available hardware.

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Coarse Grain Load Balance Algorithm for Detecting Similar Regions in DNA and Proteins Sequences

Elnour

Latif

Isnin

2014

IJST

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Fine-grained parallel implementations for SWAMP+ Smith–Waterman alignment

Cited by 4 publications

References 28 publications

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The Solution of Massive Tasks in GIS Exemplified by Determining Terrain Surface Types Above Gas Pipelines in the Czech Republic

Speeding-up Bioinformatics Algorithms with Heterogeneous Architectures: Highly Heterogeneous Smith-Waterman (HHeterSW)

Coarse Grain Load Balance Algorithm for Detecting Similar Regions in DNA and Proteins Sequences

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