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GPU technology as a platform for accelerating local complexity analysis of protein sequences.

TitleGPU technology as a platform for accelerating local complexity analysis of protein sequences.
Publication TypeJournal Article
Year of Publication2013
AuthorsPapadopoulos, Agathoklis, Ioannis Kirmitzoglou, V. J. Promponas, and Theocharis Theocharides
JournalConference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
Volume2013
Pagination2684-7
Date Published2013
ISSN1557-170X
KeywordsAlgorithms, Amino Acid Sequence, Computational Biology, Databases, Protein, Haemophilus influenzae, Plasmodium falciparum, Proteins, Proteomics, Software
Abstract

The use of GPGPU programming paradigm (running CUDA-enabled algorithms on GPU cards) in Bioinformatics showed promising results [1]. As such a similar approach can be used to speedup other algorithms such as CAST, a popular tool used for masking low-complexity regions (LCRs) in protein sequences [2] with increased sensitivity. We developed and implemented a CUDA-enabled version (GPU_CAST) of the multi-threaded version of CAST software first presented in [3] and optimized in [4]. The proposed software implementation uses the nVIDIA CUDA libraries and the GPGPU programming paradigm to take advantage of the inherent parallel characteristics of the CAST algorithm to execute the calculations on the GPU card of the host computer system. The GPU-based implementation presented in this work, is compared against the multi-threaded, multi-core optimized version of CAST [4] and yielded speedups of 5x-10x for large protein sequence datasets.

DOI10.1109/EMBC.2013.6610093
Alternate JournalConf Proc IEEE Eng Med Biol Soc


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