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State-of-the-Art GPGPU Applications in Bioinformatics
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Author(s): Nikitas Papangelopoulos (Bioinformatics & Medical Informatics Team, Biomedical Research Foundation, Academy of Athens, Athens, Greece), Dimitrios Vlachakis (Bioinformatics & Medical Informatics Team, Biomedical Research Foundation, Academy of Athens, Athens, Greece), Arianna Filntisi (Bioinformatics & Medical Informatics Team, Biomedical Research Foundation, Academy of Athens, Athens, Greece & School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece), Paraskevas Fakourelis (Bioinformatics & Medical Informatics Team, Biomedical Research Foundation, Academy of Athens, Athens, Greece), Louis Papageorgiou (Bioinformatics & Medical Informatics Team, Biomedical Research Foundation, Academy of Athens, Athens, Greece), Vasileios Megalooikonomou (Computer Engineering and Informatics Department, School of Engineering, University of Patras, Patras, Greece)and Sophia Kossida (Bioinformatics & Medical Informatics Team, Biomedical Research Foundation, Academy of Athens, Athens, Greece)
Copyright: 2013
Volume: 2
Issue: 4
Pages: 25
Source title:
International Journal of Systems Biology and Biomedical Technologies (IJSBBT)
Editor(s)-in-Chief: Tagelsir Mohamed Gasmelseid (International University of Africa, Sudan)
DOI: 10.4018/ijsbbt.2013100103
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Abstract
The exponential growth of available biological data in recent years coupled with their increasing complexity has made their analysis a computationally challenging process. Traditional central processing unist (CPUs) are reaching their limit in processing power and are not designed primarily for multithreaded applications. Graphics processing units (GPUs) on the other hand are affordable, scalable computer powerhouses that, thanks to the ever increasing demand for higher quality graphics, have yet to reach their limit. Typically high-end CPUs have 8-16 cores, whereas GPUs can have more than 2,500 cores. GPUs are also, by design, highly parallel, multicore and multithreaded, able of handling thousands of threads doing the same calculation on different subsets of a large data set. This ability is what makes them perfectly suited for biological analysis tasks. Lately this potential has been realized by many bioinformatics researches and a huge variety of tools and algorithms have been ported to GPUs, or designed from the ground up to maximize the usage of available cores. Here, we present a comprehensive review of available bioinformatics tools ranging from sequence and image analysis to protein structure prediction and systems biology that use NVIDIA Compute Unified Device Architecture (CUDA) general-purpose computing on graphics processing units (GPGPU) programming language.
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