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State-of-the-Art GPGPU Applications in Bioinformatics

State-of-the-Art GPGPU Applications in Bioinformatics

Nikitas Papangelopoulos, Dimitrios Vlachakis, Arianna Filntisi, Paraskevas Fakourelis, Louis Papageorgiou, Vasileios Megalooikonomou, Sophia Kossida
Copyright: © 2013 |Volume: 2 |Issue: 4 |Pages: 25
ISSN: 2160-9586|EISSN: 2160-9594|EISBN13: 9781466635081|DOI: 10.4018/ijsbbt.2013100103
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MLA

Papangelopoulos, Nikitas, et al. "State-of-the-Art GPGPU Applications in Bioinformatics." IJSBBT vol.2, no.4 2013: pp.24-48. http://doi.org/10.4018/ijsbbt.2013100103

APA

Papangelopoulos, N., Vlachakis, D., Filntisi, A., Fakourelis, P., Papageorgiou, L., Megalooikonomou, V., & Kossida, S. (2013). State-of-the-Art GPGPU Applications in Bioinformatics. International Journal of Systems Biology and Biomedical Technologies (IJSBBT), 2(4), 24-48. http://doi.org/10.4018/ijsbbt.2013100103

Chicago

Papangelopoulos, Nikitas, et al. "State-of-the-Art GPGPU Applications in Bioinformatics," International Journal of Systems Biology and Biomedical Technologies (IJSBBT) 2, no.4: 24-48. http://doi.org/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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