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Comparing Biological Sequences in GPUs, Hybrid Platforms and Federated Clouds: Three Case Studies
Alba Cristina Magalhaes Alves de Melo and Alessandro Ferreira Leite

04 April 2013, 15h30 - 04 April 2013, 16h30
Salle/Bat : 465/PCRI-N
Contact : christine.eisenbeis@lri.fr

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Résumé :
Biological Sequence Comparison is a very important problem in Bioinformatics. Comparing sequences with exact methods such as Smith-Waterman takes a considerablee amount of time and, for this reason, parallel strategies have been employed to obtain results faster. In this talk, we will first present the biological sequence comparison problem and some exact algorithms used to solve it. We will then talk about three case studies: (a) exact alignment of Megabase DNA sequences in GPUs (Graphics Processing Units); (b) exact comparison of a set of protein query sequences with a huge genomic database in a hybrid platform composed of multicores and GPUs and (c) exact comparison of a set of protein sequences with a huge genomic database in a Federated Public Cloud environment.

Short Bio
Alba Cristina Magalhaes Alves de Melo received the Ph.D. degree in Computer Science from the Institut National Polytechnique de Grenoble, France, in 1996, the Ms.C. degree in Computer Science from UFRGS, Brazil, in 1991 and the BS degree in Computer Science from UnB, Brazil, in 1986. She is currently an Associate Professor at the University of Brasilia, Brazil, and a CNPq Researcher level 1D. Her current research interests include cloud, grid, cluster and P2P computing, bioinformatics and application-specific accelerators. She has advised 18 graduate students in these research areas and published several papers in prestigious international journals and conferences. She is a Senior Member of the IEEE Society and IEEE Computer Society.
Alessandro Ferreira Leite is a Ph.D. student at the Université Paris-Sud 11 and University of Brasilia. He received his M.Sc. degree in Computer Science from the University of Brasilia, Brazil, in 2010 and his B.Sc. in Computer Science in 2008. His current research interests include cloud, grid, cluster and P2P computing, machine learning and network flows algorithms.

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