Computação paralela na classificação de proteínas sobre a plataforma Cellbe

Tese de mestrado, Informática, Universidade de Lisboa, Faculdade de Ciências, 2010Um dos problemas maiores da bioinformática é a previsão de função de uma proteína. A tecnologia existente já permite obter milhões e milhões de sequências a custo muito reduzido, mas a compreensão de sua função dentro dos vários organismos é ainda um grande mistério para a larga maioria de sequências proteicas existentes. A criação de software eficiente que permita analisar bases de dados de proteínas em busca de metadados é também um desafio para os biólogos e uma área de estudo recente para os cientistas da computação, por representar uma alternativa de baixo custo aos métodos de teste em laboratório. Uma metodologia que se propõe analisar estas bases de dados e anotar as proteínas são os Peptide Programs (PepProg), uma metodologia de aprendizagem automática (machine learning) para classificação funcional de sequências biológicas. Esta dissertação de mestrado se propôs a estudar meios de optimizar o desempenho da implementação existente do método PepProg através da construção de uma implementação alternativa que explore a arquitectura do processador CellBE, presente nas consolas Playstation 3 disponibilizadas pelo Departamento de Informática da Universidade de Lisboa. Para explorar os recursos do CellBE, foi necessário primeiro modificar a implementação existente do método PepProg para que pudesse correr instruções do tipo Single Instruction Multiple Data (SIMD). Esta modificação denominada vectorização, é necessária para que os múltiplos núcleos do processador CellBE pudessem ser utilizados, transformando o PepProg num algoritmo paralelo dentro da arquitectura CellBE. Com o estudo realizado, pode-se concluir que as propostas de vectorização sugeridas nesta dissertação não foram suficientes para a construção de um PepProg paralelo mais eficiente, pois o custo da vectorização do algoritmo em tempo de execução provou-se ser tão alto a ponto de não poder ser compensado pela paralelização do algoritmo.One of the biggest problems on bioinformatics is protein function determination. The existing technology allows the obtaining of millions and millions sequences at very reduced cost, but the understanding of its function inside organisms is still a great mystery for the wide majority of proteomic sequences. The creation of efficient software that allows protein database analyzes in search for metadata is also a challenge for the biologists and an area of recent study for the computation scientists, as it represent a low cost method alternatively to laboratory test methods. A methodology that considers database analysis to write down proteins is the Peptide Programs (PepProg), an automatic learning methodology (machine learning) for functional sorting of biological sequences. This master's degree dissertation studied ways to optimize the existing implementation of the method PepProg by the construction of an alternative implementation that explores CellBE processor architecture, found on Playstation 3 consoles made available by Departamento de Informática of the Universidade de Lisboa. To explore CellBE resources, it was necessary a modification of the existing PepProg implementation to guarantee that only single-instruction multiple-data (SIMD) instructions was present on code. This process, named vectorization, is a prerequisite to allow PepProg program run on CellBE multiple cores in a parallel way. With the accomplished study, it can be concluded that the vectorization methods proposed by this study were not enough for the construction of a more efficient parallel PepProg, because the algorithm vectorization cost in execution time proved to be so high, that the PepProg parallelization gain was not sufficient to compensate the extra computational processing demanded by the vectorization

Preliminary analysis of the cell be processor limitations for sequence alignment applications

Abstract. The fast growth of bioinformatics field has attracted the attention of computer scientists in the last few years. At the same time the increasing database sizes require greater efforts to improve the computational performance. From a computer architecture point of view, we intend to investigate how bioinformatics applications can benefit from future multi-core processors. In this paper we present a preliminary study of the Cell BE processor limitations when executing two representative sequence alignment applications (Ssearch and ClustalW). The inherent large parallelism of the targeted algorithms makes them ideal for architectures supporting multiple dimensions of parallelism (TLP and DLP). However, in the case of Cell BE we identified several architectural limitations that need a careful study and quantification.

Preliminary analysis of the cell BE processor limitations for sequence alignment applications

The fast growth of bioinformatics field has attracted the attention of computer scientists in the last few years. At the same time the increasing database sizes require greater efforts to improve the computational performance. From a computer architecture point of view, we intend to investigate how bioinformatics applications can benefit from future multi-core processors. In this paper we present a preliminary study of the Cell BE processor limitations when executing two representative sequence alignment applications (Ssearch and ClustalW). The inherent large parallelism of the targeted algorithms makes them ideal for architectures supporting multiple dimensions of parallelism (TLP and DLP). However, in the case of Cell BE we identified several architectural limitations that need a careful study and quantification.Peer Reviewe