Current trends in computer programming look for solutions in the challenging task of porting and optimizing existing algorithms to many-core architectures with tens of Central Processing Units (CPUs). Yet, the lack of standardized general-purpose parallel programming and porting methodologies represents the main bottleneck on these developments. We have focused on bioinformatics applied to genomics in general and the so-called >Next-Generation> Sequencing (NGS) in particular, in order to study the viability and cost of porting and optimizing well known algorithms to a many-core architecture. Three different methods are tackled in order to implement existing algorithms in Tile64, corresponding to a microprocessor containing 64 CPUs, each of them being capable of executing an independent Linux operating system. Three different approaches have been explored: (i) implementation of the Needleman-Wunsch/Smith-Waterman pairwise aligner from scratch; (ii) direct translation of the Message Passing Interface (MPI) C++ ABySS assembly algorithm with changes on the communication layer; and (iii) migration of the ClustalW tool, parallelizing only the most time-consuming stage. The performance-gain/development-cost tradeoffs indicate that the Tile64 microprocessor has the potential to increase the performance of bioinformatics in an unprecedented way for a standalone Personal Computer (PC). Yet, the effective exploitation of these parallel implementations requires a detailed understanding of the peculiar many-core characteristics when migrating previous non-parallel source codes. © 010 Elsevier B.V. All rights reserved.

This work was supported by “Ministerio de Ciencia e Innovación” (MICINN grants AGL2010-17316, BIO2009-07443 and BIO2011-15237); “Consejería de Agricultura y Pesca” of “Junta de Andalucía” (041/C/2007, 75/C/2009 & 56/C/2010); “Grupo PAI” (AGR-248); and “Universidad de Córdoba” (“Ayuda a Grupos”), Spain.

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