The CIPRES science gateway: a community resource for phylogenetic analyses
Proceedings of the 2011 TeraGrid Conference: Extreme Digital Discovery
Parallel multi-objective approaches for inferring phylogenies
EvoBIO'10 Proceedings of the 8th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
A new hybrid parallel algorithm for mrbayes
ICA3PP'10 Proceedings of the 10th international conference on Algorithms and Architectures for Parallel Processing - Volume Part I
Fine-grain parallelism using multi-core, Cell/BE, and GPU Systems
Parallel Computing
Proceedings of the ATIP/A*CRC Workshop on Accelerator Technologies for High-Performance Computing: Does Asia Lead the Way?
Embedding CIPRES science gateway capabilities in phylogenetics software environments
Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery
The Journal of Supercomputing
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We are currently faced with the situation where applications have increasing computational demands and there is a wide selection of parallel processor systems. In this paper we focus on exploiting fine-grain parallelism for a demanding Bioinformatics application - MrBayes - and its Phylogenetic Likelihood Functions (PLF) using different architectures. Our experiments compare side-by-side the scalability and performance achieved using general-purpose multi-core processors, the Cell/BE, and Graphics Processor Units (GPU). The results indicate that all processors scale well for larger computation and data sets. Also, GPU and Cell/BE processors achieve the best improvement for the parallel code section. Nevertheless, data transfers and the execution of the serial portion of the code are the reasons for their poor overall performance. The general-purpose multi-core processors prove to be simpler to program and provide the best balance between an efficient parallel and serial execution, resulting in the largest speedup.