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Exploring new search algorithms and hardware for phylogenetics: RAxML meets the IBM cell
dc.creator | Stamatakis, A. | en |
dc.creator | Blagojevic, F. | en |
dc.creator | Nikolopoulos, D. S. | en |
dc.creator | Antonopoulos, C. D. | en |
dc.date.accessioned | 2015-11-23T10:48:28Z | |
dc.date.available | 2015-11-23T10:48:28Z | |
dc.date.issued | 2007 | |
dc.identifier | 10.1007/s11265-007-0067-4 | |
dc.identifier.issn | 0922-5773 | |
dc.identifier.uri | http://hdl.handle.net/11615/33337 | |
dc.description.abstract | Phylogenetic inference is considered to be one of the grand challenges in Bioinformatics due to the immense computational requirements. RAxML is currently among the fastest and most accurate programs for phylogenetic tree inference under the Maximum Likelihood (ML) criterion. First, we introduce new tree search heuristics that accelerate RAxML by a factor of 2.43 while returning equally good trees. The performance of the new search algorithm has been assessed on 18 real-world datasets comprising 148 up to 4,843 DNA sequences. We then present the implementation, optimization, and evaluation of RAxML on the IBM Cell Broadband Engine. We address the problems and provide solutions pertaining to the optimization of floating point code, control flow, communication, and scheduling of multi-level parallelism on the Cell. | en |
dc.source | Journal of Vlsi Signal Processing Systems for Signal Image and Video Technology | en |
dc.source.uri | <Go to ISI>://WOS:000249211900007 | |
dc.subject | phylogenetic inference | en |
dc.subject | maximum likelihood | en |
dc.subject | RAxML | en |
dc.subject | IBM cell | en |
dc.subject | MAXIMUM-LIKELIHOOD | en |
dc.subject | MIXED MODELS | en |
dc.subject | RECONSTRUCTION | en |
dc.subject | TREES | en |
dc.subject | INFERENCE | en |
dc.subject | DIVERSITY | en |
dc.subject | Computer Science, Information Systems | en |
dc.subject | Engineering, Electrical & | en |
dc.subject | Electronic | en |
dc.title | Exploring new search algorithms and hardware for phylogenetics: RAxML meets the IBM cell | en |
dc.type | journalArticle | en |
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