dc.contributor.author | Pratas, F. | en |
dc.contributor.author | Trancoso, Pedro | en |
dc.contributor.author | Stamatakis, A. | en |
dc.contributor.author | Sousa, L. | en |
dc.creator | Pratas, F. | en |
dc.creator | Trancoso, Pedro | en |
dc.creator | Stamatakis, A. | en |
dc.creator | Sousa, L. | en |
dc.date.accessioned | 2019-11-13T10:42:05Z | |
dc.date.available | 2019-11-13T10:42:05Z | |
dc.date.issued | 2009 | |
dc.identifier.isbn | 978-0-7695-3802-0 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/54886 | |
dc.description.abstract | 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. © 2009 IEEE. | en |
dc.source | Proceedings of the International Conference on Parallel Processing | en |
dc.source | 38th International Conference on Parallel Processing, ICPP-2009 | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-77951491232&doi=10.1109%2fICPP.2009.30&partnerID=40&md5=29586c8885b83714e40aecdffae8ce47 | |
dc.subject | Data transfer | en |
dc.subject | Nanotechnology | en |
dc.subject | Multi-core processor | en |
dc.subject | Bioinformatics | en |
dc.subject | Data sets | en |
dc.subject | Program processors | en |
dc.subject | Multi core | en |
dc.subject | Wide selection | en |
dc.subject | Parallel code | en |
dc.subject | Computational demands | en |
dc.subject | Fine grain parallelism | en |
dc.subject | Bioinformatics applications | en |
dc.subject | Computer graphics equipment | en |
dc.subject | Graphics processor units | en |
dc.subject | Likelihood functions | en |
dc.subject | Parallel processor | en |
dc.subject | Serial execution | en |
dc.title | Fine-grain parallelism using multi-core, cell/BE, and GPU systems: Accelerating the phylogenetic likelihood function | en |
dc.type | info:eu-repo/semantics/conferenceObject | |
dc.identifier.doi | 10.1109/ICPP.2009.30 | |
dc.description.startingpage | 9 | |
dc.description.endingpage | 17 | |
dc.author.faculty | 002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.type.uhtype | Conference Object | en |
dc.description.notes | <p>Sponsors: International Association for Computers and Communications (IACC) | en |
dc.description.notes | Austrian Computer Society | en |
dc.description.notes | Conference code: 79900 | en |
dc.description.notes | Cited By :36</p> | en |
dc.contributor.orcid | Trancoso, Pedro [0000-0002-2776-9253] | |
dc.gnosis.orcid | 0000-0002-2776-9253 | |