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Comparative evaluation of platforms for parallel Ant Colony Optimization
dc.contributor.author | Guerrero, Ginés David | |
dc.contributor.author | Cecilia Canales, José María | |
dc.contributor.author | Llanes, Antonio | |
dc.contributor.author | García, José Manuel | |
dc.contributor.author | Amos, Martyn | |
dc.contributor.author | Ujaldón, Manuel | |
dc.date.accessioned | 2018-05-07T11:14:15Z | |
dc.date.available | 2018-05-07T11:14:15Z | |
dc.date.issued | 2014 | |
dc.identifier.issn | 1573-0484 | |
dc.identifier.other | doi:10.1007/s11227-014-1154-5 | |
dc.identifier.uri | http://hdl.handle.net/10952/3044 | |
dc.description | This is a post-peer-review, pre-copyedit version of an article published in Journal of Supercomputing. The final authenticated version is available online at: http://dx.doi.org/10.1007/s11227-014-1154-5 | es |
dc.description.abstract | The rapidly growing field of nature-inspired computing concerns the development and application of algorithms and methods based on biological or physical principles. This approach is particularly compelling for practitioners in high-performance computing, as natural algorithms are often inherently parallel in nature (for example, they may be based on a “swarm”-like model that uses a population of agents to optimize a function). Coupled with rising interest in nature-based algorithms is the growth in heterogenous computing; systems that use more than one kind of processor. We are therefore interested in the performance characteristics of nature-inspired algorithms on a number of different platforms. To this end, we present a new OpenCL-based implementation of the Ant Colony Optimization algorithm, and use it as the basis of extensive experimental tests. We benchmark the algorithm against existing implementations, on a wide variety of hardware platforms, and offer extensive analysis. This work provides rigorous foundations for future investigations of Ant Colony Optimization on high-performance platforms. | es |
dc.description.sponsorship | This work is jointly supported by the Fundación Séneca (Agencia Regional de Ciencia y Tecnología, Región de Murcia) under grant 15290/PI/2010, by the Spanish MEC and European Commission FEDER under grant TIN2012-31345, by the UCAM under grant PMAFI/26/12, by the Junta de Andalucía under Project of Excellence P12-TIC-1741 and by the supercomputing infrastructure of the NLHPC (ECM-02). We also thank NVIDIA for hardware donation under CUDA Teaching Center 2011-14, CUDA Research Center 2012-14 and CUDA Fellow 2012-14 Awards. | es |
dc.language.iso | en | es |
dc.publisher | Springer | es |
dc.rights | Atribución-NoComercial-CompartirIgual 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | * |
dc.subject | Heterogeneous computing | es |
dc.subject | Ant colony optimization | es |
dc.subject | CUDA | es |
dc.subject | OpenCL | es |
dc.subject | APU | es |
dc.subject | GPU | es |
dc.title | Comparative evaluation of platforms for parallel Ant Colony Optimization | es |
dc.type | article | es |
dc.rights.accessRights | openAccess | es |
dc.journal.title | The Journal of Supercomputing | es |
dc.volume.number | 69 | es |
dc.issue.number | 1 | es |
dc.description.discipline | Ingeniería, Industria y Construcción | es |