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A fast band–Krylov eigensolver for macromolecular functional motion simulation on multicore architectures and graphics processors
dc.contributor.author | Aliaga Estellés, José Ignacio | |
dc.contributor.author | Alonso-Jordá, Pedro | |
dc.contributor.author | Badía, José | |
dc.contributor.author | Chacón, Pablo | |
dc.contributor.author | Davidovic, Davor | |
dc.contributor.author | López Blanco, José R. | |
dc.contributor.author | Quintana-Orti, Enrique S. | |
dc.date.accessioned | 2016-06-06T10:12:11Z | |
dc.date.available | 2016-06-06T10:12:11Z | |
dc.date.issued | 2016-03-15 | |
dc.identifier.citation | ALIAGA ESTELLÉS, José Ignacio; ALONSO JORDÁ, Pedro; BADÍA CONTELLES, José Manuel; CHACÓN, Pablo; DAVIDOVIC, Davor; LÓPEZ BLANCO, José R.; QUINTANA ORTÍ, Enrique S. A fast band–Krylov eigensolver for macromolecular functional motion simulation on multicore architectures and graphics processors. Journal of Computational Physics (2016), v. 309, pp. 314-323 | ca_CA |
dc.identifier.uri | http://hdl.handle.net/10234/160362 | |
dc.description.abstract | We introduce a new iterative Krylov subspace-based eigensolver for the simulation of macromolecular motions on desktop multithreaded platforms equipped with multicore processors and, possibly, a graphics accelerator (GPU). The method consists of two stages, with the original problem first reduced into a simpler band-structured form by means of a high-performance compute-intensive procedure. This is followed by a memory-intensive but low-cost Krylov iteration, which is off-loaded to be computed on the GPU by means of an efficient data-parallel kernel. The experimental results reveal the performance of the new eigensolver. Concretely, when applied to the simulation of macromolecules with a few thousands degrees of freedom and the number of eigenpairs to be computed is small to moderate, the new solver outperforms other methods implemented as part of high-performance numerical linear algebra packages for multithreaded architectures. | ca_CA |
dc.description.sponsorShip | This work was supported by the CICYT projects TIN2011-23283 and TIN2014-53495-R of the MINECO and FEDER, and project P1-1B2013-20 of the Fundació Caixa Castelló-Bancaixa and UJI. | ca_CA |
dc.format.extent | 10 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Elsevier | ca_CA |
dc.relation.isPartOf | Journal of Computational Physics (2016), v. 309 | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/CNE/1.0/ | * |
dc.subject | Computational biology | ca_CA |
dc.subject | Macromolecular machines | ca_CA |
dc.subject | Eigenvalue problems | ca_CA |
dc.subject | Krylov subspace methods | ca_CA |
dc.subject | Multicore processors | ca_CA |
dc.subject | Graphics processors | ca_CA |
dc.title | A fast band–Krylov eigensolver for macromolecular functional motion simulation on multicore architectures and graphics processors | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | http://dx.doi.org/10.1016/j.jcp.2016.01.007 | |
dc.rights.accessRights | info:eu-repo/semantics/restrictedAccess | ca_CA |
dc.relation.publisherVersion | http://www.sciencedirect.com/science/article/pii/S0021999116000085 | ca_CA |
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