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dc.contributor.authorGuerrero, Ginés David
dc.contributor.authorWallace, Richard M.
dc.contributor.authorCecilia Canales, José María
dc.contributor.authorGarcía, José Manuel
dc.contributor.authorMozos, Daniel
dc.contributor.authorVázquez Poletti, José L.
dc.contributor.authorPérez Sánchez, Horacio
dc.date.accessioned2018-05-10T09:08:38Z
dc.date.available2018-05-10T09:08:38Z
dc.date.issued2013-08-14
dc.identifier.urihttp://hdl.handle.net/10952/3112
dc.description.abstractVirtual Screening (VS) methods can considerably aid drug discovery research, predicting how ligands interact with drug targets. BINDSURF is an efficient and fast blind VS methodology for the determination of protein binding sites, depending on the ligand, using the massively parallel architecture of graphics processing units(GPUs) for fast unbiased prescreening of large ligand databases. In this contribution, we provide a performance/cost model for the execution of this application on both local system and public cloud infrastructures. With our model, it is possible to determine which is the best infrastructure to use in terms of execution time and costs for any given problem to be solved by BINDSURF. Conclusions obtained from our study can be extrapolated to other GPU‐based VS methodologieses
dc.language.isoenes
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleA Performance/Cost Model for a CUDA Drug Discovery Application on Physical and Public Cloud Infrastructureses
dc.typearticlees
dc.rights.accessRightsopenAccesses
dc.journal.titleConcurrency and Computation Practice and Experiencees
dc.description.disciplineIngeniería, Industria y Construcciónes


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional