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GgRwJvJcJg13CCPE

G.D. Guerrero, R.M. Wallace, J.L. Vazquez-Poletti, J.M. Cecilia, J.M. Garcia, D. Mozos, H. Perez-Sanchez. A Performance/Cost Model for a CUDA Drug Discovery Application on Physical and Public Cloud Infrastructures. Concurrency and Computation: Practice and Experience, Wiley(10):1787-1798, 2014.

Abstract

Virtual 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 methodologies

Keywords

[ Medianet ] [ Tin2012-31518 ] [ Cloud ]

Contact

Jose Luis Vazquez-Poletti

BibTex Reference

@article{GgRwJvJcJg13CCPE,
   Author = {Guerrero, G.D. and Wallace, R.M. and Vazquez-Poletti, J.L. and Cecilia, J.M. and Garcia, J.M. and Mozos, D. and Perez-Sanchez, H.},
   Title = {A Performance/Cost Model for a CUDA Drug Discovery Application on Physical and Public Cloud Infrastructures},
   Journal = {Concurrency and Computation: Practice and Experience},
   Volume = {Wiley},
   Number = {10},
   Pages = {1787--1798},
   Year = {2014}
}

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