A performance/cost model for a CUDA drug discovery application on physical and public cloud infrastructures. (14th August 2013)

• Record Type: Journal Article
• Authors: Guerrero, Ginés D.; Wallace, Richard M.; Vázquez‐Poletti, José L.; Cecilia, José M.; García, José M.; Mozos, Daniel; Pérez‐Sánchez, Horacio; Merelli, Ivan; Pérez‐Sánchez, Horacio; Gesing, Sandra
• Is Part Of: Concurrency and computation. Volume 26:Number 10(2014:Jul.)
• Page Start: 1787
• Page End: 1798
• Publication Date: 2013-08-14
• Subjects: Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.3117
• Languages: English
• ISSNs: 1532-0626

Abstract

<abstract abstract-type="main" id="cpe3117-abs-0001"> <title>SUMMARY</title> <p>Virtual Screening (VS) methods can considerably aid drug discovery research, predicting how ligands interact with drug targets. <italic>BINDSURF</italic> 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.Copyright © 2013 John Wiley &amp; Sons, Ltd.</p> </abstract>