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A performance/cost model for a CUDA drug discovery application on physical and public cloud infrastructures
Author(s) -
Guerrero Ginés D.,
Wallace Richard M.,
VázquezPoletti José L.,
Cecilia José M.,
García José M.,
Mozos Daniel,
PérezSánchez Horacio
Publication year - 2014
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.3117
Subject(s) - cuda , cloud computing , computer science , graphics , architecture , virtual screening , massively parallel , drug discovery , distributed computing , parallel computing , operating system , bioinformatics , art , visual arts , biology
SUMMARY 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.Copyright © 2013 John Wiley & Sons, Ltd.