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Accelerating Dock6’s Amber Scoring with Graphic Processing Unit

In the drug discovery field, solving the problem of virtual screening is a long term-goal. The scoring functionality which evaluates the fitness of the docking result is one of the major challenges in virtual screening. In general, scoring functionality in docking requires large amount of floating-p...

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Detalles Bibliográficos
Autores principales: Yang, Hailong, Li, Bo, Wang, Yongjian, Luan, Zhongzhi, Qian, Depei, Chu, Tianshu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7120663/
http://dx.doi.org/10.1007/978-3-642-13119-6_35
Descripción
Sumario:In the drug discovery field, solving the problem of virtual screening is a long term-goal. The scoring functionality which evaluates the fitness of the docking result is one of the major challenges in virtual screening. In general, scoring functionality in docking requires large amount of floating-point calculations and usually takes several weeks or even months to be finished. This time-consuming disadvantage is unacceptable especially when highly fatal and infectious virus arises such as SARS and H1N1. This paper presents how to leverage the computational power of GPU to accelerate Dock6 [1]’s Amber [2] scoring with NVIDIA CUDA [3] platform. We also discuss many factors that will greatly influence the performance after porting the Amber scoring to GPU, including thread management, data transfer and divergence hidden. Our GPU implementation shows a 6.5x speedup with respect to the original version running on AMD dual-core CPU for the same problem size.