title: GPU acceleration of Dock6's Amber scoring computation.
abstract: Dressing the problem of virtual screening is a long-term goal in the drug discovery field, which if properly solved, can significantly shorten new drugs' R&D cycle. The scoring functionality that evaluates the fitness of the docking result is one of the major challenges in virtual screening. In general, scoring functionality in docking requires a large amount of floating-point calculations, which usually takes several weeks or even months to be finished. This time-consuming procedure is unacceptable, especially when highly fatal and infectious virus arises such as SARS and H1N1, which forces the scoring task to be done in a limited time. This paper presents how to leverage the computational power of GPU to accelerate Dock6's (http://dock.compbio.ucsf.edu/DOCK_6/) Amber (J. Comput. Chem. 25: 1157-1174, 2004) scoring with NVIDIA CUDA (NVIDIA Corporation Technical Staff, Compute Unified Device Architecture - Programming Guide, NVIDIA Corporation, 2008) (Compute Unified Device Architecture) 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 experiments show that the GPU-accelerated Amber scoring achieves a 6.5× speedup with respect to the original version running on AMD dual-core CPU for the same problem size. This acceleration makes the Amber scoring more competitive and efficient for large-scale virtual screening problems.
authors.affiliation: Department of Computer Science and Engineering, Sino-German Joint Software Institute, Beihang University, 100191 Beijing, China. hailong.yang@jsi.buaa.edu.cn
authors.forename: Hailong
authors.initials: H
authors.lastname: Yang
authors.forename: Qiongqiong
authors.initials: Q
authors.lastname: Zhou
authors.forename: Bo
authors.initials: B
authors.lastname: Li
authors.forename: Yongjian
authors.initials: Y
authors.lastname: Wang
authors.forename: Zhongzhi
authors.initials: Z
authors.lastname: Luan
authors.forename: Depei
authors.initials: D
authors.lastname: Qian
authors.forename: Hanlu
authors.initials: H
authors.lastname: Li
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D003198:Computer Simulation; D055808:Drug Discovery / Q000706:statistics & numerical data*; D004353:Drug Evaluation, Preclinical / Q000706:statistics & numerical data*; D006801:Humans; D066298:In Vitro Techniques; D008024:Ligands; D056004:Molecular Dynamics Simulation / Q000706:statistics & numerical data; D012984:Software; D012985:Software Design; D014584:User-Computer Interface
chemical_list: D008024:Ligands