Accelerating dock6's amber scoring with graphic processing unit

Authors:
Hailong Yang;Bo Li;Yongjian Wang;Zhongzhi Luan;Depei Qian;Tianshu Chu
Affiliations:
Department of Computer Science and Engineering, Sino-German Joint Software Institute, Beihang University, Beijing, China;Department of Computer Science and Engineering, Sino-German Joint Software Institute, Beihang University, Beijing, China;Department of Computer Science and Engineering, Sino-German Joint Software Institute, Beihang University, Beijing, China;Department of Computer Science and Engineering, Sino-German Joint Software Institute, Beihang University, Beijing, China;Department of Computer Science and Engineering, Sino-German Joint Software Institute, Beihang University, Beijing, China;Department of Computer Science and Engineering, Sino-German Joint Software Institute, Beihang University, Beijing, China
Venue:
ICA3PP'10 Proceedings of the 10th international conference on Algorithms and Architectures for Parallel Processing - Volume Part I
Year:
2010

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Abstract

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.