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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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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
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author Yang, Hailong
Li, Bo
Wang, Yongjian
Luan, Zhongzhi
Qian, Depei
Chu, Tianshu
author_facet Yang, Hailong
Li, Bo
Wang, Yongjian
Luan, Zhongzhi
Qian, Depei
Chu, Tianshu
author_sort Yang, Hailong
collection PubMed
description 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.
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spelling pubmed-71206632020-04-06 Accelerating Dock6’s Amber Scoring with Graphic Processing Unit Yang, Hailong Li, Bo Wang, Yongjian Luan, Zhongzhi Qian, Depei Chu, Tianshu Algorithms and Architectures for Parallel Processing Article 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. 2010 /pmc/articles/PMC7120663/ http://dx.doi.org/10.1007/978-3-642-13119-6_35 Text en © Springer-Verlag Berlin Heidelberg 2010 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Yang, Hailong
Li, Bo
Wang, Yongjian
Luan, Zhongzhi
Qian, Depei
Chu, Tianshu
Accelerating Dock6’s Amber Scoring with Graphic Processing Unit
title Accelerating Dock6’s Amber Scoring with Graphic Processing Unit
title_full Accelerating Dock6’s Amber Scoring with Graphic Processing Unit
title_fullStr Accelerating Dock6’s Amber Scoring with Graphic Processing Unit
title_full_unstemmed Accelerating Dock6’s Amber Scoring with Graphic Processing Unit
title_short Accelerating Dock6’s Amber Scoring with Graphic Processing Unit
title_sort accelerating dock6’s amber scoring with graphic processing unit
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7120663/
http://dx.doi.org/10.1007/978-3-642-13119-6_35
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