About: Accelerating Dock6’s Amber Scoring with Graphic Processing Unit

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Accelerating Dock6’s Amber Scoring with Graphic Processing Unit
Creator	Li, Bo Luan, Zhongzhi Qian, Depei Wang, Yongjian Yang, Hailong Chu, Tianshu
Source	PMC
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.
has issue date	2010-01-01(xsd:dateTime)
bibo:doi	10.1007/978-3-642-13119-6_35
has license	no-cc
sha1sum (hex)	2845f2b1890e91ebbd60c63f6d2ae2571dc65ca8
schema:url	https://doi.org/10.1007/978-3-642-13119-6_35
resource representing a document's title	Accelerating Dock6’s Amber Scoring with Graphic Processing Unit
has PubMed Central identifier	PMC7120663
schema:publication	Algorithms and Architectures for Parallel Processing
resource representing a document's body	covid:2845f2b1890e91ebbd60c63f6d2ae2571dc65ca8#body_text
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