About: GPU Acceleration of Dock6’s Amber Scoring Computation

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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	GPU Acceleration of Dock6’s Amber Scoring Computation
Creator	Li, Bo Zhou, Qiongqiong Yang, H Li, Hanlu Luan, Zhongzhi Qian, Depei Wang, Yongjian Yang, Hailong
Source	PMC
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.
has issue date	2010-04-27(xsd:dateTime)
bibo:doi	10.1007/978-1-4419-5913-3_56
bibo:pmid	20865535
has license	no-cc
sha1sum (hex)	8c0dfbaca357a68276ef52b576a4b9896b7db2c7
schema:url	https://doi.org/10.1007/978-1-4419-5913-3_56
resource representing a document's title	GPU Acceleration of Dock6’s Amber Scoring Computation
has PubMed Central identifier	PMC7123368
has PubMed identifier	20865535
schema:publication	Advances in Computational Biology
resource representing a document's body	covid:8c0dfbaca357a68276ef52b576a4b9896b7db2c7#body_text
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