An Efficient Algorithm for Parallel Computation of Rough Entropy using CUDA

被引：0

作者：

Jing, Si-Yuan ^{[1
]}

Liu, Cai-Ming ^{[1
]}

Li, Gong-Liang ^{[2
]}

Yan, Gao-Rong ^{[3
]}

Zhang, Yan ^{[1
]}

机构：

[1] Leshan Normal Univ, Sch Comp Sci, Leshan, Peoples R China

[2] China Acad Engn Phys, Inst Comp Applicat, Mianyang, Peoples R China

[3] Leshan Nonnal Univ, Sch Foreign Language, Leshan, Peoples R China

来源：

2017 13TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY (CIS) | 2017年

关键词：

Rough set theory; Rough Entropy; Parallel Computing; Data Mining; CUDA;

D O I：

10.1109/CIS.2017.00009

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Continuously improving computation efficiency of rough entropy is very meaningful, because it is helpful to apply rough sets to some fields with high performance requirement. Recently, Graphics Processing Unit (GPU) has gained a lot of attention from scientific communities for its applicability in high performance computing. This paper proposes an efficient algorithm which is based on sorting technique to accelerate the computation of rough entropy using CUDA. The proposed algorithm is compared with a sorting-based serial algorithm. Experimental results prove the effectiveness of the proposed algorithm.

引用

页码：1 / 5

页数：5

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