SCALABLE PARALLEL IMPLEMENTATION OF INDEPENDENT COMPONENTS ANALYSIS ON THE GRAPHICS PROCESSING UNIT

被引：0

作者：

Forgette, Jacquelyne ^{[1
]}

Smolikova, Renata Wachowiak ^{[2
]}

Wachowiak, Mark ^{[2
]}

机构：

[1] Univ Western Ontario, Dept Comp Sci, London, ON, Canada

[2] Nipissing Univ, Dept Math & Comp Sci, North Bay, ON, Canada

来源：

2011 24TH CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (CCECE) | 2011年

关键词：

Graphics processing units; parallel computing; independent component analysis; signal processing;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Independent component analysis (ICA) is an important signal processing technique used to extract source signals from signal mixtures. Although useful in a wide range of problems, ICA is computationally expensive, and is therefore not suitable in many real-time or large data size applications. This paper presents a scalable parallel implementation of ICA in which computations are performed on graphics processing units (GPUs). An implementation using the programming toolkit OpenCL, as well as local memory and memory coalescing optimizations, increase ICA efficiency, and potentially improve its utility in data-intensive applications.

引用

页码：912 / 916

页数：5

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