基于并行GPU实现KNN的文本分类新方法

2013 Fourth International Conference on Networking and Distributed Computing Pub Date : 2013-12-21 DOI:10.1109/ICNDC.2013.20

Lican Huang, Zhilong Li

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引用次数: 4

摘要

当网站有大量的网页或其他文章时，自动文本分类是有用的。k -最近邻(KNN)是一种对文本文档的域进行分类的方法。文本分类的性能取决于许多因素，但KNN过程贡献了大部分的计算负荷。我们提出了一种新的并行GPU实现KNN的方法，与CPU实现相比，它的速度提高了40倍。

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A Novel Method of Parallel GPU Implementation of KNN Used in Text Classification

Automatic text classification is useful when websites have huge volume of web pages or other articles. K-Nearest Neighbour (KNN) is a way to classify the domains of text documents. The performance of text classification depends on lots of factors but KNN process contributes most of computational loads. We present a novel method of parallel GPU implementation of KNN with speed-ups of 40 times compared with CPU implementation.

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2013 Fourth International Conference on Networking and Distributed Computing

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