Support Vector Machines on GPU with Sparse Matrix Format

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI:10.1109/ICMLA.2010.53

Tsung-Kai Lin, Shao-Yi Chien

引用次数: 37

Abstract

Emerging general-purpose Graphics Processing Unit (GPU) provides a multi-core platform for wide applications, including machine learning algorithms. In this paper, we proposed several techniques to accelerate Support Vector Machines (SVM) on GPUs. Sparse matrix format is introduced into parallel SVM to achieve better performance. Experimental results show that the speedup of 55x–133.8x over LIBSVM can be achieved in training process on NVIDIA GeForce GTX470.

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稀疏矩阵格式的GPU支持向量机

新兴的通用图形处理单元(GPU)为广泛的应用提供了多核平台，包括机器学习算法。本文提出了几种在gpu上加速支持向量机(SVM)的技术。在并行支持向量机中引入稀疏矩阵格式以获得更好的性能。实验结果表明，在NVIDIA GeForce GTX470上，该算法的训练速度比LIBSVM提高了55x - 133.8倍。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2010 Ninth International Conference on Machine Learning and Applications

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