A High Performance Parallel Ranking SVM with OpenCL on Multi-core and Many-core Platforms

IF 0.6 Q4 COMPUTER SCIENCE, THEORY & METHODS

International Journal of Grid and High Performance Computing Pub Date : 2019-01-01 DOI:10.4018/IJGHPC.2019010102

Huming Zhu, Peidao Li, P. Zhang, Zheng Luo

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引用次数: 5

Abstract

A ranking support vector machine (RSVM) is a typical pairwise method of learning to rank, which is effective in ranking problems. However, the training speed of RSVMs are not satisfactory, especially when solving large-scale data ranking problems. Recent years, many-core processing units (graphics processing unit (GPU), Many Integrated Core (MIC)) and multi-core processing units have exhibited huge superiority in the parallel computing domain. With the support of hardware, parallel programming develops rapidly. Open Computing Language (OpenCL) and Open Multi-Processing (OpenMP) are two of popular parallel programming interfaces. The authors present two high-performance parallel implementations of RSVM, an OpenCL version implemented on multi-core and many-core platforms, and an OpenMP version implemented on multi-core platform. The experimental results show that the OpenCL version parallel RSVM achieved considerable speedup on Intel MIC 7110P, NVIDIA Tesla K20M and Intel Xeon E5-2692v2, and it also shows good portability.

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基于OpenCL的多核和多核平台上的高性能并行排序支持向量机

排序支持向量机(RSVM)是一种典型的两两学习排序方法，在排序问题中非常有效。然而，rsvm的训练速度并不令人满意，特别是在解决大规模数据排序问题时。近年来，多核处理单元(图形处理单元(GPU)、多集成核(MIC))和多核处理单元在并行计算领域显示出巨大的优势。在硬件的支持下，并行编程得到了迅速的发展。开放计算语言(OpenCL)和开放多处理(OpenMP)是两种流行的并行编程接口。作者提出了两种RSVM的高性能并行实现，一种是在多核和多核平台上实现的OpenCL版本，一种是在多核平台上实现的OpenMP版本。实验结果表明，OpenCL版本并行RSVM在Intel MIC 7110P、NVIDIA Tesla K20M和Intel Xeon E5-2692v2上取得了相当大的加速，并表现出良好的可移植性。

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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来源期刊

International Journal of Grid and High Performance Computing COMPUTER SCIENCE, THEORY & METHODS-

CiteScore

1.70

自引率

10.00%

发文量