Accelerating business analytics applications

IEEE International Symposium on High-Performance Comp Architecture Pub Date : 2012-02-25 DOI:10.1109/HPCA.2012.6169044

V. Salapura, T. Karkhanis, P. Nagpurkar, J. Moreira

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引用次数: 20

Abstract

Business text analytics applications have seen rapid growth, driven by the mining of data for various decision making processes. Regular expression processing is an important component of these applications, consuming as much as 50% of their total execution time. While prior work on accelerating regular expression processing has focused on Network Intrusion Detection Systems, business analytics applications impose different requirements on regular expression processing efficiency. We present an analytical model of accelerators for regular expression processing, which includes memory bus-, I/O bus-, and network-attached accelerators with a focus on business analytics applications. Based on this model, we advocate the use of vector-style processing for regular expressions in business analytics applications, leveraging the SIMD hardware available in many modern processors. In addition, we show how SIMD hardware can be enhanced to improve regular expression processing even further. We demonstrate a realized speedup better than 1.8 for the entire range of data sizes of interest. In comparison, the alternative strategies deliver only marginal improvement for large data sizes, while performing worse than the SIMD solution for small data sizes.

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加速业务分析应用程序

在为各种决策过程挖掘数据的推动下，商业文本分析应用程序出现了快速增长。正则表达式处理是这些应用程序的一个重要组成部分，占用了它们总执行时间的50%。虽然先前在加速正则表达式处理方面的工作主要集中在网络入侵检测系统上，但业务分析应用程序对正则表达式处理效率提出了不同的要求。我们提出了正则表达式处理加速器的分析模型，其中包括内存总线、I/O总线和网络附加加速器，重点关注业务分析应用程序。基于这个模型，我们提倡在业务分析应用程序中对正则表达式使用矢量式处理，利用许多现代处理器中可用的SIMD硬件。此外，我们还展示了如何增强SIMD硬件以进一步改进正则表达式处理。我们演示了在所有感兴趣的数据大小范围内实现的优于1.8的加速。相比之下，对于大数据量，替代策略只提供了边际改进，而对于小数据量，则比SIMD解决方案性能更差。

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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IEEE International Symposium on High-Performance Comp Architecture

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