Accelerating pattern matching using a novel parallel algorithm on gpus

Cheng Hung Lin, Chen Hsiung Liu, Lung Sheng Chien, Shih Chieh Chang

研究成果: 雜誌貢獻期刊論文同行評審

59 引文 斯高帕斯(Scopus)

摘要

Graphics processing units (GPUs) have attracted a lot of attention due to their cost-effective and enormous power for massive data parallel computing. In this paper, we propose a novel parallel algorithm for exact pattern matching on GPUs. A traditional exact pattern matching algorithm matches multiple patterns simultaneously by traversing a special state machine called an Aho-Corasick machine. Considering the particular parallel architecture of GPUs, in this paper, we first propose an efficient state machine on which we perform very efficient parallel algorithms. Also, several techniques are introduced to do optimization on GPUs, including reducing global memory transactions of input buffer, reducing latency of transition table lookup, eliminating output table accesses, avoiding bank-conflict of shared memory, coalescing writes to global memory, and enhancing data transmission via peripheral component interconnect express. We evaluate the performance of the proposed algorithm using attack patterns from Snort V2.8 and input streams from DEFCON. The experimental results show that the proposed algorithm performed on NVIDIA GPUs achieves up to 143.16-Gbps throughput, 14.74 times faster than the Aho-Corasick algorithm implemented on a 3.06-GHz quad-core CPU with the OpenMP. The library of the proposed algorithm is publically accessible through Google Code.

原文英語
文章編號12
頁(從 - 到)1906-1916
頁數11
期刊IEEE Transactions on Computers
62
發行號10
DOIs
出版狀態已發佈 - 2013

ASJC Scopus subject areas

  • 軟體
  • 理論電腦科學
  • 硬體和架構
  • 計算機理論與數學

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