Revisiting Multiple Pattern Matching Algorithms for Multi-Core Architecture

Abstract

Due to the huge size of patterns to be searched, multiple pattern searching remains a challenge to several newly-arising applications like network intrusion detection. In this paper, we present an attempt to design efficient multiple pattern searching algorithms on multi-core architectures. We observe an important feature which indicates that the multiple pattern matching time mainly depends on the number and minimal length of patterns. The multi-core algorithm proposed in this paper leverages this feature to decompose pattern set so that the parallel execution time is minimized. We formulate the problem as an optimal decomposition and scheduling of a pattern set, then propose a heuristic algorithm, which takes advantage of dynamic programming and greedy algorithmic techniques, to solve the optimization problem. Experimental results suggest that our decomposition approach can increase the searching speed by more than 200% on a 4-core AMD Barcelona system.

An Efficient Security System for Mobile Data Monitoring

“…The optimal partition is finding the shortest-path and consolidated subsets. Tan et al [2] challenge Liu's optimal partition. They regarded processors as a factor of division and proved the optimal allocation of subsets to processors is NP-hard.…”

“…We leverage the idea of Tan et al [2] to develop a parallel multiple pattern matching algorithm. AEA algorithm, instead of Greedy algorithm, is adopted to schedule subsets and this algorithm can jump out of local optimization and avoid premature.…”

“…In order to overcome the high performance barrier, multicore architectures provide an opportunity to achieve high performance at a relatively low cost. For each core of a conventional multicore with the traditional processors, it is favourable to adapt coarse-gained parallelism [2]. Previous research focused on the partitions of pattern set and mapping the subsets on parallel processors (cores); therefore, the problem is transformed into a scheduling problem.…”

A Parallel Algorithm of Multiple String Matching Based on Set-Partition in Multi-core Architecture

“…The paper [29] maintains that the speed of string matching algorithm mainly depends on the number and minimal length of patterns. They proposed a heuristic algorithm using dynamic programming and the greedy algorithm techniques, to divide patterns set and choose an optimal string matching algorithm for them.…”

Multiple String Matching on a GPU using CUDAs

“…Similar parallelization efforts for multiple string matching have been presented for multi-core and cluster platforms using different programming frameworks (i.e. POSIX threads, OpenMP and MPI) [4,21,33,37]. Most of these research studies have focused on parallelization of the Aho-Corasick and Wu-Manber algorithms.…”