YARB: a Methodology to Characterize Regular Expression Matching on Heterogeneous Systems

Abstract

The continuous growth of data pushes novel and efficient approaches for information retrieval. In this context, Regular Expression (RE) matching is widely employed and represents a relevant computational kernel that carries controland memory-related issues. Among the several solutions to relieve these burdens, accelerators seem a promising alternative to general-purpose systems. However, state-of-the-art benchmarking presents a highly fragmented scenario without consensus on the approach and lacks an open-source strategy. Therefore, to fairly characterize existing execution engines, this work presents YARB, an open benchmarking methodology. It builds upon literature solutions, a comprehensive approach, and an in-depth characterization of heterogeneous systems. Moreover, YARB's openness will enable future integrations and engines comparison.

Enabling Efficient Regular Expression Matching at the Edge through Domain-Specific Architectures

“…We evaluated the proposed solution by comparing the average execution time, and the energy efficiency based on a subset of state-of-the-art high-end datacenter RE benchmarks [9], [22]. Specifically, we selected four benchmarks, spacing from security (Snort IDS from Cisco [8], a synthetic benchmark called PowerEN [9]) to other application domains, such as Protomata (bioinformatics) and Brill (NLP), to consolidate our proposed approach across various RE domains.…”

One Automaton to Rule Them All: Beyond Multiple Regular Expressions Execution

“…To fully exploit FSAs executive potential, there exist various techniques fostering the optimization of both NFAs and DFAs that tackle NFAs partitioning [26], multi-stride DFAs [11], [28], [40], and DFAs compression [33], [46]. These optimization approaches involve both architectural [6], [13], [20]- [23], [26], [41], [47] and algorithmic aspects [11], [12], [14], [33].…”