A Speculative Parallel DFA Membership Test for Multicore, SIMD and Cloud Computing Environments

TL;DR

This paper introduces a failure-free speculative parallel algorithm for DFA membership testing that efficiently exploits SIMD and cloud architectures, maintaining correctness and avoiding speed-downs.

Contribution

It presents a novel, failure-free speculative parallel DFA matching method that is fully vectorized and load-balanced for heterogeneous architectures, including cloud environments.

Findings

Achieved efficient parallel DFA matching on SIMD and multicore architectures.

Maintained sequential semantics without speed-downs.

Demonstrated scalability on cloud computing platforms.

Abstract

We present techniques to parallelize membership tests for Deterministic Finite Automata (DFAs). Our method searches arbitrary regular expressions by matching multiple bytes in parallel using speculation. We partition the input string into chunks, match chunks in parallel, and combine the matching results. Our parallel matching algorithm exploits structural DFA properties to minimize the speculative overhead. Unlike previous approaches, our speculation is failure-free, i.e., (1) sequential semantics are maintained, and (2) speed-downs are avoided altogether. On architectures with a SIMD gather-operation for indexed memory loads, our matching operation is fully vectorized. The proposed load-balancing scheme uses an off-line profiling step to determine the matching capacity of each par- ticipating processor. Based on matching capacities, DFA matches are load-balanced on inhomogeneous parallel architectures such as cloud computing environments. We evaluated our speculative DFA membership test for a representative set of benchmarks from the Perl-compatible Regular Expression (PCRE) library and the PROSITE protein database. Evaluation was conducted on a 4 CPU (40 cores) shared-memory node of the Intel Manycore Testing Lab (Intel MTL), on the Intel AVX2 SDE simulator for 8-way fully vectorized SIMD execution, and on a 20-node (288 cores) cluster on the Amazon EC2 computing cloud.