Minimizing speculation overhead in a parallel recognizer for regular texts

TL;DR

This paper introduces RI-DFA, a new automaton type that reduces speculation overhead in parallel regular text recognition, leading to faster multi-core processing with manageable construction costs.

Contribution

The paper presents RI-DFA, a novel automaton that minimizes speculation overhead in parallel recognition without increasing nondeterminism or automaton size, improving efficiency over existing methods.

Findings

RI-DFA reduces the number of starting states compared to traditional DFA.

RI-DFA achieves faster recognition times than NFA-based methods on multi-core architectures.

Construction cost of RI-DFA is moderate and practical.

Abstract

Speculative data-parallel algorithms for language recognition have been widely experimented for various types of finite-state automata (FA), deterministic (DFA) and nondeterministic (NFA), often derived from regular expressions (RE). Such an algorithm cuts the input string into chunks, independently recognizes each chunk in parallel by means of identical FAs, and at last joins the chunk results and checks overall consistency. In chunk recognition, it is necessary to speculatively start the FAs in any state, thus causing an overhead that reduces the speedup compared to a serial algorithm. Existing data-parallel DFA-based recognizers suffer from the excessive number of starting states, and the NFA-based ones suffer from the number of nondeterministic transitions. Our data-parallel algorithm is based on the new FA type called reduced interface DFA (RI-DFA), which minimizes the speculation overhead without incurring in the penalty of nondeterministic transitions or of impractically enlarged DFA machines. The algorithm is proved to be correct and theoretically efficient, because it combines the state-reduction of an NFA with the speed of deterministic transitions, thus improving on both DFA-based and NFA-based existing implementations. The practical applicability of the RI-DFA approach is confirmed by a quantitative comparison of the number of starting states for a large public benchmark of complex FAs. On multi-core computing architectures, the RI-DFA recognizer is much faster than the NFA-based one on all benchmarks, while it matches the DFA-based one on some benchmarks and performs much better on some others. The extra time cost needed to construct an RI-DFA compared to a DFA is moderate and is compatible with a practical use.