Plex: Scaling Parallel Lexing with Backtrack-Free Prescanning
Le Li, Shigeyuki Sato, Qiheng Liu, Kenjiro Taura · 2021
Lexical analysis, which converts input text into a list of tokens, plays an important role in many applications, including compilation and data extraction from texts. To recognize token patterns, a lexer incorporates a sequential computation model - automaton as its basic building component. As such, it is considered difficult to parallelize due to the inherent data dependency. Much work has been done to accelerate lexical analysis through parallel techniques. Unfortunately, existing attempts mainly rely on language-specific remedies for input segmentation, which makes it not only tricky for language extension, but also challenging for automatic lexer generation. This paper presents Plex - an automated tool for generating parallel lexers from user-defined grammars. To overcome the inherent sequentiality, Plex applies a fast prescanning phase to collect context information prior to scanning. To reduce the overheads brought by prescanning, Plex adopts a special automaton, which is derived from that of the scanner, to avoid backtracking behavior and exploits data-parallel techniques. The evaluation under several languages shows that the prescanning overhead is small, and consequently Plex is scalable and achieves 9.8-11.5X speedups using 18 threads.