research-article

Data-parallel finite-state machines

Authors:

Todd Mytkowicz,

Madanlal Musuvathi,

Wolfram SchulteAuthors Info & Claims

ACM SIGPLAN Notices, Volume 49, Issue 4

Pages 529 - 542

https://doi.org/10.1145/2644865.2541988

Published: 24 February 2014 Publication History

Abstract

A finite-state machine (FSM) is an important abstraction for solving several problems, including regular-expression matching, tokenizing text, and Huffman decoding. FSM computations typically involve data-dependent iterations with unpredictable memory-access patterns making them difficult to parallelize. This paper describes a parallel algorithm for FSMs that breaks dependences across iterations by efficiently enumerating transitions from all possible states on each input symbol. This allows the algorithm to utilize various sources of data parallelism available on modern hardware, including vector instructions and multiple processors/cores. For instance, on benchmarks from three FSM applications: regular expressions, Huffman decoding, and HTML tokenization, the parallel algorithm achieves up to a 3x speedup over optimized sequential baselines on a single core, and linear speedups up to 21x on 8 cores.

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Cited By

Nourian MNguyen TChien ABecchi M(2022)Data Transformation Acceleration using Deterministic Finite-State Transducers2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020756(141-150)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10020756
Ren BBalakrishna SJo YKrishnamoorthy SAgrawal KKulkarni M(2019)Extracting SIMD Parallelism from Recursive Task-Parallel ProgramsACM Transactions on Parallel Computing10.1145/33656636:4(1-37)Online publication date: 26-Dec-2019
https://doi.org/10.1145/3365663
Hierons RTürker U(2017)Parallel Algorithms for Generating Distinguishing Sequences for Observable Non-deterministic FSMsACM Transactions on Software Engineering and Methodology10.1145/305112126:1(1-34)Online publication date: 12-Jul-2017
https://dl.acm.org/doi/10.1145/3051121
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Data-parallel finite-state machines

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Published In

cover image ACM SIGPLAN Notices

ACM SIGPLAN Notices Volume 49, Issue 4

ASPLOS '14

April 2014

729 pages

ISSN:0362-1340

EISSN:1558-1160

DOI:10.1145/2644865

Editors:
Mark W. Bailey
Hamilton College, Clinton, NY
,
Rajeev Balasubramonian
University of Utah
,
Al Davis
University of Utah
,
Sarita Adve
University of Illinois at Urbana-Champ

Issue’s Table of Contents

ASPLOS '14: Proceedings of the 19th international conference on Architectural support for programming languages and operating systems
February 2014
780 pages
ISBN:9781450323055
DOI:10.1145/2541940
General Chairs:
Rajeev Balasubramonian
University of Utah
,
Al Davis
University of Utah
,
Program Chair:
Sarita Adve
University of Illinois at Urbana-Champ

Copyright © 2014 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 February 2014

Published in SIGPLAN Volume 49, Issue 4

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Cited By

Nourian MNguyen TChien ABecchi M(2022)Data Transformation Acceleration using Deterministic Finite-State Transducers2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020756(141-150)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10020756
Ren BBalakrishna SJo YKrishnamoorthy SAgrawal KKulkarni M(2019)Extracting SIMD Parallelism from Recursive Task-Parallel ProgramsACM Transactions on Parallel Computing10.1145/33656636:4(1-37)Online publication date: 26-Dec-2019
https://doi.org/10.1145/3365663
Hierons RTürker U(2017)Parallel Algorithms for Generating Distinguishing Sequences for Observable Non-deterministic FSMsACM Transactions on Software Engineering and Methodology10.1145/305112126:1(1-34)Online publication date: 12-Jul-2017
https://dl.acm.org/doi/10.1145/3051121
D’Antoni LVeanes M(2017)The Power of Symbolic Automata and TransducersComputer Aided Verification10.1007/978-3-319-63387-9_3(47-67)Online publication date: 13-Jul-2017
https://doi.org/10.1007/978-3-319-63387-9_3
Chantrapornchai CChoksuchat C(2016)Improving Cytogenetic Search with GPUs Using Different String Matching SchemesAdvanced Data Mining and Applications10.1007/978-3-319-49586-6_13(188-202)Online publication date: 12-Dec-2016
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Ge TZhang TLiu HTsafrir DMUSUVATHI MGupta RAbu-Ghazaleh N(2024)ngAP: Non-blocking Large-scale Automata Processing on GPUsProceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 110.1145/3617232.3624848(268-285)Online publication date: 27-Apr-2024
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Cicolini LCarloni FSantambrogio MConficconi DGrosser TDubach CSteuwer MXue JOttoni GQuintão Pereira F(2024)One Automaton to Rule Them All: Beyond Multiple Regular Expressions ExecutionProceedings of the 2024 IEEE/ACM International Symposium on Code Generation and Optimization10.1109/CGO57630.2024.10444810(193-206)Online publication date: 2-Mar-2024
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Liu HPai SJog A(2023)Asynchronous Automata Processing on GPUsProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/35794537:1(1-27)Online publication date: 2-Mar-2023
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