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Asynchronous Automata Processing on GPUs

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Adwait JogAuthors Info & Claims

Proceedings of the ACM on Measurement and Analysis of Computing Systems, Volume 7, Issue 1

Article No.: 27, Pages 1 - 27

https://doi.org/10.1145/3579453

Published: 02 March 2023 Publication History

Abstract

Finite-state automata serve as compute kernels for many application domains such as pattern matching and data analytics. Existing approaches on GPUs exploit three levels of parallelism in automata processing tasks: 1)~input stream level, 2)~automaton-level and 3)~state-level. Among these, only state-level parallelism is intrinsic to automata while the other two levels of parallelism depend on the number of automata and input streams to be processed. As GPU resources increase, a parallelism-limited automata processing task can underutilize GPU compute resources.

To this end, we propose AsyncAP, a low-overhead approach that optimizes for both scalability and throughput. Our insight is that most automata processing tasks have an additional source of parallelism originating from the input symbols which has not been leveraged before. Making the matching process associated with the automata tasks asynchronous, i.e., parallel GPU threads start processing an input stream from different input locations instead of processing it serially, improves throughput significantly and scales with input length.

When the task does not have enough parallelism to utilize all the GPU cores, detailed evaluation across 12 evaluated applications shows that AsyncAP achieves up to 58× speedup on average over the state-of-the-art GPU automata processing engine. When the tasks have enough parallelism to utilize GPU cores, AsyncAP still achieves 2.4× speedup.

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Le Glaunec AKong LMamouras K(2024)HybridSA: GPU Acceleration of Multi-pattern Regex Matching using Bit ParallelismProceedings of the ACM on Programming Languages10.1145/36897718:OOPSLA2(1699-1728)Online publication date: 8-Oct-2024
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Asynchronous Automata Processing on GPUs

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cover image Proceedings of the ACM on Measurement and Analysis of Computing Systems

Proceedings of the ACM on Measurement and Analysis of Computing Systems Volume 7, Issue 1

POMACS

March 2023

749 pages

EISSN:2476-1249

DOI:10.1145/3586099

Editors:
Augustin Chaintreau
Columbia University
,
Leana Golubchik
University of Southern California, United States
,
Zhi-Li Zhang
University of Minnesota, United States

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Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

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Published: 02 March 2023

Published in POMACS Volume 7, Issue 1

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Cited By

Le Glaunec AKong LMamouras K(2024)HybridSA: GPU Acceleration of Multi-pattern Regex Matching using Bit ParallelismProceedings of the ACM on Programming Languages10.1145/36897718:OOPSLA2(1699-1728)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3689771
Li CVerbrugge C(2024)Regular Expressions on Modern GPGPUsProceedings of the 16th Workshop on General Purpose Processing Using GPU10.1145/3649411.3649416(26-32)Online publication date: 2-Mar-2024
https://dl.acm.org/doi/10.1145/3649411.3649416
Lee CHiguchi TUcar SKaneko NAltintas OOguchi KOkoshi TKo JLiKamWa R(2024)Poster: Performance Analysis of TCP CUBIC and BBR over V2V Wi-FiProceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services10.1145/3643832.3661413(668-669)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3643832.3661413
Jaliminche LChakraborttii CChoi CLitz H(2023)Enabling Multi-tenancy on SSDs with Accurate IO Interference ModelingProceedings of the 2023 ACM Symposium on Cloud Computing10.1145/3620678.3624657(216-232)Online publication date: 30-Oct-2023
https://dl.acm.org/doi/10.1145/3620678.3624657
Liu HPai SJog A(2023)Asynchronous Automata Processing on GPUsACM SIGMETRICS Performance Evaluation Review10.1145/3606376.359352451:1(23-24)Online publication date: 27-Jun-2023
https://dl.acm.org/doi/10.1145/3606376.3593524

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