Simultaneous Finite Automata: An Efficient Data-Parallel Model for Regular Expression Matching

Sin'ya, Ryoma; Matsuzaki, Kiminori; Sassa, Masataka

doi:10.1109/icpp.2013.31

Cited by 18 publications

(10 citation statements)

References 17 publications

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“…For a small number of states, the transitive closure under multiplication can be precomputed as a set of lookup tables. This is similar to the approach used by simultaneous finite automata [56] but with the addition of an output tape.…”

Section: Spatial Query Processingmentioning

confidence: 97%

At-Gis

Ogden

Thomas

Pietzuch

2016

Proceedings of the 2016 International Conference on Management of Data

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Users in many domains, including urban planning, transportation, and environmental science want to execute analytical queries over continuously updated spatial datasets. Current solutions for largescale spatial query processing either rely on extensions to RDBMS, which entails expensive loading and indexing phases when the data changes, or distributed map/reduce frameworks, running on resource-hungry compute clusters. Both solutions struggle with the sequential bottleneck of parsing complex, hierarchical spatial data formats, which frequently dominates query execution time. Our goal is to fully exploit the parallelism offered by modern multicore CPUs for parsing and query execution, thus providing the performance of a cluster with the resources of a single machine.We describe AT-GIS, a highly-parallel spatial query processing system that scales linearly to a large number of CPU cores. AT-GIS integrates the parsing and querying of spatial data using a new computational abstraction called associative transducers (ATs). ATs can form a single data-parallel pipeline for computation without requiring the spatial input data to be split into logically independent blocks. Using ATs, AT-GIS can execute, in parallel, spatial query operators on the raw input data in multiple formats, without any pre-processing. On a single 64-core machine, AT-GIS provides 3× the performance of an 8-node Hadoop cluster with 192 cores for containment queries, and 10× for aggregation queries.

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Section: Spatial Query Processingmentioning

confidence: 97%

At-Gis

Ogden

Thomas

Pietzuch

2016

Proceedings of the 2016 International Conference on Management of Data

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“…1c shows a sequential matching routine. Simultaneous Deterministic Finite Automata: Due to the fundamental reason that the overhead of speculation is inevitable for parallel FA matching, SFA is introduced [12]. SFA is a model of efficient parallelization for FA matching, generated by the original FA.…”

Section: Introductionmentioning

confidence: 99%

“…Algorithm 1 denotes the construction algorithm to develop SFAs. Details of the algorithm can be found in [12]. Fig.…”

Section: Introductionmentioning

confidence: 99%

Parallel Construction of Simultaneous Deterministic Finite Automata on Shared-Memory Multicores

Jung

Park

Blieberger

et al. 2017

2017 46th International Conference on Parallel Processing (ICPP)

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The ubiquity of multicore architectures in modern computing devices requires the adaptation of algorithms to utilize parallel resources. String pattern matching based on finite automata (FAs) is a method that has gained widespread use across many areas in computer science. However, the structural properties of the pattern matching algorithm have hampered its parallelization. To overcome the dependencyconstraint between subsequent matching steps and exploit parallelism, simultaneous deterministic finite automata (SFAs) have been recently introduced. Given an FA A with n states, the corresponding SFA S(A) simulates n parallel instances of FA A. However, although an SFA facilitates parallel FA matching, SFA construction itself is limited by the exponential state-growth problem, which may result in O(n n ) SFA states for an FA of size n. The substantial space requirements incur proportional processing steps, both of which make sequential SFA construction intractable for all but the smallest problem sizes.In this paper, we propose several optimizations for the SFA construction algorithm, which greatly reduce the in-memory footprint and the processing steps required to construct an SFA. We introduce fingerprints as a space-and time-efficient way to represent SFA states. To compute fingerprints, we apply the Barrett reduction algorithm and accelerate it using recent additions to the x86 instruction set architecture. We exploit fingerprints to introduce hashing for further optimizations. Our parallel SFA construction algorithm is nonblocking and utilizes instruction-level, data-level, and task-level parallelism of coarse-, medium-and fine-grained granularity. We adapt static workload distributions and align the SFA data-structures with the constraints of multicore memory hierarchies, to increase the locality of memory accesses and facilitate HW prefetching.We conduct experiments on the PROSITE protein database for FAs of up to 702 FA states to evaluate performance and effectiveness of our proposed optimizations. Evaluations have been conducted on a 4 CPU (64 cores) AMD Opteron 6378 system and a 2 CPU (28 cores, 2 hyperthreads per core) Intel Xeon E5-2697 v3 system. The observed speedups over the sequential baseline algorithm are up to 118541x on the AMD system and 2113968x on the Intel system.

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“…delaying as much as possible the construction of the states. Clearly, algorithm [47] is not intended as a lexer to be invoked by a parallel parser, but as a self-standing processor for matching regular expressions -yet partially so, since it does not address the central issue of ambiguous regular expression parsing, which fortunately does not concern our intended applications. Recently, [48] has experimented on the Cell Processor a parallel version of the Aho-Korasick string matching algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…Some authors have considered the regular expression matching problem, instead of the lexing problem, and, although regular expressions and DFA models are equivalent, the parameters that dominate the experiments may widely differ in the two cases. An example suffices: [47] presents a notable new version of the mentioned [30] approach. They claim that for certain practical regular expressions that are used in network intrusion/detection systems, the size of the parallel lexer remains manageable and not bigger than the square of the minimal DFA.…”

Section: Related Workmentioning

confidence: 99%

Parallel parsing made practical

Barenghi

Reghizzi

Mandrioli

et al. 2015

Science of Computer Programming

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The property of local parsability allows to parse inputs through inspecting only a bounded-length string around the current token. This in turn enables the construction of a scalable, data-parallel parsing algorithm, which is presented in this work. Such an algorithm is easily amenable to be automatically generated via a parser generator tool, which was realized, and is also presented in the following. Furthermore, to complete the framework of a parallel input analysis, a parallel scanner can also combined with the parser. To prove the practicality of a parallel lexing and parsing approach, we report the results of the adaptation of JSON and Lua to a form fit for parallel parsing (i.e. an operator-precedence grammar) through simple grammar changes and scanning transformations. The approach is validated with performance figures from both high performance and embedded multicore platforms, obtained analyzing real-world inputs as a test-bench. The results show that our approach matches or dominates the performances of production-grade LR parsers in sequential execution, and achieves significant speedups and good scaling on multicore machines. The work is concluded by a broad and critical survey of the past work on parallel parsing and future directions on the integration with semantic analysis and incremental parsing.

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Simultaneous Finite Automata: An Efficient Data-Parallel Model for Regular Expression Matching

Cited by 18 publications

References 17 publications

At-Gis

At-Gis

Parallel Construction of Simultaneous Deterministic Finite Automata on Shared-Memory Multicores

Parallel parsing made practical

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