Pith. Sign in

RECORD

String Matching with Multicore CPUs: Performing Better with the Aho-Corasick Algorithm

PaperPDF from arXiv

data files

Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
X Bluesky LinkedIn Reddit Hacker News
S. Arudchutha, T. Nishanthy, R. G. Ragel

Distributed, Parallel, and Cluster Computing · Submitted 6 Mar 2014

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Through our implementation and evaluation of results, we prove that our method performs better compared to the state of the art.

how it is stated · asserted 1.00 read the record →

Comment (0) Read on arXiv ↗
Cite ▾
arXiv ID1403.1305 (version 1)
Pith ID6D33UTKH, Pith's permanent record of this paper and its review
CitationR. G. Ragel, S. Arudchutha, T. Nishanthy. "String Matching with Multicore CPUs: Performing Better with the Aho-Corasick Algorithm." arXiv:1403.1305 (2014). https://pith.science/paper/6D33UTKH
Claim paper

The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Open the compiled record

Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 6,057 input tokens, $0.0003 reported.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.05 · human participants 0.03 · controlled comparison 0.50 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.92 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Multiple string matching is known as locating all the occurrences of a given number of patterns in an arbitrary string. It is used in bio-computing applications where the algorithms are commonly used for retrieval of information such as sequence analysis and gene/protein identification. Extremely large amount of data in the form of strings has to be processed in such bio-computing applications. Therefore, improving the performance of multiple string matching algorithms is always desirable. Multicore architectures are capable of providing better performance by parallelizing the multiple string matching algorithms. The Aho-Corasick algorithm is the one that is commonly used in exact multiple string matching algorithms. The focus of this paper is the acceleration of Aho-Corasick algorithm through a multicore CPU based software implementation. Through our implementation and evaluation of results, we prove that our method performs better compared to the state of the art.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Multiple string matching is known as locating all the occurrences of a given number of patterns in an arbitrary string. background 1.00 0.04 0.01 asserted 1.00
s1 It is used in bio-computing applications where the algorithms are commonly used for retrieval of information such as sequence analysis and gene/protein identification. background 1.00 0.04 0.02 asserted 1.00
s2 Extremely large amount of data in the form of strings has to be processed in such bio-computing applications. background 1.00 0.05 0.01 asserted 1.00
s3 Therefore, improving the performance of multiple string matching algorithms is always desirable. background 0.96 0.04 0.03 asserted 0.84
s4 Multicore architectures are capable of providing better performance by parallelizing the multiple string matching algorithms. background 0.99 0.06 0.04 hedged possibility 0.61
s5 The Aho-Corasick algorithm is the one that is commonly used in exact multiple string matching algorithms. background 1.00 0.05 0.02 asserted 1.00
s6 The focus of this paper is the acceleration of Aho-Corasick algorithm through a multicore CPU based software implementation. contribution 0.98 0.40 0.06 asserted 0.90
s7 Through our implementation and evaluation of results, we prove that our method performs better compared to the state of the art. result 0.96 0.96 0.95 asserted 1.00

What kind of paper the classifier reports this is: method or tool 0.96 · evidence from observation or experiment 0.71.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Multiple string matching is known as locating all the occurrences of a given number of patterns in an arbitrary string. It is used in bio-computing applications where the algorithms are commonly used for retrieval of information such as sequence analysis and gene/protein identification. Extremely large amount of data in the form of strings has to be processed in such bio-computing applications. Therefore, improving the performance of multiple string matching algorithms is always desirable. Multicore architectures are capable of providing better performance by parallelizing the multiple string matching algorithms. The Aho-Corasick algorithm is the one that is commonly used in exact multiple string matching algorithms. The focus of this paper is the acceleration of Aho-Corasick algorithm through a multicore CPU based software implementation. Through our implementation and evaluation of results, we prove that our method performs better compared to the state of the art.

Human review

How Pith checks papers → · Full audit of this paper →

Discussion

Know something about this paper? Leave a comment, or highlight any passage above to comment on it.