最长公共子串算法的GPU加速

2023 IEEE 17th International Symposium on Applied Computational Intelligence and Informatics (SACI) Pub Date : 2023-05-23 DOI:10.1109/SACI58269.2023.10158638

Ádám Pintér, S. Szénási

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引用次数: 0

摘要

两个字符串的最长公共子字符串是出现在两个文本中的字符序列，并且是其中最长的。该方法广泛应用于几种文本相似度度量方法中，通常在同一文本数据上多次使用。有几种已知的方法可以解决这个问题，但这些方法大多是基于非常耗费时间和内存的过程。本文提出了一种新的数据并行模型来解决相同的问题，可用于GPU实现。实验结果表明，对于长文本数据，数据并行实现的速度明显加快。

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GPU Acceleration of Longest Common Substrings Algorithm

The Longest Common Substring of two strings is a character sequence that appears in both texts and is the longest of these. The method is widely used in several text similarity measurement methods, usually used multiple times on the same textual data. There are several already known methods to solve the problem, but these are mostly based on very time and memory intensive procedures. This paper presents a novel data-parallel model to solve the same problem, available for GPU implementation. As our experimental results show, the data-parallel implementation is significantly faster for long textual data.

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2023 IEEE 17th International Symposium on Applied Computational Intelligence and Informatics (SACI)

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