Parallel Longest Common SubSequence Analysis In Chapel

TL;DR

This paper presents a parallel implementation of the Longest Common Subsequence algorithm using Chapel, integrated into Arkouda, enabling efficient large-scale string analysis on high-performance computing resources.

Contribution

It introduces a Chapel-based parallel LCS algorithm integrated into Arkouda, facilitating large-scale string analysis with improved performance on HPC systems.

Findings

Performance improves with more parallel resources.

Longer input strings increase computation time.

Chapel integration enables scalable string analysis.

Abstract

One of the most critical problems in the field of string algorithms is the longest common subsequence problem (LCS). The problem is NP-hard for an arbitrary number of strings but can be solved in polynomial time for a fixed number of strings. In this paper, we select a typical parallel LCS algorithm and integrate it into our large-scale string analysis algorithm library to support different types of large string analysis. Specifically, we take advantage of the high-level parallel language, Chapel, to integrate Lu and Liu's parallel LCS algorithm into Arkouda, an open-source framework. Through Arkouda, data scientists can easily handle large string analytics on the back-end high-performance computing resources from the front-end Python interface. The Chapel-enabled parallel LCS algorithm can identify the longest common subsequences of two strings, and experimental results are given to show how the number of parallel resources and the length of input strings can affect the algorithm's performance.