Alphabet-dependent Parallel Algorithm for Suffix Tree Construction for Pattern Searching

TL;DR

This paper introduces an alphabet-dependent parallel algorithm for suffix tree construction, significantly improving speed for biological sequence analysis by leveraging multicore architectures, with up to 15x acceleration over sequential methods.

Contribution

The paper presents a novel parallel suffix tree construction algorithm optimized for biological data, exploiting alphabet dependence and multicore systems for enhanced efficiency.

Findings

Achieved up to 15x speedup over sequential algorithms

Effective for large biological sequences like DNA and proteins

Demonstrated efficiency gains on multicore architectures

Abstract

Suffix trees have recently become very successful data structures in handling large data sequences such as DNA or Protein sequences. Consequently parallel architectures have become ubiquitous. We present a novel alphabet-dependent parallel algorithm which attempts to take advantage of the perverseness of the multicore architecture. Microsatellites are important for their biological relevance hence our algorithm is based on time efficient construction for identification of such. We experimentally achieved up to 15x speedup over the sequential algorithm on different input sizes of biological sequences.