alexa A Parallelized Binary Search Tree
ISSN: 2165- 7866

Journal of Information Technology & Software Engineering
Open Access

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Research Article

A Parallelized Binary Search Tree

Jian Feng1, Daniel Q. Naiman2 and Bret Cooper3*

1Snoqualmie, WA 98065

22epartment of Applied Mathematics and Statistics, The Johns Hopkins University, Baltimore, MD 21218, USA

3Soybean Genomics and Improvement Laboratory, USDA-ARS, Beltsville, MD 20705, USA

*Corresponding Author:
Bret Cooper
Soybean Genomics and Improvement Laboratory
USDA-ARS, Beltsville, MD 20705, USA
E-mail: [email protected]

Received Date: November 03, 2011; Accepted Date: November 17, 2011; Published Date: November 19, 2011

Citation: Feng J, Naiman DQ, Cooper B (2011) A Parallelized Binary Search Tree. J Inform Tech Soft Engg 1:103. doi:10.4172/2165-7866.1000103

Copyright: © 2011 Feng J, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

Abstract

PTTRNFNDR is an unsupervised statistical learning algorithm that detects patterns in DNA sequences, protein sequences, or any natural language texts that can be decomposed into letters of a finite alphabet. PTTRNFNDR performs complex mathematical computations, and its processing time increases when input texts become large. To achieve better speed performance, several strategies were applied in the implementation of the program, including parallel operations of binary search trees. A standard binary search tree is not thread-safe due to its dynamic insertions and deletions. Here, we adjusted the standard binary search tree for parallelized operations to achieve improved performance of the PTTRNFNDR algorithm. The method can be applied to other software platforms to quicken data searching through parallel operations of binary search trees when several conditions are met.

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