Parallel protein secondary structure prediction schemes using Pthread and OpenMP over hyper-threading technology

Authors:
Wei Zhong;Gulsah Altun;Xinmin Tian;Robert Harrison;Phang C. Tai;Yi Pan
Affiliations:
Division of Math and Computer Science, University of South Carolina Upstate, Spartanburg, USA 29303;Department of Computer Science, Georgia State University, Atlanta, USA 30303;Software Solution Group, Intel Corporation, Santa Clara, USA;Department of Computer Science, Georgia State University, Atlanta, USA 30303 and Department of Biology, Georgia State University, Atlanta, USA 30303;Department of Biology, Georgia State University, Atlanta, USA 30303;Department of Computer Science, Georgia State University, Atlanta, USA 30303
Venue:
The Journal of Supercomputing
Year:
2007

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Abstract

Protein secondary structure prediction has a fundamental influence on today's bioinformatics research. In this work, tertiary classifiers for the protein secondary structure prediction are implemented on Denoeux Belief Neural Network (DBNN) architecture. Hydrophobicity matrix, orthogonal matrix, BLOSUM62 matrix and PSSM matrix are experimented separately as the encoding schemes for DBNN. Hydrophobicity matrix, BLOSUM62 matrix and PSSM matrix are applied to DBNN architecture for the first time. The experimental results contribute to the design of new encoding schemes. Our accuracy of the tertiary classifier with PSSM encoding scheme reaches 72.01%, which is almost 10% better than the previous results obtained in 2003. Due to the time consuming task of training the neural networks, Pthread and OpenMP are employed to parallelize DBNN in the Hyper-Threading enabled Intel architecture. Speedup for 16 Pthreads is 4.9 and speedup for 16 OpenMP threads is 4 in the 4 processors shared memory architecture. Both speedup performance of OpenMP and Pthread is superior to that of other research. With the new parallel training algorithm, thousands of amino acids can be processed in reasonable amount of time. Our research also shows that Hyper-Threading technology for Intel architecture is efficient for parallel biological algorithms.