Asian Journal of Computer Science and Technology (AJCST)
Performance Analysis of Parallelized Bioinformatics Applications
Author : Dhruv Chander Pant and OP GuptaVolume 7 No.2 July-September 2018 pp 70-74
Abstract
The main challenges bioinformatics applications facing today are to manage, analyze and process a huge volume of genome data. This type of analysis and processing is very difficult using general purpose computer systems. So the need of distributed computing, cloud computing and high performance computing in bioinformatics applications arises. Now distributed computers, cloud computers and multi-core processors are available at very low cost to deal with bulk amount of genome data. Along with these technological developments in distributed computing, many efforts are being done by the scientists and bioinformaticians to parallelize and implement the algorithms to take the maximum advantage of the additional computational power. In this paper a few bioinformatics algorithms have been discussed. The parallelized implementations of these algorithms have been explained. The performance of these parallelized algorithms has been also analyzed. It has been also observed that in parallel implementations of the various bioinformatics algorithms, impact of communication subsystems with respect to the job sizes should also be analyzed.
Keywords
Applications, Bioinformatics, High Performance Computing, Parallel Computing
Full Text:
References
[1] D. Jawadat, “Era of Bioinformatics”, in Proceedings of 2nd IEEE international conference on Information and Communication Technologies: From Theory to Applications, pp 18060-1865, 2006.
[2] R. Hughey and K. Karplus, “Bioinformatics: A New Field in Engineering Education” in Proceedings of 31st ASEE/IEEE Frontiers in Education Conference, pp 15-17, 2001.
[3] O.P. Gupta and S. Rani, “Bioinformatics applications and Tools: An Overview”, CiiT- International Journal of Biometrics and bioinformatics, Vol 3, No 3, pp. 107-110, 2010.
[4] I. Gorton, P. Greenfield, A. Sazalay and R. Williams, “Data Intensive Computations in 21st Century”, in Computer Magazine of IEEE Computer Society, Vol. 41, No. 4, pp. 30 -32, 2008.
[5] C. Mueller, M. Dalkilic and A. Lumsdaine, “Implementing Data Parallel algorithms for Bioinformatics”, in proceedings of SIAM Conference on Computational Science and Engineering, pp 226-232, 2005.
[6] K. Hwang and Z. Xu, “Scalable Parallel Computing: Technology, Architecture and Computing”, Mc-GrawHill Series in Computer Engineering, 1998.
[7] T.F. Smith and M.S. Waterman, “Identification of Common Molecular Subsequences”, Journal of Molecular Biology, Vol. 147, No. 1, pp. 195-197, 1981.
[8] Y. Chen, S. Yu and M. Leng, “Parallel Sequence Alignment Algorithms for Clustering System”, International Federation for Information Processing, Vol. 207, pp. 311-321, 2006.
[9] A. Wirawan, K.C. Keong and B. Schmidt, “Parallel DNA Sequence Alignment on Cell Broadband Engine”, Springer – Verlag Berlin Heidelber, pp. 1249– 1256, 2008.
[10] J. Ebedes and A. Datta, “ Multiple Sequence Alignment in Parallel on a Workstation Cluster”, Oxford University Press, Vol. 20, No. 77, pp. 1193-1195,2004.
[11] B.K. Pandey, S.K. Pandey and D. Pandey, “A Survey of Bioinformatics Applications on Parallel Architectures”, International journal of Computer Applications, Vol. 23, No. 4, pp. 21 – 25, 2011.
[12] V. Sachdeva, M. Kistler, E. Speight and T.H.K. Tzeng, “Exploring the Viability of Cell Broadband Engine for Bioinformatics applications”, in Proceedings of IEEE International Parallel and Distributed Processing Symposium, pp. 1-8, 2007.
[13] G. Minervini, G.L. Rocca, P.L. Luisi and F. Polticelli, “High Throughput Protein Structure Prediction in a Grid Environment”, Journal of Bio- Algorithms and Med System, Vol. 3, No. 5, pp. 39-43, 2007.
[14] H. Zhang, B. Schmidt and W.M. Witting, “ Accelerating BLASTP on the Cell Broadband Engine”, in Proceedings of the 3rd International Conference on Pattern Recognition in Bioinformatics, pp. 46 – 47, 2008.
[15] S. Rani and O.P. Gupta, “CLUS_GPU-BLASTP- accelerated protein sequence alignment using GPU- enabled cluster”, Journal of Supercomputing, Vol. 73, No. 10, pp. 4580-4595, 2017.
[16] M. Al-Rajab and J. Lu, “Bioinformatics: an overview for cancer research”, Proc. 13th International Conference on Bioinformatics and computational Biology, the University of Georgia, USA, pp. 123-128, 2012.