Performance and energy efficiency evaluation of heterogeneous systems for bioinformatics

Authors

  • Enzo Rucci School of Computer Science, National University of La Plata, Argentina

Abstract

Bioinformatics is one of the areas affected by current HPC problems due to the exponential growth of biological data in the last years and the increasing number of bioinformatics applications demanding HPC to meet performance requirements. One of these applications is sequence alignment, which is considered to be fundamental procedure in biological sciences. The alignment process compares two or more biological sequences and its purpose is to identify regions of similarity among them. The Smith-Waterman (SW) algorithm is a popular method for local sequence alignment that has been used as the basis for many subsequent algorithms, and is often employed as a benchmark when comparing different alignment techniques. However, due to the quadratic computational complexity of Smith-Waterman algorithm, several heuristics are used in practice that reduce the execution time but at the expense of not guaranteeing to discover the optimal local alignments. In order to process the ever increasing quantity of biological data with acceptable response times, it is necessary to develop new computational tools that are capable of accelerating key primitives and fundamental algorithms in an efficient manner from performance and energy consumption points of view. For that reason, this thesis considered, as general objective, evaluating performance and energy efficiency of HPC systems for accelerating Smith-Waterman biological sequence alignment.

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References

[1] M. Giles and I. Reguly, “Trends in high-performance computing for engineering calculations”, Philosophical Transactions of the Royal Society A, vol. 372, no. 2022, p. 20130319, 2014.
[2] M. Vestias and H. Neto, “Trends of CPU, GPU and FPGA for high-performance computing,” in 24th
International Conference on Field Programmable Logic and Applications (FPL), 2014, Sept 2014, pp. 1–6.
[3] B. Schmidt, “Bioinformatics: High Performance Parallel Computer Architectures”, B. Schmidt, Ed. CRC Press, 2010.
[4] T. F. Smith and M. S. Waterman, “Identification of common molecular subsequences,” Journal of Molecular Biology, vol. 147, no. 1, pp. 195–197, March 1981.
[5] T. Rognes, “Faster Smith-Waterman database searches with inter-sequence SIMD parallelization”, BMC
Bioinformatics, vol. 12:221, 2011.
[6] Y. Liu and B. Schmidt, “SWAPHI: Smith-Waterman protein database search on Xeon Phi coprocessors,” in 25th IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP 2014), 2014.
[7] L. Wang, Y. Chan, X. Duan, H. Lan, X. Meng, and W. Liu. (2014) “XSW 2.0: A fast Smith-Waterman Algorithm Implementation on Intel Xeon Phi Coprocessors”. Available at: http://sdu-hpcl.github.io/XSW/

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Published

2016-11-01

How to Cite

Rucci, E. (2016). Performance and energy efficiency evaluation of heterogeneous systems for bioinformatics. Journal of Computer Science and Technology, 16(02), p. 104–105. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/493

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