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Scheduling Large-Scale DNA Sequencing Applications

Scheduling Large-Scale DNA Sequencing Applications

Sudha Gunturu, Xiaolin Li, Laurence Tianruo Yang
Copyright: © 2010 |Pages: 17
ISBN13: 9781605666617|ISBN10: 1605666610|EISBN13: 9781605666624
DOI: 10.4018/978-1-60566-661-7.ch036
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MLA

Gunturu, Sudha, et al. "Scheduling Large-Scale DNA Sequencing Applications." Handbook of Research on Scalable Computing Technologies, edited by Kuan-Ching Li, et al., IGI Global, 2010, pp. 841-857. https://doi.org/10.4018/978-1-60566-661-7.ch036

APA

Gunturu, S., Li, X., & Yang, L. T. (2010). Scheduling Large-Scale DNA Sequencing Applications. In K. Li, C. Hsu, L. Yang, J. Dongarra, & H. Zima (Eds.), Handbook of Research on Scalable Computing Technologies (pp. 841-857). IGI Global. https://doi.org/10.4018/978-1-60566-661-7.ch036

Chicago

Gunturu, Sudha, Xiaolin Li, and Laurence Tianruo Yang. "Scheduling Large-Scale DNA Sequencing Applications." In Handbook of Research on Scalable Computing Technologies, edited by Kuan-Ching Li, et al., 841-857. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-661-7.ch036

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Abstract

This chapter studies a load scheduling strategy with near-optimal processing time that is designed to explore the computational characteristics of DNA sequence alignment algorithms, specifically, the Needleman-Wunsch Algorithm. Following the divisible load scheduling theory, an efficient load scheduling strategy is designed in large-scale networks so that the overall processing time of the sequencing tasks is minimized. In this study, the load distribution depends on the length of the sequence and number of processors in the network and, the total processing time is also affected by communication link speed. Several cases have been considered in the study by varying the sequences, communication and computation speeds, and number of processors. Through simulation and numerical analysis, this study demonstrates that for a constant sequence length as the numbers of processors increase in the network the processing time for the job decreases and minimum overall processing time is achieved.

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