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ReneGENE-DP: Accelerated Parallel Dynamic Programming for Genome Informatics

Natarajan, Santhi and KrishnaKumar, N and Pavan, M and Pal, Debnath and Nandy, SK (2018) ReneGENE-DP: Accelerated Parallel Dynamic Programming for Genome Informatics. In: IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), MAR 16-17, 2018, Bangalore, INDIA.

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Official URL: http://dx.doi.org/10.1109/CONECCT.2018.8482378

Abstract

Parsing a very long genomic string (human genome is typically 3 billion characters long) abstracts the whole complexity of biocomputing. Approximate String Matching (ASM) is the most eligible computing paradigm that captures the biological complexity of the genome, integrating various sources of biological information into tractable probabilistic models. Though computationally complex, the Dynamic Programming (DP) methodology proves to be very efficient for ASM, in discriminating substantial similarities amongst severe noise in genetic data presented by evolution. Though a significant amount of computations in the DP algorithms are accelerated on multiple platforms, the less complex traceback step is still performed in the host, presenting significant memory and Input/Output bottleneck. With billions of such alignments required to analyse the genomic big data from the Next Generation Sequencing (NGS) Platforms, this bottleneck can severely affect system performance. This paper presents ReneGENE-DP, our implementations of the DP computations on hardware accelerators, with the novelty of realizing traceback in hardware in parallel with the forward scan during analysis, on both FPGA and GPU. The fastest FPGA implementation is around 43.63x better than the fastest GPU implementation of ReneGENE-DP, which in turn, is 380.85x faster than the reference design, which is a GPU based DP algorithm with traceback on host.

Item Type: Conference Proceedings
Additional Information: Copy right for this article belong to IEEE
Department/Centre: Division of Interdisciplinary Research > Computational and Data Sciences
Depositing User: Id for Latest eprints
Date Deposited: 16 Nov 2018 15:33
Last Modified: 16 Nov 2018 15:33
URI: http://eprints.iisc.ac.in/id/eprint/61089

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