ReneGENE-GI: Empowering precision genomics with FPGAs on HPCs

Natarajan, S and KrishnaKumar, N and Pal, D and Nandy, SK (2018) ReneGENE-GI: Empowering precision genomics with FPGAs on HPCs. In: 14th International Symposium on Applied Reconfigurable Computing, ARC 2018, 2 - 4 May 2018, Santorini, pp. 178-191.

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Abstract

Genome Informatics (GI) serves to be a holistic and inter-disciplinary approach in understanding genomic big data from a computational perspective. In another decade, the omics data production rate is expected to be approaching one zettabase per year, at very low cost. There is dire need to bridge the gap between the capabilities of Next Generation Sequencing (NGS) technology in churning out omics big data and our computational capabilities in omics data management, processing, analytics and interpretation. The High Performance Computing platforms seem to be the choice for bio-computing, offering high degrees of parallelism and scalability, while accelerating the multi-stage GI computational pipeline. Amidst such high computing power, it is the choice of algorithms and implementations in the entirety of the GI pipeline that decides the precision of bio-computing in revealing biologically relevant information. Through this paper, we present ReneGENE-GI, an innovatively engineered GI pipeline. We also present the performance analysis of ReneGENE-GI’s Comparative Genomics Module (CGM), prototyped on a reconfigurable bio-computing accelerator platform. Alignment time for this prototype is about one-tenth the time taken by the single GPU OpenCL implementation of ReneGENE-GI’s CGM, which itself is 2.62x faster than CUSHAW2-GPU (the GPU CUDA implementation of CUSHAW). With the single-GPU implementation demonstrating a speed up of 150+ x over standard heuristic aligners in the market like BFAST, the reconfigurable accelerator version of ReneGENE-GI’s CGM is several orders faster than the competitors, offering precision over heuristics.

Item Type:	Conference Paper
Publication:	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Series.:	Lecture Notes in Computer Science
Publisher:	Springer Verlag
Additional Information:	The copyright for this article belongs to Springer Verlag
Keywords:	Data handling; Field programmable gate arrays (FPGA); Genes; Graphics processing unit; Information management; Pipelines; Reconfigurable architectures, Comparative genomics; Computational capability; Computing power; Genome informatics; GPU implementation; High performance computing; Next-generation sequencing; Performance analysis, Big data
Department/Centre:	Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited:	24 Aug 2022 04:59
Last Modified:	24 Aug 2022 04:59
URI:	https://eprints.iisc.ac.in/id/eprint/76079

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