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AccuRA: Accurate Alignment of Short Reads on Scalable Reconfigurable Accelerators

Natarajan, Santhi and Kumar, N Krishna and Pal, Dehnath and Nandy, S K (2016) AccuRA: Accurate Alignment of Short Reads on Scalable Reconfigurable Accelerators. In: International Conference on Embedded Computer Systems - Architectures, Modeling and Simulation (SAMOS), JUL 17-21, 2016, Samos, GREECE, pp. 79-87.

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Official URL: http://dx.doi.org/ 10.1109/SAMOS.2016.7818334


Classified as a big data problem, Short Read Mapping (SRM) within the Next Generation Sequencing (NGS) pipeline presents profound technical and computing challenges. Existing solutions handle the high volume of data leveraging heuristics, to claim notable performance standards. The results from SRM leave a huge impact across fields, including medical diagnostics and drug discovery. In this context, we need precise, affordable, reliable and actionable results from SRM, to support any application, with uncompromised accuracy and performance. Here, we present AccuRA, a massively parallel, scalable, high performance reconfigurable accelerator for accurate alignment of short reads. Supplemented with multithreaded firmware architecture, AccuRA precisely aligns short reads, at a fine-grained single nucleotide resolution, and offers full coverage of the genome. AccuRA's parallel dynamic programming kernels seamlessly perform traceback process in hardware simultaneously along with forward scan, thus achieving SRM in the minimum possible and deterministic time. The AccuRA prototype, hosting eight kernel units on a single reconfigurable device, aligns short reads with an alignment performance of 20.48 Giga Cell Updates Per Second (GCUPs). AccuRA also scales well at multiple levels of design granularity, while successfully aligning genomes of various sizes, ranging from small archeal, bacterial, fungal genomes, to the large mammalian human genome.

Item Type: Conference Proceedings
Publisher: IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Additional Information: Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 20 May 2017 05:51
Last Modified: 06 Nov 2018 12:11
URI: http://eprints.iisc.ac.in/id/eprint/56921

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