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CORAL: Verification-aware OpenCL based Read Mapper for Heterogeneous Systems

Lookup NU author(s): Sidharth Maheshwari, Venkateshwarlu Gudur, Dr Rishad Shafik, Dr Ian Wilson, Professor Alex Yakovlev

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This is the authors' accepted manuscript of an article that has been published in its final definitive form by Institute of Electrical and Electronics Engineers, 2019.

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Abstract

Genomics has the potential to transform medicine from reactive to a personalized, predictive, preventive and participatory (P4) form. The computational costs of genomics have become a daunting challenge. Most modern computing systems are heterogeneous consisting of combinations of CPUs, GPUs and FPGAs. They require platform-specific software and languages to program making their simultaneous operation challenging. Existing read mappers and analysis tools in the WGS pipeline do not scale for such heterogeneity. Additionally, the computational cost of mapping reads is high due to expensive dynamic programming based verification, where optimized implementations are already available. Thus, improvement in filtration techniques is needed to reduce verification overhead. To address the aforementioned limitations with regards to the mapping element of the WGS pipeline, we propose a Cross-platfOrm Read mApper using opencL (CORAL). CORAL is capable of executing on heterogeneous devices/platforms simultaneously. It can reduce computational time by suitably distributing the workload without any additional programming effort. We showcase this on a quadcore Intel CPU along with two Nvidia GTX 590 GPUs, distributing the workload judiciously to achieve up to 2× speedup compared to when only CPUs are used. To reduce the verification overhead, CORAL dynamically adapts k-mer length during filtration. We demonstrate competitive timings against other mappers using real and simulated read. CORAL is available at: https://github.com/nclaes/CORAL.


Publication metadata

Author(s): Maheshwari S, Gudur VY, Shafik R, Wilson I, Yakovlev A, Acharyya A

Publication type: Article

Publication status: Published

Journal: IEEE/ACM Transactions On Computational Biology And Bioinformatics

Year: 2019

Volume: Sept

Pages: ePub ahead of print

Online publication date: 26/09/2019

Acceptance date: 02/04/2016

Date deposited: 10/01/2020

ISSN (electronic): 1557-9964

Publisher: Institute of Electrical and Electronics Engineers

URL: https://doi.org/10.1109/tcbb.2019.2943856

DOI: 10.1109/tcbb.2019.2943856


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