PLEDGER: Embedded Whole Genome Read Mapping using Algorithm-HW Co-design and Memory-aware Implementation

Maheshwari, Sidharth and Shafik, Rishad and Wilson, Ian and Yakovlev, Alex and Gudur, Venkateshwarlu Y. and Acharyya, Amit (2021) PLEDGER: Embedded Whole Genome Read Mapping using Algorithm-HW Co-design and Memory-aware Implementation. In: 2021 Design, Automation and Test in Europe Conference and Exhibition, DATE 2021, 1 February 2021through 5 February 2021, Virtual, Online.

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Abstract

With over 6000 known genetic disorders, genomics is a key driver to transform the current generation of healthcare from reactive to personalized, predictive, preventive and participatory (P4) form. High throughput sequencing technologies produce large volumes of genomic data, making genome reassembly and analysis computationally expensive in terms of performance and energy. In this paper, we propose an algorithm-hardware co-design driven acceleration approach for enabling translational genomics. Core to our approach is a Pyopencl based tooL for gEnomic workloaDs tarGeting Embedded platforms (PLEDGER). PLEDGER is a scalable, portable and energy-efficient solution to genomics targeting low-cost embedded platforms. It is a read mapping tool to reassemble genome, which is a crucial prerequisite to genomics. Using bit-vectors and variable level optimisations, we propose a low-memory footprint, dynamic programming based filtration and verification kernel capable of accelerated parallel heterogeneous executions. We demonstrate, for the first time, mapping of real reads to whole human genome on a memory-restricted embedded platform using novel memory-aware preprocessed data structures. We compare the performance and accuracy of PLEDGER with state-of-the-art RazerS3, Hobbes3, CORAL and REPUTE on two systems: 1) Intel i7-8750H CPU + Nvidia GTX 1050 Ti, 2) Odroid N2 with 6 cores: 4xCortex-A73 + 2xCortex-A53 and Mali GPU. PLEDGER demonstrates persistent energy and accuracy advantages compared to state-of-the-art read mappers producing up to 11× speedups and 5.9× energy savings compared to state-of-the-art hardware resources. © 2021 EDAA.

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