Big data genome sequencing on Zynq based clusters (abstract only)

Abstract

Next-generation sequencing (NGS) problems have attracted many attentions of researchers in biological and medical computing domains. The current state-of-the-art NGS computing machines are dramatically lowering the cost and increasing the throughput of DNA sequencing. In this paper, we propose a practical study that uses Xilinx Zynq board to summarize acceleration engines using FPGA accelerators and ARM processors for the state-of-the-art short read mapping approaches. The heterogeneous processors and accelerators are coupled with each other using a general Hadoop distributed processing framework. First the reads are collected by the central server, and then distributed to multiple accelerators on the Zynq for hardware acceleration. Therefore, the combination of hardware acceleration and Map-Reduce execution flow could greatly accelerate the task of aligning short length reads to a known reference genome. Our approach is based on preprocessing the reference genomes and iterative jobs for aligning the continuous incoming reads. The hardware acceleration is based on the creditable read-mapping algorithm RMAP software approach. Furthermore, the speedup analysis on a Hadoop cluster, which concludes 8 development boards, is evaluated. Experimental results demonstrate that our proposed architecture and methods has the speedup of more than 112X, and is scalable with the number of accelerators. Finally, the Zynq based cluster has efficient potential to accelerate even general large scale big data applications. This work was supported by the NSFC grants No. 61379040, No. 61272131 and No. 61202053.