短读映射的可重构加速

James Arram, K. H. Tsoi, W. Luk, P. Jiang
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引用次数: 40

摘要

下一代DNA测序机的吞吐量最近有所提高,这对分析产生的大量数据提出了巨大的计算挑战。本文提出了一种基于可重构计算技术的新方法,用于加速短读映射,其中数百万个短读的位置相对于已知参考序列进行定位。我们的方法由两个关键组件组成:用于大部分对齐过程的精确字符串匹配器,以及用于其余情况的近似字符串匹配器。我们描述了设计空间中有趣的区域,包括同构的、异构的和运行时可重构的设计,并提供了相应性能的粗略估计。我们表明,针对单个FPGA的这种架构的特定实现可以比Intel X5650 CPU上的BWA快293倍,比NVIDIA GTX 580 GPU上的SOAP3快134倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reconfigurable Acceleration of Short Read Mapping
Recent improvements in the throughput of nextgeneration DNA sequencing machines poses a great computational challenge in analysing the massive quantities of data produced. This paper proposes a novel approach, based on reconfigurable computing technology, for accelerating short read mapping, where the positions of millions of short reads are located relative to a known reference sequence. Our approach consists of two key components: an exact string matcher for the bulk of the alignment process, and an approximate string matcher for the remaining cases. We characterise interesting regions of the design space, including homogeneous, heterogeneous and run-time reconfigurable designs and provide back of envelope estimations of the corresponding performance. We show that a particular implementation of this architecture targeting a single FPGA can be up to 293 times faster than BWA on an Intel X5650 CPU, and 134 times faster than SOAP3 on an NVIDIA GTX 580 GPU.
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