基于fpga的收缩阵列加速Nussinov RNA二级结构预测

A. Jacob, J. Buhler, R. Chamberlain
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引用次数: 40

摘要

RNA结构预测或折叠是一项计算密集型任务,是生物信息学中几个搜索应用的核心。我们开始通过使用二维收缩阵列架构加速Nussinov折叠算法来解决高通量RNA折叠的需求。我们采用并行字符串括号的经典结果来为Nussinov算法产生有效的收缩阵列,并详细说明这些阵列设计以产生完全实现的FPGA实现。我们的设计在Xilinx Virtex-II 6000 FPGA上比现代x86 CPU实现了高达39倍的加速。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Accelerating Nussinov RNA secondary structure prediction with systolic arrays on FPGAs
RNA structure prediction, or folding, is a compute-intensive task that lies at the core of several search applications in bioinformatics. We begin to address the need for high-throughput RNA folding by accelerating the Nussinov folding algorithm using a 2D systolic array architecture. We adapt classic results on parallel string parenthesization to produce efficient systolic arrays for the Nussinov algorithm, elaborating these array designs to produce fully realized FPGA implementations. Our designs achieve estimated speedups up to 39times on a Xilinx Virtex-II 6000 FPGA over a modern x86 CPU.
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