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基于 CPU-GPU 混合计算平台的 RNA 二级结构预测算法并行化研究

夏飞 朱强华 金国庆

国防科技大学学报Issue(6):138-146,9.
国防科技大学学报Issue(6):138-146,9.

基于 CPU-GPU 混合计算平台的 RNA 二级结构预测算法并行化研究

Accelerating RNA secondary structure prediction applications based on CPU-GPU hybrid platforms

夏飞 1朱强华 1金国庆1

作者信息

  • 1. 海军工程大学 电子工程学院,湖北 武汉 430033
  • 折叠

摘要

Abstract

Prediction of ribonucleic acid (RNA)secondary structure remains to be one of the most important research areas in bioinformatics. The Zuker algorithm is one of the most popular methods of free energy minimization for RNA secondary structure prediction.However,general-purpose computers including parallel computers or multi-core computers exhibit parallel efficiency of no more than 50% on Zuker.For this problem,a CPU-GPU hybrid computing system that accelerates the Zuker algorithm applications for RNA secondary structure prediction is proposed.The computing tasks were allocated between CPU and GPU for parallel cooperate execution.Performance differences between the CPU and the GPU in the task-allocation scheme were considered to obtain workload balance.To improve the hybrid system performance,the Zuker algorithm was optimally implemented with special methods for CPU and GPU architecture.A speedup of 15.93 ×over optimized multi-core SIMD CPU implementation and performance advantage of 16% over optimized GPU implementation were shown in the experimental results.More than 14% of the sequences were executed on CPU in the hybrid system.To the best of our knowledge,our implementation combining CPU and GPU is the only accelerator platform implementing the complete Zuker algorithm.Moreover,the hybrid computing system is proven to be promising and applicable to accelerate other bioinformatics applications.

关键词

生物信息学/RNA二级结构预测/最小自由能/混合加速方法

Key words

bioinformatics/RNA secondary structure prediction/minimal free energy model/hybrid acceleration

分类

信息技术与安全科学

引用本文复制引用

夏飞,朱强华,金国庆..基于 CPU-GPU 混合计算平台的 RNA 二级结构预测算法并行化研究[J].国防科技大学学报,2013,(6):138-146,9.

基金项目

国家自然科学基金资助项目(61202127);国家863计划资助项目 ()

国防科技大学学报

OA北大核心CSCDCSTPCD

1001-2486

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