| 引用本文: | 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,2013,35(6):138-146.[点击复制] |
| XIA Fei,ZHU Qianghua,JIN Guoqing.Accelerating RNA secondary structure prediction applications based on CPU-GPU hybrid platforms[J].Journal of National University of Defense Technology,2013,35(6):138-146[点击复制] |
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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 |
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(海军工程大学 电子工程学院， 湖北 武汉 430033)
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| 摘要: |
| RNA二级结构预测是生物信息学领域重要的研究方向，基于最小自由能模型的Zuker算法是目前该领域最典型使用最广泛的算法之一。本文基于CPU+GPU的混合计算平台实现了对Zuker算法的并行和加速。根据CPU和GPU计算性能的差异，通过合理的任务分配策略，实现二者之间的并行协作计算和处理单元间的负载平衡；针对CPU和GPU的不同硬件特性，对Zuker算法在CPU和GPU上的实现分别采取了不同的并行优化方法，提高了混合加速系统的计算性能。实验结果表明，CPU处理单元在混合系统中承担了14%以上的计算任务，与传统的多核CPU并行方案相比，采用混合并行加速方法可获得15.93的全局加速比；与最优的单纯GPU加速方案相比，可获得16%的性能提升，并且该混合计算方案可用于对其它生物信息学序列分析应用的并行和加速。 |
| 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 |
| DOI： |
| 投稿日期：2013-01-29 |
| 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（2008AA01A201） |
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| Accelerating RNA secondary structure prediction applications based on CPU-GPU hybrid platforms |
| XIA Fei, ZHU Qianghua, JIN Guoqing |
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(Electronic Engineering College, Naval University of Engineering, Wuhan 430033, China)
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| 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. |
| Keywords: bioinformatics RNA secondary structure prediction minimal free energy model hybrid acceleration |
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