CAJ | 학술논문

应用GPU通用高性能编程技术实现了一种加速BLAST算法的新方法.BLAST是目前最常用的用于生物序列查询比对的算法和软件包,其处理速度受到串行化执行和磁盘I/O等因素的影响.本文通过实验分析了BLAST软件包中的典型程序BLASTN的运行热点,并选定关键热点模块,应用CUDA编程技术对其进行并行化改造.对比实验结果表明,对于平均序列长度较大的序列库,应用GPGPU并行化可明显缩短该模块的运行时间,获得超过35倍的加速比.这说明,我们可以利用GPGPU对BLAST进行并行化加速,以满足高性能生物序列查询的需求.
응용GPU통용고성능편정기술실현료일충가속BLAST산법적신방법.BLAST시목전최상용적용우생물서렬사순비대적산법화연건포,기처리속도수도천행화집행화자반I/O등인소적영향.본문통과실험분석료BLAST연건포중적전형정서BLASTN적운행열점,병선정관건열점모괴,응용CUDA편정기술대기진행병행화개조.대비실험결과표명,대우평균서렬장도교대적서렬고,응용GPGPU병행화가명현축단해모괴적운행시간,획득초과35배적가속비.저설명,아문가이이용GPGPU대BLAST진행병행화가속,이만족고성능생물서렬사순적수구.
In this article we present a novel approach to accelerating the BLAST algorithm by using the GPGPU technol-ogy. BLAST is the most widely used algorithm and software package for biological sequence search. It is, however, limited by serial process and heavy disk I/O operation. We analyze BLASTN, a typical BLAST tool, identify the most important hotspot of BLASTN, and use CUDA to reprogram the hotspot process. Our test results show that more than 35 times speedup has been achieved in the GPU BLASTN as compared to the CPU counterpart, which demonstrates the significance of parallelizing the BLAST algorithm using the GPGPU technology.