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基于Hadoop框架和GPU架构的局部比对工具。

Local alignment tool based on Hadoop framework and GPU architecture.

作者信息

Hung Che-Lun, Hua Guan-Jie

机构信息

Department of Computer Science and Communication Engineering, Providence University, No. 200, Section 7, Taiwan Boulevard, Shalu District, Taichung 43301, Taiwan.

Department of Computer Science and Information Engineering, Providence University, No. 200, Section 7, Taiwan Boulevard, Shalu District, Taichung 43301, Taiwan.

出版信息

Biomed Res Int. 2014;2014:541490. doi: 10.1155/2014/541490. Epub 2014 May 14.

Abstract

With the rapid growth of next generation sequencing technologies, such as Slex, more and more data have been discovered and published. To analyze such huge data the computational performance is an important issue. Recently, many tools, such as SOAP, have been implemented on Hadoop and GPU parallel computing architectures. BLASTP is an important tool, implemented on GPU architectures, for biologists to compare protein sequences. To deal with the big biology data, it is hard to rely on single GPU. Therefore, we implement a distributed BLASTP by combining Hadoop and multi-GPUs. The experimental results present that the proposed method can improve the performance of BLASTP on single GPU, and also it can achieve high availability and fault tolerance.

摘要

随着诸如Slex等下一代测序技术的迅速发展,越来越多的数据被发现和发表。为了分析如此海量的数据,计算性能是一个重要问题。最近,许多工具,如SOAP,已经在Hadoop和GPU并行计算架构上实现。BLASTP是一种在GPU架构上实现的重要工具,供生物学家用于比较蛋白质序列。为了处理大规模生物学数据,仅依靠单个GPU是困难的。因此,我们通过结合Hadoop和多个GPU实现了分布式BLASTP。实验结果表明,所提出的方法可以提高BLASTP在单个GPU上的性能,并且还能实现高可用性和容错能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7438/4052794/94410741e872/BMRI2014-541490.001.jpg

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