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Local Alignment Tool Based on Hadoop Framework and GPU Architecture
Author(s) -
CheLun Hung,
Guan-Jie Hua
Publication year - 2014
Publication title -
biomed research international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.772
H-Index - 126
eISSN - 2314-6141
pISSN - 2314-6133
DOI - 10.1155/2014/541490
Subject(s) - computer science , big data , fault tolerance , architecture , parallel computing , cuda , general purpose computing on graphics processing units , cloud computing , distributed computing , data mining , operating system , graphics , art , visual arts
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.

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