A case study of tuning MapReduce for efficient Bioinformatics in the cloud

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• 作者：Lizhen Shi ; ^a ; ^{lshi@cs.fsu.edu} ; Zhong Wang^b ; ^{zhongwang@lbl.gov} ; Weikuan Yu^a ; ^{yuw@cs.fsu.edu} ; Xiandong Meng^b ; ^{xiandongmeng@lbl.gov}
• 关键词：Hadoop ; YARN ; Parameter optimization ; K-mer counting ; NGS
• 刊名：Parallel Computing
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：61
• 期：Complete
• 页码：83-95
• 全文大小：1044 K
• 卷排序：61

文摘

Extracting about twenty key parameters, which could lead to significant performance benefits, from the YARN and MapReduce parameter space, and classifing them into four groups: CPU relevant, memory relevant, disk relevant, and network relevant parameters. Emphasizing the significance of the application characteristics associated with the near-optimal configuration and exploring the unique characteristics of applications in the domain of bioinformatics. Documenting an exemplary case for tuning MapReduce-based bioinformatics applications in the cloud.