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Research and implementation of scalable parallel computing based on Map-Reduce

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As a parallel programming model,Map-Reduce is used for distributed computing of massive data.Map-Reduce model encapsulates the details of parallel implementation,fault-tolerant processing,local computing and load balancing,etc.,provides a simple but powerful interface.In case of having no clear idea about distributed and parallel programming,this interface can be utilized to save development time.This paper introduces the method of using Hadoop,the open-source Map-Reduce software platform,to combine PCs to carry out scalable parallel computing.Our experiment using 12 PCs to compute N-body problem based on Map-Reduce model shows that we can get a 9.8x speedup ratio.This work indicates that the Map-Reduce can be applied in scalable parallel computing.

Map-Reducedistributed computingN-body problem

NGUYEN Thanh-cuong、SHEN Wen-feng、CHAI Ya-hui、XU Wei-min

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School of Computer Engineering and Science, Shanghai University, Shanghai 200072, P.R.China

School of Information Engineering, East China Jiaotong University, Nanchang 330013, P.R.China

Shanghai Leading Academic Discipline Project国家高技术研究发展计划(863计划)Major Technology R&D Program of ShanghaiScience and Technology Pillar Project of Jiangxi

J501032009AA01220108DZ5016002010BGB00604

2011

10.1007/s11741-011-0763-3

上海大学学报(英文版)
上海大学

上海大学学报(英文版)

影响因子：0.196
ISSN：1007-6417
年,卷(期)：2011.15(5)
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