Research and implementation of scalable parallel computing based on Map-Reduce |
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| Authors: | NGUYEN Thanh-cuong SHEN Wen-feng CHAI Ya-hui XU Wei-min |
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| Institution: | 1. School of Computer Engineering and Science, Shanghai University, Shanghai 200072, P.R.China
2. 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 |
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| Abstract: | 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. |
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| Keywords: | Map-Reduce distributed computing N-body problem |
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