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Data Mining for High Performance Computing

Data Mining for High Performance Computing
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Author(s): Shen Lu (Soft Challenge LLC, USA)
Copyright: 2015
Pages: 19
Source title: Research and Applications in Global Supercomputing
Source Author(s)/Editor(s): Richard S. Segall (Arkansas State University, USA), Jeffrey S. Cook (Independent Researcher, USA) and Qingyu Zhang (Shenzhen University, China)
DOI: 10.4018/978-1-4666-7461-5.ch014


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With the development of information technology, the size of the dataset becomes larger and larger. Distributed data processing can be used to solve the problem of data analysis on large datasets. It partitions the dataset into a large number of subsets and uses different processors to store, manage, broadcast, and synchronize the data analysis. However, distributed computing gives rise to new problems such as the impracticality of global communication, global synchronization, dynamic topology changes of the network, on-the-fly data updates, the needs to share resources with other applications, frequent failures, and recovery of resource. In this chapter, the concepts of distributed computing are introduced, the latest research are presented, the advantage and disadvantage of different technologies and systems are analyzed, and the future trends of the distributed computing are summarized.

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