Algorithms and Architectures for Parallel Processing: 14th by Xian-he Sun, Wenyu Qu, Ivan Stojmenovic, Wanlei Zhou,

By Xian-he Sun, Wenyu Qu, Ivan Stojmenovic, Wanlei Zhou, Zhiyang Li, Hua Guo, Geyong Min, Tingting Yang, Yulei Wu, Lei Liu (eds.)

This quantity set LNCS 8630 and 8631 constitutes the complaints of the 14th foreign convention on Algorithms and Architectures for Parallel Processing, ICA3PP 2014, held in Dalian, China, in August 2014. The 70 revised papers provided within the volumes have been chosen from 285 submissions. the 1st quantity contains chosen papers of the most convention and papers of the first foreign Workshop on rising issues in instant and cellular Computing, ETWMC 2014, the fifth overseas Workshop on clever conversation Networks, IntelNet 2014, and the fifth overseas Workshop on instant Networks and Multimedia, WNM 2014. the second one quantity includes chosen papers of the most convention and papers of the Workshop on Computing, verbal exchange and regulate applied sciences in clever Transportation procedure, 3C in ITS 2014, and the Workshop on safeguard and privateness in computing device and community structures, SPCNS 2014.

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Additional resources for Algorithms and Architectures for Parallel Processing: 14th International Conference, ICA3PP 2014, Dalian, China, August 24-27, 2014. Proceedings, Part II

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684–689 (2011) 12. : Clustering Very Large Multi-dimensional Datasets with MapReduce. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 690–698 (2011) 13. : HaLoop: Efficient Iterative Data Processing on Large Clusters. VLDB Endow 3(1-2), 285–296 (2010) 14. : Twister: A Runtime for Iterative MapReduce. In: Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing, pp. 810–818 (2010) 15. : Scalable KMeans++.

However in our case, the NEI analysis must follow the continuously moving particles, not the ‘fixed’ points on the underlying Eulerian grid. So the paper proposed an in situ scheme for running the NEI analysis on dedicated nodes near the simulation cluster.

The results for WordCount and Distributed Grep are shown in Fig. 6(a) and 6(b), respectively. The maximal size of text file is 12 GB in our experiments. Distributed Grep application has a different MapReduce pattern compared with WordCount application. For WordCount application, the Reduce stage is complex, and takes more time than the Map stage. For Distributed Grep application, the Reduce stage is very simple, and it just collects and sums up the intermediate results. As you can see from these two figures, as the increase of input text file size, job completion time also increases.

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