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

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

ISBN-10: 3319111930

ISBN-13: 9783319111933

ISBN-10: 3319111949

ISBN-13: 9783319111940

This quantity set LNCS 8630 and 8631 constitutes the lawsuits of the 14th overseas 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 includes 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 foreign Workshop on clever communique 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, communique and keep an eye on applied sciences in clever Transportation process, 3C in ITS 2014, and the Workshop on safety and privateness in computing device and community structures, SPCNS 2014.

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Additional info 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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The server receives and stores all Rweight value, and all nodes are then sorted by their Rweight value. A smaller value of Rweight means a higher node priority, and therefore more jobs should be assigned to it, or more file chunks should be placed on it. In this algorithm, Rcpu is measured in GHz, and Rmem is measured in GB, while Rbandwidth is measured in 100Mbps. datasize are measured in GB. We define a threshold T hweight to distinguish overloaded nodes as follow, T hweight = ξ [max(Rweight [j]) − min(Rweight [j])] + min(Rweight [j]) (11) where ξ is an adjustment factor.

Generally speaking, it requires at least 2 ∗ O(logψ) MapReduce jobs to choose the initial centers. When logψ is enormous, we cannot put up with so many MapReduce jobs. Furthermore, in the above section, we have shown that MapReduce does not directly support the iterative operation and the more MapReduce jobs the more cost. As shown in Figure 1, the network cost of PSKM++ is small and there is m m only i=1 Ci + i=1 φXi (Uj ) in each iteration, but the I/O cost is huge because the whole input has to read twice.

As a result, considering the twelve most abundant elements in the universe plus fully ionized hydrogen and electrons, totally 181 extra variables need to be stored and another 181 advection equations need to be solved at every grid point. Except the increased computing workloads introduced by additional advection equations, it will use up to roughly 15 times of system memory more than the case without NEI, and the following high communication overhead between neighbor processes is unavoidable eithor.

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Algorithms and Architectures for Parallel Processing: 14th International Conference, ICA3PP 2014, Dalian, China, August 24-27, 2014. Proceedings, Part II by Xian-he Sun, Wenyu Qu, Ivan Stojmenovic, Wanlei Zhou, Zhiyang Li, Hua Guo, Geyong Min, Tingting Yang, Yulei Wu, Lei Liu (eds.)

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