Read e-book online Algorithms and Architectures for Parallel Processing: 14th 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 foreign convention on Algorithms and Architectures for Parallel Processing, ICA3PP 2014, held in Dalian, China, in August 2014. The 70 revised papers awarded within the volumes have been chosen from 285 submissions. the 1st quantity includes chosen papers of the most convention and papers of the first overseas Workshop on rising themes in instant and cellular Computing, ETWMC 2014, the fifth overseas Workshop on clever communique Networks, IntelNet 2014, and the fifth foreign Workshop on instant Networks and Multimedia, WNM 2014. the second one quantity contains chosen papers of the most convention and papers of the Workshop on Computing, communique and keep an eye on applied sciences in clever Transportation approach, 3C in ITS 2014, and the Workshop on protection and privateness in machine and community structures, SPCNS 2014.

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

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Hence, it can drastically reduce the iteration rounds from k to approximate O(logψ). Scalable k-means++ enhances the scalability of k-means++ and it is easily paralleled in MapReduce framework. Another merit of it is that it achieves an O(logk) approximation to the k-means objective. However, scalable k-means++ does not thoroughly break the inherent sequential nature of k-means++. Thus, it is embarrassingly parallel and can not be executed on MapReduce-based systems efficiently. Considering that there is no communication between Mappers, MapReduce scalable k-means++ requires two MapReduce jobs to complete in each round.

Xiao et al. 5) where Ne is the electron number density, αZ i = (Ne , T ) are the collisional and dielectronic recombination coefficients, and SiZ = S(Ne , T ) are the collisional ionization coefficients. 7, where XiZ is the mass fraction of the ion i of the element Z. 7) are integrated by NEI solver. 7) ∂t In many cases, integrating a large number of stiff ODEs at each time step will dominate the total wall-clock time of a simulation [16], but fortunately in this case the NEI solver, which only integrates several small sets of ODEs(≤27), is very lightweight, comparing with the much more complex PDE (partial differential equation) solvers, such as hydro solver and thermal conduction solver (diffuse).

6 All centers Ci are shuffled to one Reducer. m 7 One Reducer merges all the centers C = i=1 Ci . 8 Outputs U1 = C U0 . /* iterative job */ 9 for j = 2; j ≤ log ψ; j + + do // Oversampling 10 m Mappers read X in parallel, Uj−1 and φX (Uj−2 ). Each of them chooses centers with px = o ∗ ∗ d2 (x, Uj−1 )/φX (Uj−2 ) and computes φXi (Uj−1 ). 11 All centers Ci , all costs φXi (Uj−1 ), prx and ∗ d2 (x, Uj−1 ) for each new center are shuffled to one Reducer. // Refining by one Reducer m 12 Computes φX (Uj−1 ) and merges C = i=1 Ci .

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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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