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 court cases 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 contains chosen papers of the most convention and papers of the first overseas Workshop on rising issues in instant and cellular Computing, ETWMC 2014, the fifth overseas Workshop on clever verbal exchange Networks, IntelNet 2014, and the fifth overseas 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, conversation and keep an eye on applied sciences in clever Transportation procedure, 3C in ITS 2014, and the Workshop on safety and privateness in machine and community platforms, SPCNS 2014.
Read Online 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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Additional resources for Algorithms and Architectures for Parallel Processing: 14th International Conference, ICA3PP 2014, Dalian, China, August 24-27, 2014. Proceedings, Part II
In order to evaluate NPBFS algorithm, the parameters are set to empirical values. 6. 8GHz CPU, 4GB memory, and 1Gbps ethernet. 4GHz CPU, 8GB memory, and 1Gbps ethernet. 86GHz CPU, 1GB memory, and 100Mbps ethernet for each. 2 B. Tang, H. He, and G. Fedak Throughput of HybridDFS I/O We have implemented a set of micro-benchmarks, and have measured the achieved throughput as more and more concurrent clients access HybridDFS. Since that MapReduce applications need the “write-once-read-many” model, we evaluated the I/O performance when a single client writes data and concurrent clients read data.
15–28, 2014. c Springer International Publishing Switzerland 2014 16 Y. Xu et al. is one of the most widely used clustering methods, but it suﬀers from the wellknown problem that converges to a local optimum. Due to the reason that it is highly dependent upon the chosen of initial centers. In recent years, many researches have focused on improving its initialization method [5,6]. An important piece of work in this direction is the k-means++ . This algorithm is fast with small data in practice.
Px = ∗ d2 (x, Uj−1 )/φX (Uj−1 ). If px is still larger than prx , then this center is still kept in C, otherwise, it is removed from C. The output of this iteration is Uj and φX (Uj−1 ), then they become the input of the next iteration. As mentioned above, the network cost of OnR in each iteration includes 4 parts, all 22 Y. Xu et al. m chosen centers m i=1 Ci , all local clustering costs i=1 φXi (Uj−1 ), random value prx and ∗ d2 (x, Uj−1 ) for each chosen center. , i=1 Ci and i=1 φXi (Uj ), the main beneﬁt of OnR approach is that it dramatically reduces the I/O cost (read input data X only once), at the cost of shuﬄing a bit more data in each iteration.