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 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 overseas 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, verbal exchange and keep an eye on applied sciences in clever Transportation method, 3C in ITS 2014, and the Workshop on safeguard and privateness in computing device 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 I PDF
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Extra info for Algorithms and Architectures for Parallel Processing: 14th International Conference, ICA3PP 2014, Dalian, China, August 24-27, 2014. Proceedings, Part I
Web service QoS information model Firstly, when the Web service was once invoked, the Web service QoS information can be described by a two-dimensional user-service matrix. Service users may come from diﬀerent locations and have little knowledge about each other, so it is quite normal they wouldnt share the QoS information with others. Consequently, as shown in Figure 1 (a), only a sub-fraction of the QoS information is available. To acquire suﬃcient information for Web service recommendation, the collaborative ﬁltering methods[2,3,4,5,6] were adopted to predict the lacking QoS information.
Also, the QoS Value of Web services may change over time due to the dynamic environment. How to handle the dynamic data streams of incoming service QoS value is a big challenge. To address above problems, we propose an Web Service Recommendation Framework by considering the temporal information. We explore to envision such QoS value data as a tensor and transform it into tensor factorization problem. A Tucker decomposition (TD) method is proposed to cope with the model which includes multidimensional information: user, service and time.
For ﬁner grid with much bigger data that may be needed in the future, gpuPOM has to be extended to a GPU cluster. It is a part of the future work. 140 CUDA-aware MPI D2H2D MPI overlapping Our design Seconds per simulation day 120 100 80 60 40 20 0 1-GPU 2-GPUs 4-GPUs Number of GPUs Fig. 5. The weak scaling test between our communication overlapping design and another two communication methods Porting the Princeton Ocean Model to GPUs 13 Table 2. 92 In the strong scaling experiment, we test the eﬃciency of the gpuPOM on multiple GPUs.