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Advances in Grid and Pervasive Computing: 5th International by Peter Sloot (auth.), Paolo Bellavista, Ruay-Shiung Chang,

By Peter Sloot (auth.), Paolo Bellavista, Ruay-Shiung Chang, Han-Chieh Chao, Shin-Feng Lin, Peter M. A. Sloot (eds.)

This ebook constitutes the lawsuits of the fifth overseas convention, CPC 2010 , held in Hualien, Taiwan in may possibly 2010. The sixty seven complete papers are conscientiously chosen from 184 submissions and concentrate on issues resembling cloud and Grid computing, peer-to-peer and pervasive computing, sensor and moile networks, service-oriented computing, source administration and scheduling, Grid and pervasive functions, semantic Grid and ontologies, cellular trade and providers.

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Extra resources for Advances in Grid and Pervasive Computing: 5th International Conference, GPC 2010, Hualien, Taiwan, May 10-13, 2010. Proceedings

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0 ˇˇtava and P. Tvrd´ık M. S 22 1 2 3 4 5 6 7 CPU cores (b) Fig. 2. (a) Coarse-grained parallelized QuickSort running in a cluster started on the Gamma machine. (b) Coarse-grained parallelized QuickSort running in a cluster started on the Alpha machine. efficiency, that is quite a good number compared to 75% efficiency in a nonclustered 2 core version. While speeds still increase considerably after involving the machine Gamma, the efficiency drops rapidly. The problem here is due to a performance bottleneck on the machine Alpha.

Textural Features for Image Classification. IEEE Trans. on Systems, Man, and Cybernetics 3(6), 610–621 (1973) 5. : FPGA Architecture for Fast Parallel Computation of Co-occurrence Matrices. Microprocessors and Microsystems 31, 160–165 (2007) 6. : Architecture and Implementation of a Distributed Reconfigurable Metacomputer. In: Proc. 2nd Int. Symp. on Parallel and Distributed Computing, October 2003, pp. 153–158 (2003) 7. : A New Approach to Implement Discrete Wavelet Transform using Collaboration of Reconfigurable Elements.

In a multi-core environment, it could have a form of a single shared work-queue from which execution units fetch their work. This model naturally transforms into a distributed case in Erlang, it is only required to start some workers on remote nodes. Due to the model simplicity, we will use this model in our transformation examples. While many algorithms fit nicely into the master-worker pattern, not all programs can be or are structured in this way. One of common reasons why the programs are not structured in a master-worker pattern is the requirement to perform parallel reduction at the end of a computation.

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