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

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

This ebook constitutes the complaints of the fifth overseas convention, CPC 2010 , held in Hualien, Taiwan in may perhaps 2010. The sixty seven complete papers are conscientiously chosen from 184 submissions and concentrate on subject matters reminiscent of cloud and Grid computing, peer-to-peer and pervasive computing, sensor and moile networks, service-oriented computing, source administration and scheduling, Grid and pervasive purposes, semantic Grid and ontologies, cellular trade and prone.

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Read or Download Advances in Grid and Pervasive Computing: 5th International Conference, GPC 2010, Hualien, Taiwan, May 10-13, 2010. Proceedings PDF

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

Sample text

4 Examples In order to demonstrate the approach and evaluate its performance, we will show a few examples. The demonstration code was selected not because it is the best candidate for scalability, but rather because it is most relevant in the real world. 1 Tests Setup and Methodology We have performed all measurements in a test environment consisting of 3 heterogeneous machines. It is more difficult to achieve good speedups in a heterogeneous environment, because the scheduling is complicated by unequal computing power of machines.

However, we could not expect high speedups in this case, because based on Figure 1 the Gamma machine is 2 times faster than both Alpha and Beta together, so the speedup when only Gamma and Beta are involved, can be considered good (the absolute best speedup we can get is 22% while we got 10% speedup). The worse performance with the third machine involved is caused by a suboptimal scheduling when slower machines get too much work to do. We have to wait for the slowest machine to finish with its work at the end of the calculation, so smarter scheduling algorithms avoiding this problem could possibly resolve this limitation.

A static scheduling heuristic for heterogeneous processors. , Boug´e, L. ) Euro-Par 1996. LNCS, vol. 1124, pp. 573–577. Springer, Heidelberg (1996) 11. : Processor self-scheduling for multiple-nested parallel loops. In: ICPP, pp. 528–535 (1986) 12. : An efficiency and scalability model for heterogeneous clusters. In: IEEE International Conference on Cluster Computing, p. 427 (2001) 13. : Parallelizing fundamental algorithms such as sorting on multi-core processors for eda acceleration. In: ASP-DAC 2009: Proceedings of the 2009 Asia and South Pacific Design Automation Conference, Piscataway, NJ, USA, pp.

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