New PDF release: Algorithms and Architectures for Parallel Processing: 8th

By Hong Shen (auth.), Anu G. Bourgeois, S. Q. Zheng (eds.)

ISBN-10: 3540695001

ISBN-13: 9783540695004

ISBN-10: 354069501X

ISBN-13: 9783540695011

This publication constitutes the refereed court cases of the eighth foreign convention on Algorithms and Architectures for Parallel Processing, ICA3PP 2008, held in Agia Napa, Cyprus, in June 2008.

The 31 revised complete papers offered including 1 keynote speak and 1 educational have been rigorously reviewed and chosen from 88 submissions. The papers are geared up in topical sections on scheduling and cargo balancing, interconnection networks, parallel algorithms, dispensed structures, parallelization instruments, grid computing, and software program systems.

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Extra info for Algorithms and Architectures for Parallel Processing: 8th International Conference, ICA3PP 2008, Cyprus, June 9-11, 2008 Proceedings

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Proportion among short, medium and long jobs) – the system behavior in case of wrong estimation of jobs execution time. In order to evaluate the performance, two evaluation concerns have been taken into account: the first one is related to queue waiting time perceived by resource users, the second one is related to resources usage (load balancing and jobs throughput) perceived by resource managers. In the following, we shortly depict the grid model, the algorithms compared with the proposed one and, finally, we show the results.

Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Computing Surveys 31(4), 406–471 (1999) 4. : Theory and Practice of Uncertain Programming, 1st edn. Physica-Verlag, Heidelberg (2002) 5. : Dynamic critical-path scheduling: An effective technique for allocating task graphs to multiprocessors. IEEE Tran. Parallel Distrib. Syst. 7(5), 506–521 (1996) 6. Wu, Gajaki: Hypertool: A programming aid for message-passing systems. IEEE Trans. Parallel Distrib. Syst. 1(3), 330–343 (1990) An ACO Inspired Strategy to Improve Jobs Scheduling in a Grid Environment Marilena Bandieramonte, Antonella Di Stefano, and Giovanni Morana Dept.

V2 − 1, v1 + 1, v1 + 2, . . , v if v1 > v2 . We then rearrange the sequence from small to large and obtain a new gene section y. Finally, we generate legal integer decision vectors (x , y ) and replace the parent with the offspring (x , y ). 5 Performance Results In this section, we use the DAG shown in Figure 1, and assume that the number of processors is 4. Note that the problem is NP-hard even if the variables are constants. So the solution of our algorithm may be near-optimal, as other genetic algorithms.

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Algorithms and Architectures for Parallel Processing: 8th International Conference, ICA3PP 2008, Cyprus, June 9-11, 2008 Proceedings by Hong Shen (auth.), Anu G. Bourgeois, S. Q. Zheng (eds.)

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