Download e-book for kindle: Algorithms and Architectures for Parallel Processing: 8th by Hong Shen (auth.), Anu G. Bourgeois, S. Q. Zheng (eds.)

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

ISBN-10: 3540695001

ISBN-13: 9783540695004

ISBN-10: 354069501X

ISBN-13: 9783540695011

This booklet 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 conscientiously reviewed and chosen from 88 submissions. The papers are prepared 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

Example text

Bandieramonte, A. Di Stefano, and G. Morana and storage nodes, but also the underlying networks connecting them are heterogeneous. Fig. 1 shows a typical example of grid deployment. In this type of hierarchical organization it is possible to identify three scheduling layers. The first one is related to the algorithm used by RB to distribute jobs among CEs; this algorithm influences in a strong manner the performance of the whole system. The second scheduling layer manages the jobs allocation done by the CE on its underlying WNs.

We define the rank-based evaluation function as a(1 − a)i−1 ,i = 1, 2, . , P opulationSize, where a ∈ (0, 1) is a given parameter. Note that i = 1 means the best individual, i = P opulationSize the worst one. 3 Crossover Let us illustrate the crossover operator on the pair (x1 , y1 ) and (x2 , y2 ). First, we generate legal integer decision vectors for (x1 , y2 ) and (x2 , y1 ), which are denoted as (x1 , y2 ) and (x2 , y1 ). Then, two children are produced by the crossover operation as (x1 , y2 ) and (x2 , y1 ).

Another optimal schedule produced by DCP with different scheduling list [2] is denoted by DCP-2 shown below. Processor Processor Processor Processor 1: 2: 3: 4: n1 → n2 → n7 ; n4 → n8 → n9 ; n3 → n5 ; n6 . The schedule produced by MCP algorithm which is not an optimal schedule (makespan = 20) is shown below and denoted by MCP. Processor Processor Processor Processor 1: 2: 3: 4: n1 → n4 → n2 → n7 → n9 ; n3 → n6 ; n8 ; n5 . We suppose that the target value of expected makespan is b = 0. A run of the uncertain intelligent scheduling (UIS) algorithm (10000 cycles in stochastic simulation, 400 generations in genetic algorithm) shows that the optimal schedule which is denoted by UIS-1 is Processor 1: n1 → n2 → n7 → n8 → n9 ; Processor 2: n4 → n5 ; 21 UIS−1 UIS−2 UIS−3 DCP−1 DCP−2 20 Expected makespan 19 18 17 16 15 14 0 200 400 600 Number of samples 800 Fig.

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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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