Get Algorithms and Architectures for Parallel Processing: 8th PDF

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

ISBN-10: 3540695001

ISBN-13: 9783540695004

ISBN-10: 354069501X

ISBN-13: 9783540695011

This e-book constitutes the refereed complaints 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 equipped in topical sections on scheduling and cargo balancing, interconnection networks, parallel algorithms, allotted platforms, parallelization instruments, grid computing, and software program systems.

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Similarly, for the gene y, we generate two random mutation positions v1 and v2 between 1 and p − 1, and set yi as a random integer number yi between 0 and v for i = v1 , v1 + 1, . . , v2 if v1 ≤ v2 . Or i = 1, 2, . . , 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.

In our algorithm this decision is taken basing on pheromone trails, following the strategy of the aliened ant. The scheduler assigns a probability value to the underlying resources, basing on the value of the relevant entry in the trails vector. The probability for the ith resource is calculated as: 1− phresi phresT OT (1) where phresi is the value of pheromone trail related to the ith resource and phresT OT is the sum of pheromone trails of all resources. The lower is the pheromone value the greater is the probability to select the resource.

For each node ni in Toplist do . max = 0 . for each node nx ∈ P arent(ni ) do . if t(nx ) + ω(nx ) + c(nx , ni ) > max then . max = t(nx ) + ω(nx ) + c(nx , ni ) . endif . endfor . t(ni ) = max . endfor 3 Scheduling Representation The scheduling in this paper is represented by Liu’s[4] formulation via two decision vectors [4] x and y, where x = (x1 , x2 , . . , xv ) is an integer decision vector representing v nodes with 1 ≤ xi ≤ v and xi = xj for all i = j and i, j = 1, 2, . . , v. That is , the sequence {x1 , x2 , .

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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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