By Hong Shen (auth.), Anu G. Bourgeois, S. Q. Zheng (eds.)
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 provided including 1 keynote speak and 1 instructional 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.
Read or Download Algorithms and Architectures for Parallel Processing: 8th International Conference, ICA3PP 2008, Cyprus, June 9-11, 2008 Proceedings PDF
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Additional resources for Algorithms and Architectures for Parallel Processing: 8th International Conference, ICA3PP 2008, Cyprus, June 9-11, 2008 Proceedings
A genetic algorithm for multiprocessor scheduling. IEEE Transaction on Parallel and Distributed Systems 5(2), 113–120 (1994) 2. : Eﬃcient scheduling of arbitrary task graphs to multiprocessors using a parallel genetic algorithm. Journal of Parallel and Distributed Computing (47), 58–77 (1997) 3. : 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.
Yang et al. n1 Z(n1) e12 n2 e17 Z(n2) e26 e15 e14 e13 n3 n4 n5 Z(n3) Z(n4) Z(n5) e27 e38 e48 n6 n7 n8 Z(n6) Z(n7) Z(n8) e69 e79 e89 n9 Z(n9) Fig. 1. Example of a DAG Table 1. 2 21 Computing Top Levels The top level  (t-level ) of a node ni is the length of a longest path (there can be more than one longest path) from an entry node to ni excluding ni . An algorithm for computing the t-levels is shown below. Because computation costs and communication costs are supposed to be stochastic variables in this paper, notations in the algorithm, like ω(ni ) and c(ni , nj ), denote samples of computation costs and communication costs.
The other three algorithms present bad results: in particular, Fig. 3. μqwt and σlb when changing the workload in terms of quantity of submitted jobs An ACO Inspired Strategy to Improve Jobs Scheduling 39 the Random algorithm gives the worst result, demonstrating the importance of a good scheduling algorithm. In terms of σlb, instead, all algorithms present the same behavior: their trend worsens with increasing workload size. As it is foreseeable, the RA algorithm shows the best load balancing capability.
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.)