By Peter Benner, Pablo Ezzatti, Enrique Quintana-Ortí, Alfredo Remón (auth.), Rocco Aversa, Joanna Kołodziej, Jun Zhang, Flora Amato, Giancarlo Fortino (eds.)
This quantity set LNCS 8285 and 8286 constitutes the court cases of the thirteenth overseas convention on Algorithms and Architectures for Parallel Processing , ICA3PP 2013, held in Vietri sul Mare, Italy in December 2013. the 1st quantity comprises 10 exotic and 31 average papers chosen from ninety submissions and overlaying themes corresponding to titanic info, multi-core programming and software program instruments, disbursed scheduling and cargo balancing, high-performance medical computing, parallel algorithms, parallel architectures, scalable and dispensed databases, dependability in allotted and parallel platforms, instant and cellular computing. the second one quantity contains 4 sections together with 35 papers from one symposium and 3 workshops held at the side of ICA3PP 2013 major convention. those are thirteen papers from the 2013 foreign Symposium on Advances of allotted and Parallel Computing (ADPC 2013), five papers of the overseas Workshop on huge facts Computing (BDC 2013), 10 papers of the overseas Workshop on relied on info in tremendous info (TIBiDa 2013) in addition to 7 papers belonging to Workshop on Cloud-assisted clever Cyber-Physical platforms (C-Smart CPS 2013).
Read or Download Algorithms and Architectures for Parallel Processing: 13th International Conference, ICA3PP 2013, Vietri sul Mare, Italy, December 18-20, 2013, Proceedings, Part II PDF
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Extra info for Algorithms and Architectures for Parallel Processing: 13th International Conference, ICA3PP 2013, Vietri sul Mare, Italy, December 18-20, 2013, Proceedings, Part II
Djebbar and G. Belalem Clustering the Dependency Matrix In scientific workflows, many instances will be executed simultaneously. Some tasks use a large amount of data and produce, and several other output data. In order to perform a task, all required data must be located in the same datacenter and this may require some data movement (also called datasets). In addition, if two datasets are always used together with many tasks, they must be stored together in order to reduce the frequency of data movement.
289–296 (2010) 4. : Hierarchical cubic networks. IEEE Trans. Par. Dis. Sys. 6, 427–435 (1995) 5. : Node-to-set disjoint-path routing in hierarchical cubic networks. Comp. J. 55, 1440–1446 (2012) 6. : Set-to-set disjoint paths routing in hierarchical cubic networks. Comp. J. (to appear) 7. : The hierarchical hypercube: a new interconnection topology for massively parallel systems. IEEE Trans. Par. Dis. Sys. 5, 17–30 (1994) 8. : Optimal cube-connected cube multicomputers. J. Microcomp. Applications 17, 135–146 (1994) 9.
K − 1, in−1 , . . , i1 , i0 ], −n) ❀ ([k − 1, in−1 , . . , i1 , i0 ], +n) → ([0, in−1 , . . , i1 , i0 ], −n) = y ❀ ([0, in−1 , . . , i1 , i0 ], +n) = x. So, 20 A. Bossard and K. Kaneko for each of the k n clusters, we have appended to C a Hamiltonian path covering the dimension n + 1, and thus C is a Hamiltonian cycle for TCC(n + 1, k). 5 Conclusions We have proposed in this paper a new topology for interconnection networks of massively parallel systems: torus-connected cycles. This topology aims at mitigating the problems faced by many network topologies proposed in the literature that hamper the actual hardware implementation of these networks.