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Savina Bansal, Padam Kumar, Kuldip Singh, "An Improved Duplication Strategy for Scheduling Precedence Constrained Graphs in Multiprocessor Systems," IEEE Transactions on Parallel and Distributed Systems, vol. 14, no. 6, pp. 533544, June, 2003.  
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@article{ 10.1109/TPDS.2003.1206502, author = {Savina Bansal and Padam Kumar and Kuldip Singh}, title = {An Improved Duplication Strategy for Scheduling Precedence Constrained Graphs in Multiprocessor Systems}, journal ={IEEE Transactions on Parallel and Distributed Systems}, volume = {14}, number = {6}, issn = {10459219}, year = {2003}, pages = {533544}, doi = {http://doi.ieeecomputersociety.org/10.1109/TPDS.2003.1206502}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Parallel and Distributed Systems TI  An Improved Duplication Strategy for Scheduling Precedence Constrained Graphs in Multiprocessor Systems IS  6 SN  10459219 SP533 EP544 EPD  533544 A1  Savina Bansal, A1  Padam Kumar, A1  Kuldip Singh, PY  2003 KW  Algorithm KW  distributed computing KW  interconnection network KW  multiprocessor scheduling. VL  14 JA  IEEE Transactions on Parallel and Distributed Systems ER   
Abstract—Scheduling precedence constrained task graphs, with or without duplication, is one of the most challenging NPcomplete problems in parallel and distributed computing systems. Duplication heuristics are more effective, in general, for fine grain tasks graphs and for networks with high communication latencies. However, most of the available duplication algorithms are designed under the assumption of unbounded availability of fully connected processors, and lie in high complexity range. Low complexity optimal duplication algorithms work under restricted cost and/or shape parameters for the task graphs. Further, the required number of processors grows in proportion to the taskgraph size significantly. An improved duplication strategy is proposed that works for arbitrary task graphs, with a limited number of interconnectionconstrained processors. Unlike most other algorithms that replicate all possible parents/ancestors of a given task, the proposed algorithm tends to avoid redundant duplications and duplicates the nodes selectively, only if it helps in improving the performance. This results in lower duplications and also lower time and space complexity. Simulation results are presented for clique and an interconnectionconstrained network topology with random and regular benchmark task graph suites, representing a variety of parallel numerical applications. Performance, in terms of normalized schedule length and efficiency, is compared with some of the wellknown and recently proposed algorithms. The suggested algorithm turns out to be most efficient, as it generates better or comparable schedules with remarkably less processor consumption.
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