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Keqin Li, Yi Pan, "Probabilistic Analysis of Scheduling Precedence Constrained Parallel Tasks on Multicomputers with Contiguous Processor Allocation," IEEE Transactions on Computers, vol. 49, no. 10, pp. 10211030, October, 2000.  
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@article{ 10.1109/12.888038, author = {Keqin Li and Yi Pan}, title = {Probabilistic Analysis of Scheduling Precedence Constrained Parallel Tasks on Multicomputers with Contiguous Processor Allocation}, journal ={IEEE Transactions on Computers}, volume = {49}, number = {10}, issn = {00189340}, year = {2000}, pages = {10211030}, doi = {http://doi.ieeecomputersociety.org/10.1109/12.888038}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Computers TI  Probabilistic Analysis of Scheduling Precedence Constrained Parallel Tasks on Multicomputers with Contiguous Processor Allocation IS  10 SN  00189340 SP1021 EP1030 EPD  10211030 A1  Keqin Li, A1  Yi Pan, PY  2000 KW  Averagecase performance ratio KW  binary system partitioning KW  contiguous processor allocation KW  largesttaskfirst KW  parallel task KW  precedence constraint KW  probabilistic analysis KW  task scheduling. VL  49 JA  IEEE Transactions on Computers ER   
Abstract—Given a set of precedence constrained parallel tasks with their processor requirements and execution times, the problem of scheduling precedence constrained parallel tasks on multicomputers with contiguous processor allocation is to find a nonpreemptive schedule of the tasks on a multicomputer such that the schedule length is minimized. This scheduling problem is substantially more difficult than other scheduling problems due to precedence constraints among tasks, the inherent difficulty of task scheduling, and processor allocation in multicomputers. We present an approximation algorithm called LLB that schedules tasks levelbylevel using the largesttaskfirst strategy supported by the binary system partitioning scheme to handle the three difficult issues in our scheduling problem. Though algorithm LLB does not have a bounded worstcase performance ratio, we show through probabilistic analysis that LLB has a quite reasonable averagecase performance ratio for typical classes of parallel computations. In particular, algorithm LLB has an averagecase performance ratio less than two for large scale parallel computations that have wide task graphs (i.e., that exhibit large parallelism).
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