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R. Krishnamurti, E. Ma, "An Approximation Algorithm for Scheduling Tasks on Varying Partition Sizes in Partitionable Multiprocessor Systems," IEEE Transactions on Computers, vol. 41, no. 12, pp. 15721579, December, 1992.  
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@article{ 10.1109/12.214665, author = {R. Krishnamurti and E. Ma}, title = {An Approximation Algorithm for Scheduling Tasks on Varying Partition Sizes in Partitionable Multiprocessor Systems}, journal ={IEEE Transactions on Computers}, volume = {41}, number = {12}, issn = {00189340}, year = {1992}, pages = {15721579}, doi = {http://doi.ieeecomputersociety.org/10.1109/12.214665}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Computers TI  An Approximation Algorithm for Scheduling Tasks on Varying Partition Sizes in Partitionable Multiprocessor Systems IS  12 SN  00189340 SP1572 EP1579 EPD  15721579 A1  R. Krishnamurti, A1  E. Ma, PY  1992 KW  task scheduling; polynomial time algorithm; parameter dependent bound; asymptotically tight bound; approximation algorithm; partition sizes; partitionable multiprocessor systems; multiple partitions; controller; processors; minimum completion time schedule; parallelization; NPhard; worstcase performance bound; computational complexity; multiprocessing programs; multiprocessing systems; parallel algorithms; scheduling. VL  41 JA  IEEE Transactions on Computers ER   
A partitionable multiprocessor system can form multiple partitions, each consisting of a controller and a varying number of processors. Given such a system and a set of tasks, each of which can be executed on partitions of varying sizes, the authors study the problem of choosing the partition sizes and a minimum completion time schedule for the execution of these tasks. They assume that the number of tasks to be scheduled on the system is no more than the maximum number of partitions that can be formed simultaneously by the system, and that parallelization of the tasks can achieve at most perfect speedup. They show this scheduling problem to be NPhard, and present a polynomial time approximation algorithm for this problem. The authors derive a parameter dependent, asymptotically tight worstcase performance bound for the algorithm, and evaluate its average performance through simulation.
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