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Min Tan, Howard Jay Siegel, "A Stochastic Model for Heterogeneous Computing and Its Application in Data Relocation Scheme Development," IEEE Transactions on Parallel and Distributed Systems, vol. 9, no. 11, pp. 10881101, November, 1998.  
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@article{ 10.1109/71.735956, author = {Min Tan and Howard Jay Siegel}, title = {A Stochastic Model for Heterogeneous Computing and Its Application in Data Relocation Scheme Development}, journal ={IEEE Transactions on Parallel and Distributed Systems}, volume = {9}, number = {11}, issn = {10459219}, year = {1998}, pages = {10881101}, doi = {http://doi.ieeecomputersociety.org/10.1109/71.735956}, 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  A Stochastic Model for Heterogeneous Computing and Its Application in Data Relocation Scheme Development IS  11 SN  10459219 SP1088 EP1101 EPD  10881101 A1  Min Tan, A1  Howard Jay Siegel, PY  1998 KW  Data relocation KW  greedy algorithm KW  heterogeneous computing KW  mapping KW  matching KW  optimization KW  scheduling KW  stochastic modeling. VL  9 JA  IEEE Transactions on Parallel and Distributed Systems ER   
Abstract—In a dedicated, mixedmachine, heterogeneous computing (HC) system, an application program may be decomposed into subtasks, then each subtask assigned to the machine where it is best suited for execution. Data relocation is defined as selecting the sources for needed data items. It is assumed that multiple independent subtasks of an application program can be executed concurrently on different machines whenever possible. A theoretical stochastic model for HC is proposed, in which the computation times of subtasks and communication times for intermachine data transfers can be random variables. The optimization problem for finding the optimal matching, scheduling, and data relocation schemes to minimize the total execution time of an application program is defined based on this stochastic HC model. The global optimization criterion and search space for the above optimization problem are described. It is validated that a greedy algorithmbased approach can establish a local optimization criterion for developing data relocation heuristics. The validation is provided by a theoretical proof based on a set of common assumptions about the underlying HC system and application program. The local optimization criterion established by the greedy approach, coupled with the search space defined for choosing valid data relocation schemes, can help developers of future practical data relocation heuristics.
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