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Hasyim Gautama, Arjan J.C. van Gemund, "LowCost Static Performance Prediction of Parallel Stochastic Task Compositions," IEEE Transactions on Parallel and Distributed Systems, vol. 17, no. 1, pp. 7891, January, 2006.  
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@article{ 10.1109/TPDS.2006.13, author = {Hasyim Gautama and Arjan J.C. van Gemund}, title = {LowCost Static Performance Prediction of Parallel Stochastic Task Compositions}, journal ={IEEE Transactions on Parallel and Distributed Systems}, volume = {17}, number = {1}, issn = {10459219}, year = {2006}, pages = {7891}, doi = {http://doi.ieeecomputersociety.org/10.1109/TPDS.2006.13}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Parallel and Distributed Systems TI  LowCost Static Performance Prediction of Parallel Stochastic Task Compositions IS  1 SN  10459219 SP78 EP91 EPD  7891 A1  Hasyim Gautama, A1  Arjan J.C. van Gemund, PY  2006 KW  Performance prediction KW  stochastic graphs KW  workload distribution. VL  17 JA  IEEE Transactions on Parallel and Distributed Systems ER   
Abstract—Current analytic solutions to the execution time distribution of a parallel composition of tasks having stochastic execution times are computationally complex, except for a limited number of distributions. In this paper, we present an analytical solution based on approximating execution time distributions in terms of the first four statistical moments. This lowcost approach allows the parallel execution time distribution to be approximated at ultralow solution complexity for a wide range of execution time distributions. The accuracy of our method is experimentally evaluated for synthetic distributions as well as for task execution time distributions found in real parallel programs and kernels (NASEP, SSSP, APSP, Splash2Barnes, PSRS, and WATOR). Our experiments show that the prediction error of the mean value of the parallel execution time for
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