Performance prediction of ocean color Monte Carlo simulations using multi-layer perceptron neural networks

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Citations (Scopus)


A performance modeling method is presented to predict the execution time of a parallel Monte Carlo (MC) radiative transfer simulation code for ocean color applications. The execution time of MC simulations is predicted using a multi-layer perceptron (MLP) neural network regression model trained with past execution time measurements in different execution environments and simulation cases. On the basis of the MLP performance model, a complementary job-environment mapping algorithm enables an efficient utilization of available high-performance computing resources minimizing the total execution time of the simulation jobs distributed in multiple environments.
Original languageUnknown
Title of host publicationProceedings of the International Conference on Computational Science (ICCS 2011), Procedia Computer Science, Vol.4
Publication statusPublished - 1 Jan 2011
EventInternational Conference on Computational Science (ICCS 2011) -
Duration: 1 Jan 2011 → …


ConferenceInternational Conference on Computational Science (ICCS 2011)
Period1/01/11 → …

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