Scalable high-dimensional dynamic stochastic economic modeling

Détails

ID Serval
serval:BIB_3E208C036077
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Scalable high-dimensional dynamic stochastic economic modeling
Périodique
Journal of Computational Science
Auteur⸱e⸱s
Brumm J., Mikushin D., Scheidegger S., Schenk O.
ISSN
1877-7503
Statut éditorial
Publié
Date de publication
11/2015
Peer-reviewed
Oui
Volume
11
Pages
12-25
Langue
anglais
Résumé
We present a highly parallelizable and flexible computational method to solve high-dimensional stochastic dynamic economic models. Solving such models often requires the use of iterative methods, like time iteration or dynamic programming. By exploiting the generic iterative structure of this broad class of economic problems, we propose a parallelization scheme that favors hybrid massively parallel computer architectures. Within a parallel nonlinear time iteration framework, we interpolate policy functions partially on GPUs using an adaptive sparse grid algorithm with piecewise linear hierarchical basis functions. GPUs accelerate this part of the computation one order of magnitude thus reducing overall computation time by 50%. The developments in this paper include the use of a fully adaptive sparse grid algorithm and the use of a mixed MPI-Intel TBB-CUDA/Thrust implementation to improve the interprocess communication strategy on massively parallel architectures. Numerical experiments on “Piz Daint” (Cray XC30) at the Swiss National Supercomputing Centre show that high-dimensional international real business cycle models can be efficiently solved in parallel. To the best of our knowledge, this performance on a massively parallel petascale architecture for such nonlinear high-dimensional economic models has not been possible prior to present work.
Mots-clé
Theoretical Computer Science, Modelling and Simulation, General Computer Science
Web of science
Création de la notice
06/11/2018 8:46
Dernière modification de la notice
20/08/2019 13:34
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