2015
DOI: 10.1016/j.jocs.2015.07.004
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Scalable high-dimensional dynamic stochastic economic modeling

Abstract: 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 par… Show more

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Cited by 19 publications
(19 citation statements)
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References 39 publications
(87 reference statements)
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“…Instead, this training input (and the corresponding, nonsensical training target) can be discarded-that is to say, it is not added to the training set. This is in stark contrast to grid-based methods such as "Smolyak" (see, e.g., Krueger and Kubler, (2004) and Judd et al, (2014)) or "adaptive sparse grids" (see, e.g., Brumm and Scheidegger, (2017) and Brumm et al, (2015)), where the construction of the surrogate breaks down if not every optimization problem required by the algorithm can be solved. Second, computing solutions solely on a domain of relevancethat is, W (θ), allows one to carry out VFI on complex, high-dimensional geometries without suffering from massive inefficiencies, as the computational resources are concentrated where needed.…”
Section: A Solution Algorithm For Dynamic Incentive Problemsmentioning
confidence: 96%
“…Instead, this training input (and the corresponding, nonsensical training target) can be discarded-that is to say, it is not added to the training set. This is in stark contrast to grid-based methods such as "Smolyak" (see, e.g., Krueger and Kubler, (2004) and Judd et al, (2014)) or "adaptive sparse grids" (see, e.g., Brumm and Scheidegger, (2017) and Brumm et al, (2015)), where the construction of the surrogate breaks down if not every optimization problem required by the algorithm can be solved. Second, computing solutions solely on a domain of relevancethat is, W (θ), allows one to carry out VFI on complex, high-dimensional geometries without suffering from massive inefficiencies, as the computational resources are concentrated where needed.…”
Section: A Solution Algorithm For Dynamic Incentive Problemsmentioning
confidence: 96%
“…To solve for self-justied equlibria in general, we need to repeatedly approximate and interpolate multi-variate policy function on irregularly-shapedthat is, non-hypercubic domains. In such environments, standard grid-based methods such as Smolyak (see, e.g., Krueger and Kubler (2004) and Judd et al (2014)) or adaptive sparse grids (see, e.g., Brumm and Scheidegger (2017) and Brumm et al (2015)), will fail. To this end, we will follow closely Scheidegger and Bilionis (2017) and use Gaussian process regression (GPR) (see, e.g., Rasmussen and Williams (2005) and Sec.…”
Section: Function Approximation On High-dimensional and Irregularly-smentioning
confidence: 99%
“…Combining distributed memory parallelism with the on-node shared memory parallelism and the usage of GPUs, we are able to scale our code to at least 2,048 compute nodes with more than 60% efficiency, resulting in an overall speedup of more than 4 orders of magnitude compared to running this numerical experiment on a single CPU. More details regarding the optimization of our code and the parallel implementation can be found in Appendix D and in our companion paper (Brumm, Mikushin, Scheidegger, and Schenk (2015)).…”
Section: Parallelizationmentioning
confidence: 99%