Simon Scheidegger

Simon Scheidegger, Hoover Institution

(Peta-) Scalable High-Dimensional Dynamic Stochastic Economic Modeling

Solving for the global solution of an economic model with substantial heterogeneity is very costly: The computation time and storage requirements increase dramatically with the amount of heterogeneity, i.e. with the dimensionality of the problem. It is therefore often far beyond the scope of current methods to include as much heterogeneity as a natural modeling choice would suggest. In this talk, I will 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 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. The solution method I will present includes 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. As a concrete applications of this framework, I will present results for an annually calibrated OLG model as well as a novel method for pricing American options under multi-factor models. The latter has competitive algorithmic complexity for long maturities and scales well to high-dimensional settings.