Working Paper: NBER ID: w15909
Authors: Eric M. Aldrich; Jess Fernández-Villaverde; A. Ronald Gallant; Juan F. Rubio-RamÃrez
Abstract: This paper shows how to build algorithms that use graphics processing units (GPUs) installed in most modern computers to solve dynamic equilibrium models in economics. In particular, we rely on the compute unified device architecture (CUDA) of NVIDIA GPUs. We illustrate the power of the approach by solving a simple real business cycle model with value function iteration. We document improvements in speed of around 200 times and suggest that even further gains are likely.
Keywords: GPU; Dynamic Equilibrium Models; Computation Speed; Real Business Cycle
JEL Codes: C87; E0
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Use of GPUs (C88) | Improvements in computation time (C89) |
| Use of GPUs (C88) | Speed improvement in solving RBC model (C69) |
| GPU architecture (C68) | Improved data-parallel computations (C89) |
| Type of algorithm (C69) | Degree of speed improvement (C69) |
| Use of GPUs (C88) | Speedup in grid search with Howard improvement method (C52) |
| Parallel processing capabilities of GPUs (C15) | Improvements in computation time (C89) |