Computational Economics

, Volume 43, Issue 4, pp 485–495 | Cite as

Heterogeneous Computing in Economics: A Simplified Approach



This paper shows the potential of heterogeneous computing in solving dynamic equilibrium models in economics. We illustrate the power and simplicity of C++ Accelerated Massive Parallelism (C++ AMP) recently introduced by Microsoft. Starting from the same exercise as Aldrich et al. (J Econ Dyn Control 35:386–393, 2011) we document a speed gain together with a simplified programming style that naturally enables parallelization.


CUDA C++ C++ AMP DSGE models Econometrics  Heterogeneous computing 



We are grateful to an anonymous Referee and the Editor for their helpful comments and suggestions. We also thank the participants at Norges Bank research seminar held in Oslo and the CFE’12 Conference held in Oviedo. We acknowledge financial support by the Center for Research in Econometric Analysis of Time Series, CREATES, funded by the Danish National Research Foundation.


  1. Aldrich, E. M., Fernández-Villaverde, J., & Gallant, A. R. (2011). Tapping the supercomputer under your desk: Solving dynamic equilibrium models with graphics processors. Journal of Economic Dynamics and Control, 35, 386–393.CrossRefGoogle Scholar
  2. Boyd, C. (2008). Data-parallel computing. Queue, 6, 30–39.CrossRefGoogle Scholar
  3. Creel, M. (2005). User-friendly parallel computations with econometric examples. Computational Economics, 26, 107–128.CrossRefGoogle Scholar
  4. Creel, M., & Goffe, W. L. (2008). Multi-core CPUs, clusters, and grid computing: A tutorial. Computational Economics, 32, 353–382.CrossRefGoogle Scholar
  5. Flynn, M. (1972). Some computer organizations and their effectiveness. IEEE Transaction Computing, 21, 948–960.Google Scholar
  6. Heer, B., & Maussner, A. (2005). Dynamic general equilibrium modelling: Computational methods and applications. Berlin: Springer.Google Scholar
  7. ISO. (2011). ISO/IEC 14882:2011 Information technology—Programming languages—C++. Geneva: International Organization for Standardization.Google Scholar
  8. Morozov, S., & Mathur, S. (2012). Massively parallel computation using graphics processors with application to optimal experimentation in dynamic control. Computational Economics, 21, 151–182.CrossRefGoogle Scholar
  9. Stroustrup, B. (2000). The C++ programming language (3rd ed.). Boston, MA: Addison-Wesley Longman.Google Scholar
  10. Tauchen, G. (1986). Finite state Markov–Chain approximations to univariate and vector autoregressions. Economics Letters, 20, 177–181.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Mathematical SciencesAalborg University and CREATESAalborgDenmark
  2. 2.CREATES, Department of Economics and BusinessAarhus UniversityAarhus VDenmark

Personalised recommendations