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A Practical Guide to Parallelization in Economics

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  • Fernández-Villaverde, Jesús
  • Zarruk Valencia , David

Abstract

This guide provides a practical introduction to parallel computing in economics. After a brief introduction to the basic ideas of parallelization, we show how to parallelize a prototypical application in economics using, on CPUs, Julia, Matlab, R, Python, C++–OpenMP, Rcpp–OpenMP, and C++–MPI, and, on GPUs, CUDA and OpenACC. We provide code that the user can download and fork, present comparative results, and explain the strengths and weaknesses of each approach. We conclude with some additional remarks about alternative approaches.

Suggested Citation

  • Fernández-Villaverde, Jesús & Zarruk Valencia , David, 2018. "A Practical Guide to Parallelization in Economics," CEPR Discussion Papers 12890, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:12890
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    1. Aldrich, Eric M. & Fernández-Villaverde, Jesús & Ronald Gallant, A. & Rubio-Ramírez, Juan F., 2011. "Tapping the supercomputer under your desk: Solving dynamic equilibrium models with graphics processors," Journal of Economic Dynamics and Control, Elsevier, vol. 35(3), pages 386-393, March.
    2. Fernández-Villaverde, Jesús & Krueger, Dirk, 2011. "Consumption And Saving Over The Life Cycle: How Important Are Consumer Durables?," Macroeconomic Dynamics, Cambridge University Press, vol. 15(5), pages 725-770, November.
    3. Joao F. Cocco, 2005. "Consumption and Portfolio Choice over the Life Cycle," The Review of Financial Studies, Society for Financial Studies, vol. 18(2), pages 491-533.
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    5. Yoram Ben-Porath, 1967. "The Production of Human Capital and the Life Cycle of Earnings," Journal of Political Economy, University of Chicago Press, vol. 75(4), pages 352-352.
    6. Juan C. Conesa & Dirk Krueger, 1999. "Social Security Reform with Heterogeneous Agents," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 2(4), pages 757-795, October.
    7. Gourieroux, Christian & Monfort, Alain, 1997. "Simulation-based Econometric Methods," OUP Catalogue, Oxford University Press, number 9780198774754.
    8. Aldrich, EM, 2014. "GPU Computing in Economics," Santa Cruz Department of Economics, Working Paper Series qt8p12748g, Department of Economics, UC Santa Cruz.
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    Citations

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    Cited by:

    1. Chase Coleman & Spencer Lyon & Lilia Maliar & Serguei Maliar, 2021. "Matlab, Python, Julia: What to Choose in Economics?," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1263-1288, December.
    2. Jesús Fernández-Villaverde & Pablo A. Guerrón-Quintana, 2021. "Estimating DSGE Models: Recent Advances and Future Challenges," Annual Review of Economics, Annual Reviews, vol. 13(1), pages 229-252, August.
    3. Julien Albertini & Stéphane Moyen, 2020. "A General and Efficient Method for Solving Regime-Switching DSGE Models," Working Papers halshs-03067554, HAL.
    4. Julien Albertini & Stéphane Moyen, 2024. "A General and Efficient Method for Solving Regime-Switching DSGE Models," Computational Economics, Springer;Society for Computational Economics, vol. 64(6), pages 3645-3682, December.
    5. Mingzhuo Deng & Pablo A. Guerron-Quintana & Lewis Tseng, 2023. "Parallel Computation of Sovereign Default Models," Computational Economics, Springer;Society for Computational Economics, vol. 62(3), pages 1047-1085, October.
    6. Bognanni, Mark & Zito, John, 2020. "Sequential Bayesian inference for vector autoregressions with stochastic volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    7. Jesús Fernández-Villaverde & Galo Nuño & Jesse Perla, 2024. "Taming the curse of dimensionality: quantitative economics with deep learning," Working Papers 2444, Banco de España.
    8. Yasuo Hirose & Takeki Sunakawa, 2019. "Review of Solution and Estimation Methods for Nonlinear Dynamic Stochastic General Equilibrium Models with the Zero Lower Bound," The Japanese Economic Review, Springer, vol. 70(1), pages 51-104, March.
    9. Aur'elien Ouattara & Matthieu Bult'e & Wan-Ju Lin & Philipp Scholl & Benedikt Veit & Christos Ziakas & Florian Felice & Julien Virlogeux & George Dikos, 2021. "Scalable Econometrics on Big Data -- The Logistic Regression on Spark," Papers 2106.10341, arXiv.org.
    10. Bluhm, Benjamin & Cutura, Jannic, 2020. "Econometrics at scale: Spark up big data in economics," SAFE Working Paper Series 266, Leibniz Institute for Financial Research SAFE.
    11. Peri, Alessandro, 2020. "A hardware approach to value function iteration," Journal of Economic Dynamics and Control, Elsevier, vol. 114(C).
    12. Jesus Fernandez-Villaverde, 2020. "Simple Rules for a Complex World with Arti?cial Intelligence," PIER Working Paper Archive 20-010, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    13. Duarte, Victor & Duarte, Diogo & Fonseca, Julia & Montecinos, Alexis, 2020. "Benchmarking machine-learning software and hardware for quantitative economics," Journal of Economic Dynamics and Control, Elsevier, vol. 111(C).
    14. Jeppe Druedahl, 2021. "A Guide on Solving Non-convex Consumption-Saving Models," Computational Economics, Springer;Society for Computational Economics, vol. 58(3), pages 747-775, October.

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    More about this item

    Keywords

    Computational methods; Parallel computing; Programming languages;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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