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GPU Computing in Economics

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  • Aldrich, EM

Abstract

This paper discusses issues related to GPU for economic problems. It highlights new methodologies and resources that are available for solving and estimating economic models and emphasizes situations when they are useful and others where they are impractical. Two examples illustrate the different ways these GPU parallel methods can be employed to speed computation. © 2014 Elsevier B.V.

Suggested Citation

  • Aldrich, EM, 2014. "GPU Computing in Economics," Santa Cruz Department of Economics, Working Paper Series qt8p12748g, Department of Economics, UC Santa Cruz.
    • Handle: RePEc:cdl:ucscec:qt8p12748g
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    References listed on IDEAS

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    Cited by:

    1. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2015. "Dynamic predictive density combinations for large data sets in economics and finance," Working Paper 2015/12, Norges Bank.
    2. Carrillo-Maldonado, Paul & Díaz-Cassou, Javier, 2023. "An anatomy of external shocks in the Andean region," The Journal of Economic Asymmetries, Elsevier, vol. 27(C).
    3. Fernández-Villaverde, Jesús & Zarruk Valencia , David, 2018. "A Practical Guide to Parallelization in Economics," CEPR Discussion Papers 12890, C.E.P.R. Discussion Papers.
    4. Michael C. Hatcher & Eric M. Scheffel, 2016. "Solving the Incomplete Markets Model in Parallel Using GPU Computing and the Krusell–Smith Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 48(4), pages 569-591, December.
    5. Nalan Baştürk & Stefano Grassi & Lennart Hoogerheide & Herman K. Van Dijk, 2016. "Parallelization Experience with Four Canonical Econometric Models Using ParMitISEM," Econometrics, MDPI, vol. 4(1), pages 1-20, March.
    6. Fernández-Villaverde, J. & Rubio-Ramírez, J.F. & Schorfheide, F., 2016. "Solution and Estimation Methods for DSGE Models," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 527-724, Elsevier.
    7. Julien Albertini & Stéphane Moyen, 2020. "A General and Efficient Method for Solving Regime-Switching DSGE Models," Working Papers 2035, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    8. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2020. "A Bayesian Dynamic Compositional Model for Large Density Combinations in Finance," Working Paper series 20-27, Rimini Centre for Economic Analysis.

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