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Cuda Based Computational Methods For Macroeconomic Forecasts

Author

Listed:
  • Bogdan OANCEA

    () ("Nicolae Titulescu" University from Bucharest, Romania)

  • Tudorel ANDREI

    () (The Bucharest Academy of Economic Studies, Romania)

  • Raluca DRAGOESCU

    () ("Artifex" University, Bucharest, Romania)

Abstract

Parallel computing can offer an enormous advantage regarding the performance for very large applications in almost any field: economics, scientific computing, computer vision, databases, data mining. GPUs are high performance many-core processors that can obtain very high FLOP rates. Since the first idea of using GPU for general purpose computing, things have evolved and now there are several approaches to GPU programming: CUDA from NVIDIA and Stream from AMD. CUDA is now a popular programming model for general purpose computations on GPU for C/C++ programmers. In this paper we present an implementation of some iterative and direct linear systems solvers that use the CUDA programming model. Our CUDA library is used to solve macroeconometric models with forward-looking variables based on Newton method for nonlinear systems of equations. The most difficult step for Newton methods represents the resolution of a large linear system for each iteration. Our library implements LU factorization, Jacobi, Gauss-Seidel and non-stationary iterative methods (GMRES, BiCG, BiCGSTAB) using C-CUDA extension. We compare the performance of our CUDA implementation with classic programs written to be run on CPU. Our performance tests show speedups of approximately 80 times for single precision floating point and 40 times for double precision.

Suggested Citation

  • Bogdan OANCEA & Tudorel ANDREI & Raluca DRAGOESCU, 2012. "Cuda Based Computational Methods For Macroeconomic Forecasts," New Trends in Modelling and Economic Forecast (MEF 2011), ROMANIAN ACADEMY – INSTITUTE FOR ECONOMIC FORECASTING;"Nicolae Titulescu" University of Bucharest, Faculty of Economic Sciences, vol. 1(1), pages 42-53, January.
  • Handle: RePEc:ntu:ntumef:vol1-iss1-12-042
    as

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    File URL: http://mef.univnt.ro/wp-content/uploads/MEF/2011/mef-01.04.pdf
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    References listed on IDEAS

    as
    1. Fair, Ray C & Taylor, John B, 1983. "Solution and Maximum Likelihood Estimation of Dynamic Nonlinear Rational Expectations Models," Econometrica, Econometric Society, vol. 51(4), pages 1169-1185, July.
    2. Michael Creel & William Goffe, 2008. "Multi-core CPUs, Clusters, and Grid Computing: A Tutorial," Computational Economics, Springer;Society for Computational Economics, vol. 32(4), pages 353-382, November.
    3. Flint Brayton & Eileen Mauskopf & David L. Reifschneider & Peter A. Tinsley & John Williams, 1997. "The role of expectations in the FRB/US macroeconomic model," Federal Reserve Bulletin, Board of Governors of the Federal Reserve System (U.S.), issue Apr, pages 227-245.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    parallel algorithms; macroeconometric models; rational expectations models; linear algebra; Krylov techniques;

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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