PARALLEL ALGORITHM FOR CALCULATING GENERAL EQUILIBRIUM IN MULTIREGION ECONOMIC GROWTH MODELS

Nikolai B. Melnikov     (Lomonosov Moscow State University; Central Economics and Mathematics Institute, RAS, Moscow, Russian Federation)
Arseniy P. Gruzdev     (Lomonosov Moscow State University, Moscow, Russian Federation)
Michael G. Dalton     (National Oceanic and Atmospheric Administration, Seattle WA, United States)
Brian C. O'Neill     (National Center for Atmospheric Research, Boulder CO, United States)

Abstract


We develop and analyze a parallel algorithm for computing a solution in a multiregion dynamic general equilibrium model. The algorithm is based on an iterative method of the Gauss–Seidel type and exploits a special block structure of the model. Calculation of prices and input-output ratios in production for different time steps is carried out in parallel. We implement the parallel algorithm using the OpenMP interface for systems with shared memory. The efficiency of the algorithm is studied with the numbers of cores varying in the full range from one to the number of time steps of the model.


Keywords


Computable general equilibrium, Economic growth, Iterative methods, High-performance computing, OpenMP

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References


  1. Kelley C. Iterative Methods for Linear and Nonlinear Equations. SIAM: Philadelphia, 1995.
  2. Melnikov N., Gruzdev A., Dalton M., O'Neill B. Parallel algorithm for solving large-scale dynamic general equilibrium models // Russian Supercomputing Days, Moscow, 2015. P. 84–95.
  3. Fair R., Taylor J. Solution and maximum likelihood estimation of dynamic nonlinear rational expectations models // Econometrica, 1983. Vol. 51. P. 1169–1185.
  4. Dalton M., O'Neill B., Prskawetz A., Jiang L., Pitkin J. Population aging and future carbon emissions in the United States. Energy economics, 2008. Vol. 30, P. 642–675.
  5. Melnikov N., O'Neill B., Dalton M. Accounting for the household heterogeneity in dynamic general equilibrium models // Energy economics, 2012. Vol. 34. P. 1475–1483.
  6. Pernice M., Walker H. NITSOL: a Newton iterative solver for nonlinear systems // SIAM J. Sci. Comput. 1998. Vol. 19, P. 302–318.
  7. O'Neill B., Dalton D., Fuchs R., Jiang L., Pachauri S., Zigova K. Global demographic trends and future carbon emissions // Proc. Natl. Acad. Sci. U.S.A., 2010. Vol. 107. P. 17521–17526.
  8. Ren X., Weitzel M., O'Neill B.C., Lawrence P., Meiyappan P., Levis S., Balistreri E.J., Dalton M. Avoided economic impacts of climate change on agriculture: integrating a land surface model (CLM) with a global economic model (iPETS) // Climatic Change, 2016. P. 1–15. DOI: 10.1007/s10584-016-1791-1
  9. Stokey N., Lucas R., Prescott E., Recursive Methods in Economic Dynamics. Harvard University Press: Cambridge MA, 1989. 608 p.
  10. Armington P. A theory of demand for products distinguished by place of production // IMF Staff Papers, 1969. Vol. 16. P.170–201.
  11. Eisenstat S., Walker H. Globally convergent inexact Newton methods // SIAM J. Optimization, 1994. Vol. 4. P. 393–422.
  12. Sadovnichy V., Tikhonravov A., Voevodin Vl., Opanasenko V. ''Lomonosov'': Supercomputing at Moscow State University. In Contemporary High Performance Computing: From Petascale toward Exascale. Chapman & Hall/ CRC Computational Science, 2013. P. 283–307.
  13. Computational and Information Systems Laboratory, 2012. Yellowstone: IBM iDataPlex System (Climate Simulation Laboratory). Boulder, CO: National Center for Atmospheric Research. http://n2t.net/ark:/85065/d7wd3xhc
  14. Basic Linear Algebra Subprograms. Available from: http://www.netlib.org/blas/ Accessed 10 October 2016.
  15. Linear Algebra Package. Available from: http://www.netlib.org/lapack/ Accessed 10 October 2016.



DOI: http://dx.doi.org/10.15826/umj.2016.2.005

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