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Solving Nonlinear Dynamic Models on Parallel Computers

Author

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  • Coleman, Wilbur John, II

Abstract

This paper describes an algorithm that takes advantage of parallel computing to solve discrete-time recursive systems that have an endogenous state variable. The algorithm is shown to work well on a CRAY X-MP vector processor as well as on a network of stand-alone workstations that simulates a multiprocessor computer.

Suggested Citation

  • Coleman, Wilbur John, II, 1993. "Solving Nonlinear Dynamic Models on Parallel Computers," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(3), pages 325-330, July.
    • Handle: RePEc:bes:jnlbes:v:11:y:1993:i:3:p:325-30
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    Cited by:

    1. Sergei Morozov & Sudhanshu Mathur, 2012. "Massively Parallel Computation Using Graphics Processors with Application to Optimal Experimentation in Dynamic Control," Computational Economics, Springer;Society for Computational Economics, vol. 40(2), pages 151-182, August.
    2. Mathur, Sudhanshu & Morozov, Sergei, 2009. "Massively Parallel Computation Using Graphics Processors with Application to Optimal Experimentation in Dynamic Control," MPRA Paper 16721, University Library of Munich, Germany.
    3. Morozov, Sergei & Mathur, Sudhanshu, 2009. "Massively parallel computation using graphics processors with application to optimal experimentation in dynamic control," MPRA Paper 30298, University Library of Munich, Germany, revised 04 Apr 2011.
    4. Yongyang Cai & Kenneth Judd & Greg Thain & Stephen Wright, 2015. "Solving Dynamic Programming Problems on a Computational Grid," Computational Economics, Springer;Society for Computational Economics, vol. 45(2), pages 261-284, February.
    5. Christopher A. Swann, 2001. "Software for parallel computing: the LAM implementation of MPI," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(2), pages 185-194.

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