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Parallel Krylov Methods for Econometric Model Simulation

Author

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  • Giorgio Pauletto
  • Manfred Gilli

Abstract

This paper investigates parallel solution methods to simulate large-scale macroeconometric models with forward-looking variables. The method chosen is the Newton-Krylov algorithm, and we concentrate on a parallel solution to the sparse linear system arising in the Newton algorithm. We empirically analyze the scalability of the GMRES method, which belongs to the class of so-called Krylov subspace methods. The results obtained using an implementation of the PETSc 2.0 software library on an IBM SP2 show a near linear scalability for the problem tested.

Suggested Citation

  • Giorgio Pauletto & Manfred Gilli, 2000. "Parallel Krylov Methods for Econometric Model Simulation," Computational Economics, Springer;Society for Computational Economics, vol. 16(1/2), pages 173-186, October.
    • Handle: RePEc:kap:compec:v:16:y:2000:i:1/2:p:173-186
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    Cited by:

    1. 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.
    2. N. B. Melnikov & A. P. Gruzdev & M. G. Dalton & M. Weitzel & B. C. O’Neill, 2021. "Parallel Extended Path Method for Solving Perfect Foresight Models," Computational Economics, Springer;Society for Computational Economics, vol. 58(2), pages 517-534, August.
    3. Gilli, Manfred & Kellezi, Evis & Pauletto, Giorgio, 2002. "Solving finite difference schemes arising in trivariate option pricing," Journal of Economic Dynamics and Control, Elsevier, vol. 26(9-10), pages 1499-1515, August.

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