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Parallel Krylov Methods for Econometric Model Simulation

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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.

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Bibliographic Info

Article provided by Society for Computational Economics in its journal Computational Economics.

Volume (Year): 16 (2000)
Issue (Month): 1/2 (October)
Pages: 173-186

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Handle: RePEc:kap:compec:v:16:y:2000:i:1/2:p:173-186

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Web page: http://www.springerlink.com/link.asp?id=100248
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Related research

Keywords: parallel computing; Newton-Krylov methods; sparse matrices; forward-looking models; GMRES; scalability;

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Cited by:
  1. William L. Goffe & Michael Creel, 2005. "Multi-core CPUs, Clusters and Grid Computing: a Tutorial," Computing in Economics and Finance 2005 438, Society for Computational Economics.
  2. 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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