Parallel Krylov Methods for Econometric Model Simulation
Giorgio Pauletto () and
Manfred Gilli ()
Computational Economics, 2000, vol. 16, issue 1/2, 173-186
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.
Keywords: parallel computing; Newton-Krylov methods; sparse matrices; forward-looking models; GMRES; scalability (search for similar items in EconPapers)
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