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. We concentrate on a parallel solution to the sparse linear system arising in the Newton algorithm, and 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. JEL Classification: C63, C88, C30.