Enhancing the performance of the FETI method with preconditioning ¶techniques implemented on clusters of networked computers

 The FETI domain decomposition method for solving large-scale problems in computational structural mechanics involves the solution of an interface problem, which is handled by a Preconditioned Conjugate Projected Gradient (PCPG) algorithm. Two preconditioners are widely used to accelerate the convergence of the iterative PCPG algorithm: the optimal Dirichlet preconditioner and the economical lumped preconditioner. The Dirichlet preconditioner is computationally more efficient than the lumped preconditioner for ill-conditioned problems, but needs additional storage for the stiffness matrices of the subdomains' internal degrees of freedom (d.o.f.). In this study a new set of PCPG preconditioners is presented by providing approximate expressions to the inverse iteration matrix of the PCPG algorithm. The resulting approximate Dirichlet preconditioners are obtained by using instead of the whole stiffness matrix of the internal d.o.f. in each subdomain the following alternatives: a diagonal scaling matrix, a SSOR type matrix or an incomplete Cholesky factorization matrix. The computational behavior and performance of the proposed PCPG preconditioners is evaluated using an implementation of the FETI method on a cluster of ethernet-networked PCs running the message passing software PVM. It is demonstrated that the FETI method equipped with the approximate Dirichlet preconditioners leads for a number of large-scale problems to faster and less storage demanding overall solutions than with either Dirichlet or lumped preconditioner. Received: 28 December 2001 / Accepted: 22 August 2002