  The fully atomistic modeling of real-size plasmonic nanostructures is
computationally demanding, therefore most calculations are limited to
small-to-medium sized systems. However, plasmonic properties strongly depend on
the actual shape and size of the samples. In this paper we substantially extend
the applicability of classical, fully atomistic approaches by exploiting
state-of-the-art numerical iterative Krylov-based techniques. In particular, we
focus on the recently developed $\omega$FQ model, when specified to carbon
nanotubes, graphene-based nanostructures and metal nanoparticles. The
performance of Generalized Minimal Residual (GMRES) and Quasi-Minimum Residual
(QMR) algorithms is studied, with special emphasis on the dependence of the
convergence rate on the dimension of the structures (up to 1 million atoms) and
the physical parameters entering the definition of the atomistic approach.
