  As the need for computational power and efficiency rises, parallel systems
become increasingly popular among various scientific fields. While multiple
core-based architectures have been the center of attention for many years, the
rapid development of general purposes GPU-based architectures takes high
performance computing to the next level. In this work, different
implementations of a parallel version of the preconditioned GMRES - an
established iterative solver for large and sparse linear equation sets - are
presented, each of them on different computing architectures: From distributed
and shared memory core-based to GPU-based architectures. The computational
experiments emanate from the dicretization of a benchmark boundary value
problem with the finite element method. Major advantages and drawbacks of the
various implementations are addressed in terms of parallel speedup, execution
time and memory issues. Among others, comparison of the results in the
different architectures, show the high potentials of GPU-based architectures.
