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As the need for computational power and efficiency rises, parallel systemsbecome increasingly popular among various scientific fields. While multiplecore-based architectures have been the center of attention for many years, therapid development of general purposes GPU-based architectures takes highperformance computing to the next level. In this work, differentimplementations of a parallel version of the preconditioned GMRES - anestablished iterative solver for large and sparse linear equation sets - arepresented, each of them on different computing architectures: From distributedand shared memory core-based to GPU-based architectures. The computationalexperiments emanate from the dicretization of a benchmark boundary valueproblem with the finite element method. Major advantages and drawbacks of thevarious implementations are addressed in terms of parallel speedup, executiontime and memory issues. Among others, comparison of the results in thedifferent architectures, show the high potentials of GPU-based architectures.

On the performance of various parallel GMRES implementations on CPU and GPU clusters