The solution of large sparse linear systems on parallel computers using a hybrid implementation of the block Cimmino method

We are interested in solving large sparse systems of linear equations in parallel. Computing the solution of such systems requires a large amount of memory and computational power. The two main ways to obtain the solution are direct and iterative approaches. The former achieves this goal fast but with a large memory footprint while the latter is memory friendly but can be slow to converge. In this work we try first to combine both approaches to create a hybrid solver that can be memory efficient while being fast. Then we discuss a novel approach that creates a pseudo-direct solver that compensates for the drawback of the earlier approach. In the first chapters we take a look at row projection techniques, especially the block Cimmino method and examine some of their numerical aspects and how they affect the convergence. We then discuss the acceleration of convergence using conjugate gradients and show that a block version improves the convergence. Next, we see how partitioning the linear system affects the convergence and show how to improve its quality. We finish by discussing the parallel implementation of the hybrid solver, discussing its performance and seeing how it can be improved. The last two chapters focus on an improvement to this hybrid solver. We try to improve the numerical properties of the linear system so that we converge in a single iteration which results in a pseudo-direct solver. We first discuss the numerical properties of the new system, see how it works in parallel and see how it performs versus the iterative version and versus a direct solver. We finally consider some possible improvements to the solver. This work led to the implementation of a hybrid solver, our "ABCD solver" (Augmented Block Cimmino Distributed solver), that can either work in a fully iterative mode or in a pseudo-direct mode