Massively parallel linearly-implicit extrapolation algorithms as a powerful tool in process simulation

We study the parallelization of linearly-implicit extrapolation codes for the solution of large scale PDE systems and differential algebraic equations on distributed memory machines. The main advantage of these algorithms is that they enable adapativity both in time and space. Additive Krylov-Schwarz methods yield high parallel perfomance for extrapolation methods. Our approach combines a slightly overlapping domain decomposition together with a polynomial block Neumann preconditioner and a reduced system technique. A further speedup we got by the explicit computation of the matrix-products of the preconditioner and the matrix of the linear system. The parallel algorithms exhibit scalability up to 64 processors already for medium-sized test problems. We show that the codes are really efficient in large application systems for chemical engineering problems.