A Direct Solver for Time Parallelization

With the advent of very large scale parallel computer, having millions of processing cores, it has become important to also use the time direction for parallelization. Among the successful methods doing this are the parareal algorithm, the paraexp algorithm, PFASST and also waveform relaxation methods of Schwarz or Dirichlet-Neumann or Neumann type. We present here a mathematical analysis of a further method to parallelize in time, proposed by Maday and Ronquist in 2007. It is based on the diagonalization of the time stepping matrix. Like for many time domain parallelization methods, this seems at first not to be a very promising approach, since this matrix is essentially triangular, and for a fixed time step even a Jordan block, and thus not diagonalizable. If one however chooses different time steps, diagonalization is possible, and one has to trade of between the accuracy due to necessarily having different time steps, and numerical errors in the diagonalization process of these almost not diagonalizable matrices. We study this trade-off mathematically and propose an optimization strategy for the choice of the parameters, for a Backward Euler discretization of the heat equation in two dimensions.