In a second part we will be considering methods which are specifically designed for message-passing parallel platforms. This portability is achieved by using MPI for performing communication between processors. The methods considered in this context are essentially adapted from the Domain Decomposition framework. We will also touch on some related issues such as the effects of the partitioning on load balancing and on convergence rate of the algorithms. With the rapid increase in computing power offered by high performance machines there has been a corresponding increase in the size of applications problems which are now tractable.
Iterative methods employ the matrix of interest only as a multiplier, so sparsity is preserved.
In addition the basic operations from which these methods are built are well suited to parallel implementation on a range of different architectures. In addition, some salient features of alternative iterative approaches and direct methods are considered. The tutorial is based on recent work by the presenter, M. Szularz, J.S. Weston, K. Murphy, R.H. Perrott and others. Introduction to High Performance Linear Algebra Marcin Paprzycki , University of Southern Mississippi, USA Over the last few years a number of changes occurred in the area of high performance computing. This process resulted, among others, in a rapid convergence of HPC hardware architectures into few, relatively similar, developmental lines.