Jaroslaw Bylina

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In this paper a distributed iterative GMRES algorithm for solving huge and sparse linear systems (that appear in the Markov chain analysis of queueing network models) is considered. It is implemented using the MPI standard on a collection of Linux machines and the emphasis is put upon the size of linear systems being solved and possibility of storing huge(More)
Markovian models can generate very large sparse matrices, which are difficult to store and solve. A useful method for finding transient probabilities in Markovian models is the uniformization. The aim of this paper is to show that the performance of the uniformization can be improved using multi-GPU architecture. We propose partitioning scheme for HYB(More)
The authors consider the use of the parallel iterative methods for solving large sparse linear equation systems resulting from Markov chains-on a computer cluster. A combination of Jacobi and Gauss-Seidel iterative methods is examined in a parallel version. Some results of experiments for sparse systems with over 3 times 10<sup>7</sup> equations and about 2(More)