Numerical methods in Markov chain modeling
Operations Research
Iterative aggregation/disaggregation techniques for nearly uncoupled markov chains
Journal of the ACM (JACM)
From queueing networks to Markov chains: the XMARCA interface
Performance Evaluation - Special issue: performance modeling tools
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Comparison of Partitioning Techniques for Two-Level Iterative Solvers on Large, Sparse Markov Chains
SIAM Journal on Scientific Computing
A parallel solver for large-scale Markov chains
Applied Numerical Mathematics - Developments and trends in iterative methods for large systems of equations—in memoriam Rüdiger Weiss
Aggregation Methods for Large Markov Chains
Proceedings of the International Workshop on Computer Performance and Reliability
On Solving Stochastic Coupling Matrices Arising in Iterative Aggregation/Disaggregation Methods
MASCOTS '94 Proceedings of the Second International Workshop on Modeling, Analysis, and Simulation On Computer and Telecommunication Systems
Application of Threshold Partitioning of Sparse Matrices to Markov Chains
IPDS '96 Proceedings of the 2nd International Computer Performance and Dependability Symposium (IPDS '96)
High performance RDMA based all-to-all broadcast for infiniband clusters
HiPC'05 Proceedings of the 12th international conference on High Performance Computing
A new parallel algorithm for solving large-scale Markov chains
The Journal of Supercomputing
| Hi-index | 0.00 |
In this paper, we propose a new scalable parallel block aggregated iterative method (PBA) for computing the stationary distribution of a Markov chain. The PBA technique is based on aggregation of groups (block) of Markov chain states. Scalability of the PBA algorithm depends on varying the number of blocks and their size, assigned to each processor. PBA solves the aggregated blocks very efficiently using a modified LU factorization technique. Some Markov chains have been tested to compare the performance of PBA algorithm with other block techniques such as parallel block Jacobi and block Gauss---Seidel. In all the tested models PBA outperforms the other parallel block methods.