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Parallel Markov chain Monte Carlo simulations
| Content Provider | eScholarship Repository: University of California |
|---|---|
| Author | Ren, Ruichao Orkoulas, G. |
| Abstract | With strict detailed balance, parallel Monte Carlo simulation through domain decomposition cannot be validated with conventional Markov chain theory, which describes an intrinsically serial stochastic process. In this work, the parallel version of Markov chain theory and its role in accelerating Monte Carlo simulations via cluster computing is explored. It is shown that sequential updating is the key to improving efficiency in parallel simulations through domain decomposition. A parallel scheme is proposed to reduce interprocessor communication or synchronization, which slows down parallel simulation with increasing number of processors. Parallel simulation results for the two-dimensional lattice gas model show substantial reduction of simulation time for systems of moderate and large size. (c) 2007 American Institute of Physics. |
| File Format | |
| ISSN | 00219606 |
| Alternate Webpage(s) | https://escholarship.org/uc/item/4vh518kv |
| Journal | Journal of Chemical Physics |
| Volume Number | 126 |
| Language | English |
| Publisher | AMER INST PHYSICS |
| Publisher Date | 2007-06-01 |
| Access Restriction | Open |
| Subject Keyword | Parallel Simulation Markov Chain Monte Carlo Transition Matrix |
| Content Type | Text |
| Resource Type | Article |