Parallel Quasirandom Walks on the Boundary

Karaivanova Aneta, Mascagni Michael and Simonov Nikolai A.
Additional contact information
Karaivanova Aneta: Central Laboratory for Parallel Processing, Bulgarian Academy of Sciences, Sofia, Bulgaria, aneta@csit.fsu.edu
Mascagni Michael: Department of Computer Science and School of Computational Science and Information Technology, Florida State University, Tallahassee, Florida, USA, Michael.Mascagni@fsu.edu
Simonov Nikolai A.: School of Computational Science and Information Technology, Florida State University, Tallahassee, Florida, USA and Institute of Computational Mathematics and Mathematical Geophysics, Novosibirsk, Russia, simonov@csit.fsu.edu

Monte Carlo Methods and Applications, 2004, vol. 10, issue 3-4, 311-319

Abstract: The Monte Carlo method called “random walks on boundary” has been successfully used for solving boundary-value problems. This method has significant advantages when compared with random walks on spheres, balls or on discrete grids when an exterior Dirichlet or Neumann problem is solved, or when we are interested in computing the solution to a problem at an arbitrary number of points using a single random walk.In this paper we will investigate ways:• to increase the convergence rate of this method by using quasirandom sequences instead of pseudorandom numbers for the construction of the boundary walks,• to find an efficient parallel implementation of this method on a cluster using MPI.In our parallel implementation we use disjoint contiguous blocks of quasirandom numbers extracted from a given quasirandom sequence for each processor. In this case, the increased convergence rate does not come at the cost of less trustworthy answers. We also present some numerical examples confirming both the increased rate of convergence and the good parallel efficiency of the method.

Date: 2004
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/mcma.2004.10.3-4.311 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:mcmeap:v:10:y:2004:i:3-4:p:311-319:n:13

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/mcma/html

DOI: 10.1515/mcma.2004.10.3-4.311

Access Statistics for this article

Monte Carlo Methods and Applications is currently edited by Karl K. Sabelfeld

More articles in Monte Carlo Methods and Applications from De Gruyter
Bibliographic data for series maintained by Peter Golla ().