Monte Carlo methods for matrix computations on the gridBranford, S., Sahin, C., Thandavan, A., Weihrauch, C., Alexandrov, V. N. and Dimov, I. T. (2008) Monte Carlo methods for matrix computations on the grid. Future Generation Computer Systems-the International Journal of Grid Computing Theory Methods and Applications, 24 (6). pp. 605-612. ISSN 0167-739X Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1016/j.future.2007.07.006 Abstract/SummaryMany scientific and engineering applications involve inverting large matrices or solving systems of linear algebraic equations. Solving these problems with proven algorithms for direct methods can take very long to compute, as they depend on the size of the matrix. The computational complexity of the stochastic Monte Carlo methods depends only on the number of chains and the length of those chains. The computing power needed by inherently parallel Monte Carlo methods can be satisfied very efficiently by distributed computing technologies such as Grid computing. In this paper we show how a load balanced Monte Carlo method for computing the inverse of a dense matrix can be constructed, show how the method can be implemented on the Grid, and demonstrate how efficiently the method scales on multiple processors. (C) 2007 Elsevier B.V. All rights reserved.
Altmetric Deposit Details University Staff: Request a correction | Centaur Editors: Update this record |
Lists
Lists
