Sciweavers

Share
FGCS
2008

Monte Carlo methods for matrix computations on the grid

8 years 11 months ago
Monte Carlo methods for matrix computations on the grid
Many 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.
Simon Branford, Cihan Sahin, Ashish Thandavan, Chr
Added 10 Dec 2010
Updated 10 Dec 2010
Type Journal
Year 2008
Where FGCS
Authors Simon Branford, Cihan Sahin, Ashish Thandavan, Christian Weihrauch, Vassil N. Alexandrov, Ivan Tomov Dimov
Comments (0)
books