By Topic

Dynamic Linear Solver Selection for Transient Simulations Using Machine Learning on Distributed Systems

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Eller, P.R. ; Corps of Eng., Eng. R&D Center, Inf. Technol. Lab., US Army, Vicksburg, MS, USA ; Cheng, J.-R.C. ; Maier, R.S.

Many transient simulations spend a significant portion of the overall runtime solving a linear system. A wide variety of preconditioned linear solvers have been developed to quickly and accurately solve different types of linear systems, each having options to customize the preconditioned solver for a given linear system. Transient simulations may produce significantly different linear systems as the simulation progresses due to special events occurring that make the linear systems more difficult to solve or the model moving closer to a state of equilibrium where the linear systems are easier to solve. Machine learning algorithms provide the ability to dynamically select the preconditioned linear solver for each linear system produced by a simulation. We can generate databases by computing attributes for each linear system, physical attributes for the transient simulation, computational attributes, and running times for a set of preconditioned solvers on each linear system. Machine learning algorithms can then use these databases to generate classifiers capable of dynamically selecting a preconditioned solver for each linear system given a set of attributes. This allows us to quickly and accurately compute each transient simulation using different preconditioned solvers throughout the simulation. This also provides the potential to produce speedups in comparison with using a single preconditioned solver for an entire transient simulation.

Published in:

Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW), 2012 IEEE 26th International

Date of Conference:

21-25 May 2012