MDSIMAID: Automatic parameter optimization in fast electrostatic algorithms

MDSIMAID is a recommender system that optimizes parallel Particle Mesh Ewald (PME) and both sequential and parallel multigrid (MG) summation fast electrostatic solvers. MDSIMAID optimizes the running time or parallel scalability of these methods within a given error tolerance. MDSIMAID performs a ru...

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Bibliographic Details
Published in:Journal of computational chemistry 2005-07, Vol.26 (10), p.1021-1031
Main Authors: Crocker, Michael S., Hampton, Scott S., Matthey, Thierry, Izaguirre, Jesús A.
Format: Article
Language:English
Subjects:
Algorithms
automatic parameter optimization
Chemistry
fast electrostatic algorithms
Online Access:Get full text
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Description
Summary:MDSIMAID is a recommender system that optimizes parallel Particle Mesh Ewald (PME) and both sequential and parallel multigrid (MG) summation fast electrostatic solvers. MDSIMAID optimizes the running time or parallel scalability of these methods within a given error tolerance. MDSIMAID performs a run time constrained search on the parameter space of each method starting from semiempirical performance models. Recommended parameters are presented to the user. MDSIMAID's optimization of MG leads to configurations that are up to 14 times faster or 17 times more accurate than published recommendations. Optimization of PME can improve its parallel scalability, making it run twice as fast in parallel in our tests. MDSIMAID and its Python source code are accessible through a Web portal located at http://mdsimaid.cse.nd.edu. © 2005 Wiley Periodicals, Inc. J Comput Chem 26: 1021–1031, 2005
ISSN:0192-8651
1096-987X
DOI:10.1002/jcc.20240