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Parallel Solution of Nonlinear Elliptic Equations
Numerical Methods
We develop subspace decomposition methods for the numerical solution
of the nonlinear Poisson-Boltzmann equation, a non-linear partial
differential equation,
describes the electrostatic potential of large complex biomolecules in solvent.
These important
problems have several interesting features impacting numerical algorithms,
including discontinuous coefficients, rapid nonlinearities, and three
spatial dimensions. We develop linear multilevel and domain decomposition
methods for the linearized problem, and extend the methods to the nonlinear
case with global inexact-Newton methods. Both theoretical
numerical
analysis and empirical evidence suggests that these methods are among the
most robust and efficient methods available for the this class of problems.
Implementation
The program implementing the numerical methods described above is known
as MG. The original version of MG (Multigrid Solver) was implemented
in FORTRAN 77 as part of Michael Holst's Phd thesis "Multilevel Methods
for the Poisson-Boltzmann Equation" at the
University of Illinois at Urbana-Champaign (UIUC).
A parallel implementation (PMG) of the multilevel-based methods for the fully
nonlinear 3D problem has been developed using
Compositional C++ (CC++).
This parallel implementation allows for
the simulation of extremely large and complex biological systems, (on
the order of one hundred million unknowns), due to both the large total
memory avaiable on massively parallel distributed computers, and due
to the additional computational efficiency gained by parallelization of
an already extrememly efficient linear complexity (in both computation and
memory) sequential algorithm.
This parallel implementation was accomplished via a collaboration between
Michael Holst
in Applied Mathematics at Caltech and
John Garnett in the
Computational Biology Group
at Caltech.
Presentations
Applications
Caltech Home
Computational Biology Home
California Institute of Technology, Pasadena, CA 91125. mholst@math.ucsd.edu |