A parallel additive Schwarz preconditioned Jacobi-Davidson algorithm for polynomial eigenvalue problems in quantum dot simulation

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/51123

Title:	A parallel additive Schwarz preconditioned Jacobi-Davidson algorithm for polynomial eigenvalue problems in quantum dot simulation
Authors:	Hwang,FN;Wei,ZH;Huang,TM;Wang,WC
Contributors:	數學系
Keywords:	ELECTRONIC STATES;HERMITIAN EIGENPROBLEMS;NUMERICAL SCHEMES;LIMITED MEMORY;ENERGY-STATES;SYSTEMS;EIGENSOLVER;SEEKING
Date:	2010
Issue Date:	2012-03-27 18:22:25 (UTC+8)
Publisher:	國立中央大學
Abstract:	We develop a parallel Jacobi-Davidson approach for finding a partial set of eigenpairs of large sparse polynomial eigenvalue problems with application in quantum dot simulation. A Jacobi-Davidson eigenvalue solver is implemented based on the Portable, Extensible Toolkit for Scientific Computation (PETSc). The eigensolver thus inherits PETSc's efficient and various parallel operations, linear solvers. preconditioning schemes, and easy usages. The parallel eigenvalue solver is then used to solve higher degree polynomial eigenvalue problems arising in numerical simulations of three dimensional quantum dots governed by Schrodinger's equations. We find that the parallel restricted additive Schwarz preconditioner in conjunction with a parallel Krylov subspace method (e.g. GMRES) can solve the correction equations, the most costly step in the Jacobi-Davidson algorithm, very efficiently in parallel. Besides, the overall performance is quite satisfactory. We have observed near perfect superlinear speedup by using up to 320 processors. The parallel eigensolver can find all target interior eigenpairs of a quintic polynomial eigenvalue problem with more than 32 million variables within 12 minutes by using 272 Intel 3.0 GHz processors. (C) 2009 Elsevier Inc. All rights reserved.
Relation:	JOURNAL OF COMPUTATIONAL PHYSICS
Appears in Collections:	[Department of Mathematics] journal & Dissertation

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