Parallelization strategies for density matrix renormalization group algorithms on shared-memory systems

Shared-memory (SMP) parallelization strategies for density matrix renormalization group (DMRG) algorithms enable the treatment of complex systems in solid state physics. We present two different approaches by which parallelization of the standard DMRG algorithm can be accomplished in an efficient wa...

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Bibliographic Details
Published in:Journal of computational physics Vol. 194; no. 2; pp. 795 - 808
Main Authors: Hager, G., Jeckelmann, E., Fehske, H., Wellein, G.
Format: Journal Article
Language:English
Published: Amsterdam Elsevier Inc 01.03.2004
Elsevier
Subjects:
Computational techniques
Exact sciences and technology
Mathematical methods in physics
Physics
Algorithms
Complex system
Density matrix
Two-dimensional calculations
Two dimensional model
Calculation
Renormalization group
Calculation methods
ISSN:0021-9991, 1090-2716
Online Access:Get full text
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Summary:Shared-memory (SMP) parallelization strategies for density matrix renormalization group (DMRG) algorithms enable the treatment of complex systems in solid state physics. We present two different approaches by which parallelization of the standard DMRG algorithm can be accomplished in an efficient way. The methods are illustrated with DMRG calculations of the two-dimensional Hubbard model and the one-dimensional Holstein–Hubbard model on contemporary SMP architectures. The parallelized code shows good scalability up to at least eight processors and allows us to solve problems which exceed the capability of sequential DMRG calculations.
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ISSN:0021-9991
1090-2716
DOI:10.1016/j.jcp.2003.09.018