Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/119873
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Type: Journal article
Title: Single flavour optimisations to Hybrid Monte Carlo
Author: Haar, T.
Kamleh, W.
Zanotti, J.
Nakamura, Y.
Citation: Computer Physics Communications, 2019; 238:111-123
Publisher: Elsevier
Issue Date: 2019
ISSN: 0010-4655
1879-2944
Statement of
Responsibility: 
Taylor Haar, Waseem Kamleh, James Zanotti, Yoshifumi Nakamura
Abstract: It has become increasingly important to include one or more individual flavours of dynamical fermion in lattice QCD simulations. This is due in part to the advent of QCD+QED calculations, where isospin symmetry breaking means that the up, down, and strange quarks must be treated separately. These single-flavour pseudofermions are typically implemented as rational approximations to the inverse of the fermion matrix, using the technique known as Rational Hybrid Monte Carlo (RHMC). Over the years, a wide range of methods have been developed for accelerating simulations of two degenerate flavours of pseudofermion, while there are comparatively fewer such techniques for single-flavour pseudofermions. Here, we investigate two different filtering methods that can be applied to RHMC for simulating single-flavour pseudofermions, namely polynomial filtering (PF-RHMC), and filtering via truncations of the ordered product (tRHMC). A novel integration step-size tuning technique based on the characteristic scale is also introduced. Studies are performed on two different lattice volumes, demonstrating that one can achieve significant reductions in the computational cost of single-flavour simulations with these filtering techniques.
Keywords: Hybrid Monte Carlo algorithm; multiple time scale integration; single-flavour simulations
Rights: © 2018 Elsevier B.V. All rights reserved.
DOI: 10.1016/j.cpc.2018.12.009
Grant ID: http://purl.org/au-research/grants/arc/FT100100005
http://purl.org/au-research/grants/arc/DP120104627
http://purl.org/au-research/grants/arc/DP140103067
http://purl.org/au-research/grants/arc/DP150103164
Published version: http://dx.doi.org/10.1016/j.cpc.2018.12.009
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