000857022 001__ 857022
000857022 005__ 20230310131341.0
000857022 0247_ $$2doi$$a10.1016/j.cpc.2018.10.008
000857022 0247_ $$2ISSN$$a0010-4655
000857022 0247_ $$2ISSN$$a1386-9485
000857022 0247_ $$2ISSN$$a1879-2944
000857022 0247_ $$2Handle$$a2128/21487
000857022 0247_ $$2WOS$$aWOS:000458227100003
000857022 0247_ $$2altmetric$$aaltmetric:38320787
000857022 037__ $$aFZJ-2018-06303
000857022 082__ $$a530
000857022 1001_ $$0P:(DE-Juel1)132171$$aKrieg, Stefan$$b0$$eCorresponding author
000857022 245__ $$aAccelerating Hybrid Monte Carlo simulations of the Hubbard model on the hexagonal lattice
000857022 260__ $$aAmsterdam$$bNorth Holland Publ. Co.$$c2019
000857022 3367_ $$2DRIVER$$aarticle
000857022 3367_ $$2DataCite$$aOutput Types/Journal article
000857022 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1548859848_28074
000857022 3367_ $$2BibTeX$$aARTICLE
000857022 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000857022 3367_ $$00$$2EndNote$$aJournal Article
000857022 520__ $$aWe present different methods to increase the performance of Hybrid Monte Carlo simulations of the Hubbard model in two-dimensions. Our simulations concentrate on a hexagonal lattice, though can be easily generalized to other lattices. It is found that best results can be achieved using a flexible GMRES solver for matrix inversions and the second order Omelyan integrator with Hasenbusch acceleration on different time scales for molecular dynamics. We demonstrate how an arbitrary number of Hasenbusch mass terms can be included into this geometry and find that the optimal speed depends weakly on the choice of the number of Hasenbusch masses and their values. As such, the tuning of these masses is amenable to automization and we present an algorithm for this tuning that is based on the knowledge of the dependence of solver time and forces on the Hasenbusch masses. We benchmark our algorithms to systems where direct numerical diagonalization is feasible and find excellent agreement. We also simulate systems with hexagonal lattice dimensions up to 102 × 102 and Nt=64 . We find that the Hasenbusch algorithm leads to a speed up of more than an order of magnitude.
000857022 536__ $$0G:(DE-HGF)POF3-511$$a511 - Computational Science and Mathematical Methods (POF3-511)$$cPOF3-511$$fPOF III$$x0
000857022 536__ $$0G:(GEPRIS)35592816$$aDFG project 35592816 - TRR 55: Hadronenphysik mit Gitter-QCD (35592816)$$c35592816$$x1
000857022 536__ $$0G:(EU-Grant)754304$$aDEEP-EST - DEEP - Extreme Scale Technologies (754304)$$c754304$$fH2020-FETHPC-2016$$x2
000857022 536__ $$0G:(EU-Grant)610476$$aDEEP-ER - DEEP Extended Reach (610476)$$c610476$$fFP7-ICT-2013-10$$x3
000857022 536__ $$0G:(EU-Grant)287530$$aDEEP - Dynamical Exascale Entry Platform (287530)$$c287530$$fFP7-ICT-2011-7$$x4
000857022 536__ $$0G:(EU-Grant)730913$$aPRACE-5IP - PRACE 5th Implementation Phase Project (730913)$$c730913$$fH2020-EINFRA-2016-1$$x5
000857022 588__ $$aDataset connected to CrossRef
000857022 7001_ $$0P:(DE-Juel1)159481$$aLuu, Thomas$$b1
000857022 7001_ $$0P:(DE-HGF)0$$aOstmeyer, Johann$$b2
000857022 7001_ $$0P:(DE-HGF)0$$aPapaphilippou, Philippos$$b3
000857022 7001_ $$00000-0003-1412-7582$$aUrbach, Carsten$$b4
000857022 773__ $$0PERI:(DE-600)1466511-6$$a10.1016/j.cpc.2018.10.008$$gp. S0010465518303564$$p15-25$$tComputer physics communications$$v236$$x0010-4655$$y2019
000857022 8564_ $$uhttps://juser.fz-juelich.de/record/857022/files/W1477057.pdf
000857022 8564_ $$uhttps://juser.fz-juelich.de/record/857022/files/1804.07195.pdf$$yOpenAccess
000857022 8564_ $$uhttps://juser.fz-juelich.de/record/857022/files/W1477057.pdf?subformat=pdfa$$xpdfa
000857022 8564_ $$uhttps://juser.fz-juelich.de/record/857022/files/1804.07195.pdf?subformat=pdfa$$xpdfa$$yOpenAccess
000857022 8767_ $$8W1477057$$92018-11-05$$d2018-11-07$$eColour charges$$jZahlung erfolgt
000857022 909CO $$ooai:juser.fz-juelich.de:857022$$pdnbdelivery$$popenCost$$pec_fundedresources$$pVDB$$pdriver$$pOpenAPC$$popen_access$$popenaire
000857022 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132171$$aForschungszentrum Jülich$$b0$$kFZJ
000857022 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)159481$$aForschungszentrum Jülich$$b1$$kFZJ
000857022 9131_ $$0G:(DE-HGF)POF3-511$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data$$vComputational Science and Mathematical Methods$$x0
000857022 9141_ $$y2019
000857022 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000857022 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search
000857022 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bCOMPUT PHYS COMMUN : 2017
000857022 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000857022 915__ $$0StatID:(DE-HGF)0110$$2StatID$$aWoS$$bScience Citation Index
000857022 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000857022 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5
000857022 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000857022 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC
000857022 915__ $$0StatID:(DE-HGF)1150$$2StatID$$aDBCoverage$$bCurrent Contents - Physical, Chemical and Earth Sciences
000857022 915__ $$0StatID:(DE-HGF)0310$$2StatID$$aDBCoverage$$bNCBI Molecular Biology Database
000857022 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000857022 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz
000857022 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List
000857022 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
000857022 9201_ $$0I:(DE-Juel1)IAS-4-20090406$$kIAS-4$$lTheorie der Starken Wechselwirkung$$x1
000857022 980__ $$ajournal
000857022 980__ $$aVDB
000857022 980__ $$aUNRESTRICTED
000857022 980__ $$aI:(DE-Juel1)JSC-20090406
000857022 980__ $$aI:(DE-Juel1)IAS-4-20090406
000857022 980__ $$aAPC
000857022 9801_ $$aAPC
000857022 9801_ $$aFullTexts