000860307 001__ 860307
000860307 005__ 20200914100518.0
000860307 0247_ $$2doi$$a10.1016/S0167-8191(99)00048-4
000860307 0247_ $$2ISSN$$a0167-8191
000860307 0247_ $$2ISSN$$a1872-7336
000860307 037__ $$aFZJ-2019-01082
000860307 082__ $$a620
000860307 1001_ $$0P:(DE-HGF)0$$aGüsken, Stephan$$b0
000860307 245__ $$aLattice QCD with two dynamical Wilson fermions on APE100 parallel systems
000860307 260__ $$aAmsterdam [u.a.]$$bNorth-Holland, Elsevier Science$$c1999
000860307 3367_ $$2DRIVER$$aarticle
000860307 3367_ $$2DataCite$$aOutput Types/Journal article
000860307 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1600070695_27411
000860307 3367_ $$2BibTeX$$aARTICLE
000860307 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000860307 3367_ $$00$$2EndNote$$aJournal Article
000860307 520__ $$aThe cost for stochastic sampling of quantum chromodynamics (QCD) vacuum configurations outweighs by far the costs of the remaining computational tasks in Lattice QCD, due to the non-local forces arising from the dynamics of fermion loops in the vacuum fluctuations. The evaluation of quality and hence efficiency of sampling algorithms is largely determined by the assessment of their decorrelation capacity along the Monte Carlo time series. In order to gain control over statistical errors, state-of-the-art research and development on QCD sampling algorithms need substantial amount of teraflops-hours. Over the past years two German–Italian collaborations, SESAM and TχL, carried out exploratory simulations, joining their resources in a meta-computing effort on various computer platforms in Italy and Germany. In this article, we shall discuss the practical aspects of this work, present highlights of autocorrelation measurements, illustrate the impact of unquenching on some fundamental parameters of QCD and describe the lessons to be learned for future, more realistic computer experiments of this kind.
000860307 588__ $$aDataset connected to CrossRef
000860307 7001_ $$0P:(DE-Juel1)132179$$aLippert, Thomas$$b1$$ufzj
000860307 7001_ $$0P:(DE-HGF)0$$aSchilling, Klaus$$b2
000860307 773__ $$0PERI:(DE-600)1466340-5$$a10.1016/S0167-8191(99)00048-4$$gVol. 25, no. 10-11, p. 1227 - 1242$$n10-11$$p1227 - 1242$$tParallel computing$$v25$$x0167-8191$$y1999
000860307 909CO $$ooai:juser.fz-juelich.de:860307$$pextern4vita
000860307 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132179$$aForschungszentrum Jülich$$b1$$kFZJ
000860307 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bPARALLEL COMPUT : 2017
000860307 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000860307 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000860307 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search
000860307 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC
000860307 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List
000860307 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000860307 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000860307 915__ $$0StatID:(DE-HGF)1160$$2StatID$$aDBCoverage$$bCurrent Contents - Engineering, Computing and Technology
000860307 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5
000860307 9801_ $$aEXTERN4VITA
000860307 980__ $$ajournal
000860307 980__ $$aEDITORS
000860307 980__ $$aI:(DE-Juel1)JSC-20090406
000860307 980__ $$aI:(DE-Juel1)NIC-20090406