000860310 001__ 860310
000860310 005__ 20200914093554.0
000860310 0247_ $$2doi$$a10.1016/0167-8191(92)90120-V
000860310 0247_ $$2ISSN$$a0167-8191
000860310 0247_ $$2ISSN$$a1872-7336
000860310 037__ $$aFZJ-2019-01085
000860310 082__ $$a620
000860310 1001_ $$0P:(DE-Juel1)132179$$aLippert, T.$$b0$$ufzj
000860310 245__ $$aQuark propagator on the Connection Machine
000860310 260__ $$aAmsterdam [u.a.]$$bNorth-Holland, Elsevier Science$$c1992
000860310 3367_ $$2DRIVER$$aarticle
000860310 3367_ $$2DataCite$$aOutput Types/Journal article
000860310 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1600068930_28885
000860310 3367_ $$2BibTeX$$aARTICLE
000860310 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000860310 3367_ $$00$$2EndNote$$aJournal Article
000860310 520__ $$aThe computation of the fermion propagator in lattice Quantum Chromodynamics requires the solution of a large system of linear equations. We discuss and compare the structure, implementation and performance of two linear equation solvers, the Jacobi algorithm and the Conjugate Gradient algorithm, on the Connection Machine CM-2. We investigate the computer time needed for next neighbor communication versus the time required for floating point operations on 84 and 164 lattices. We compare the convergence behavior of Conjugate Gradient and Jacobi as applied to gauge configurations at β = 0.0 and 6.0.
000860310 588__ $$aDataset connected to CrossRef
000860310 7001_ $$0P:(DE-HGF)0$$aSchilling, K.$$b1
000860310 7001_ $$0P:(DE-HGF)0$$aPetkov, N.$$b2
000860310 773__ $$0PERI:(DE-600)1466340-5$$a10.1016/0167-8191(92)90120-V$$gVol. 18, no. 12, p. 1291 - 1299$$n12$$p1291 - 1299$$tParallel computing$$v18$$x0167-8191$$y1992
000860310 909CO $$ooai:juser.fz-juelich.de:860310$$pextern4vita
000860310 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132179$$aForschungszentrum Jülich$$b0$$kFZJ
000860310 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aExternal Institute$$b1$$kExtern
000860310 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bPARALLEL COMPUT : 2017
000860310 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000860310 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000860310 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search
000860310 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC
000860310 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List
000860310 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000860310 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000860310 915__ $$0StatID:(DE-HGF)1160$$2StatID$$aDBCoverage$$bCurrent Contents - Engineering, Computing and Technology
000860310 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5
000860310 9801_ $$aEXTERN4VITA
000860310 980__ $$ajournal
000860310 980__ $$aEDITORS
000860310 980__ $$aI:(DE-Juel1)JSC-20090406
000860310 980__ $$aI:(DE-Juel1)NIC-20090406