000860311 001__ 860311
000860311 005__ 20200914093623.0
000860311 0247_ $$2doi$$a10.1016/0167-8191(92)90092-L
000860311 0247_ $$2ISSN$$a0167-8191
000860311 0247_ $$2ISSN$$a1872-7336
000860311 037__ $$aFZJ-2019-01086
000860311 082__ $$a620
000860311 1001_ $$0P:(DE-HGF)0$$aDontje, T.$$b0
000860311 245__ $$aStatistical analysis of simulation-generated time series: Systolic vs. semi-systolic correlation on the Connection Machine
000860311 260__ $$aAmsterdam [u.a.]$$bNorth-Holland, Elsevier Science$$c1992
000860311 3367_ $$2DRIVER$$aarticle
000860311 3367_ $$2DataCite$$aOutput Types/Journal article
000860311 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1600068961_27411
000860311 3367_ $$2BibTeX$$aARTICLE
000860311 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000860311 3367_ $$00$$2EndNote$$aJournal Article
000860311 520__ $$aAutocorrelation becomes an increasingly important tool to verify improvements in the state of the simulational art in Latice Gauge Theory. Semi-systolic and full-systolic algorithms are presented which are intensively used for correlation computations on the Connection Machine CM-2. The semi-systolic algorithm makes use of an intrinsic, microprogrammed global-add reduction function which is implemented extremely well on the Connection Machine. Nevertheless, the full-systolic correlation algorithm which makes use only of local communication and computation operations turns out to be substantially superior to the semi-systolic scheme whose basic step involved a non-local sum computation that extends over the entire machine.
000860311 588__ $$aDataset connected to CrossRef
000860311 7001_ $$0P:(DE-Juel1)132179$$aLippert, Thomas$$b1$$ufzj
000860311 7001_ $$0P:(DE-HGF)0$$aPetkov, N.$$b2
000860311 7001_ $$0P:(DE-HGF)0$$aSchilling, K.$$b3
000860311 773__ $$0PERI:(DE-600)1466340-5$$a10.1016/0167-8191(92)90092-L$$gVol. 18, no. 5, p. 575 - 588$$n5$$p575 - 588$$tParallel computing$$v18$$x0167-8191$$y1992
000860311 909CO $$ooai:juser.fz-juelich.de:860311$$pextern4vita
000860311 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132179$$aForschungszentrum Jülich$$b1$$kFZJ
000860311 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aExternal Institute$$b3$$kExtern
000860311 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bPARALLEL COMPUT : 2017
000860311 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000860311 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000860311 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search
000860311 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC
000860311 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List
000860311 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000860311 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000860311 915__ $$0StatID:(DE-HGF)1160$$2StatID$$aDBCoverage$$bCurrent Contents - Engineering, Computing and Technology
000860311 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5
000860311 9801_ $$aEXTERN4VITA
000860311 980__ $$ajournal
000860311 980__ $$aEDITORS
000860311 980__ $$aI:(DE-Juel1)JSC-20090406
000860311 980__ $$aI:(DE-Juel1)NIC-20090406