000811184 001__ 811184
000811184 005__ 20210129223755.0
000811184 0247_ $$2doi$$a10.1016/0167-8191(88)90122-6
000811184 0247_ $$2ISSN$$a0167-8191
000811184 0247_ $$2ISSN$$a1872-7336
000811184 0247_ $$2WOS$$aWOS:A1988R035300019
000811184 0247_ $$2altmetric$$aaltmetric:2874362
000811184 037__ $$aFZJ-2016-03693
000811184 082__ $$a004
000811184 1001_ $$0P:(DE-HGF)0$$aGurke, Renate$$b0$$eCorresponding author
000811184 245__ $$aThe approximate solution of the Euclidean traveling salesman problem on a CRAY X-MP
000811184 260__ $$aAmsterdam [u.a.]$$bNorth-Holland, Elsevier Science$$c1988
000811184 3367_ $$2DRIVER$$aarticle
000811184 3367_ $$2DataCite$$aOutput Types/Journal article
000811184 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1467793955_25740
000811184 3367_ $$2BibTeX$$aARTICLE
000811184 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000811184 3367_ $$00$$2EndNote$$aJournal Article
000811184 520__ $$aThe efficient use of MIMD computers calls for a careful choice of adequate algorithms as for an implementation taking into account the particular architecture. To demonstrate these facts, a parallel algorithm to find an approximate solution to the Euclidean Traveling Salesman Problem (ETSP) is presented. The algorithm is a parallelization of Karp's partitioning algorithm. It is a divide-and-conquer method for solving the ETSP approximately. Since the successor vertex to any vertex in the tour is usually a nearby vertex, the problem can be ‘geographically’ partitioned into subproblems which then can be solved independently. The resulting subtours can be combined into a single tour which is an approximate solution to the ETSP. The algorithm is implemented on a CRAY X-MP with two and four processors, and results using macrotasking and microtasking are presented.
000811184 536__ $$0G:(DE-HGF)POF3-899$$a899 - ohne Topic (POF3-899)$$cPOF3-899$$fPOF III$$x0
000811184 588__ $$aDataset connected to CrossRef
000811184 773__ $$0PERI:(DE-600)1466340-5$$a10.1016/0167-8191(88)90122-6$$gVol. 8, no. 1-3, p. 177 - 183$$n1-3$$p177 - 183$$tParallel computing$$v8$$x0167-8191$$y1988
000811184 909CO $$ooai:juser.fz-juelich.de:811184$$pVDB
000811184 9131_ $$0G:(DE-HGF)POF3-899$$1G:(DE-HGF)POF3-890$$2G:(DE-HGF)POF3-800$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bProgrammungebundene Forschung$$lohne Programm$$vohne Topic$$x0
000811184 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bPARALLEL COMPUT : 2014
000811184 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000811184 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000811184 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bThomson Reuters Master Journal List
000811184 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000811184 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000811184 915__ $$0StatID:(DE-HGF)1160$$2StatID$$aDBCoverage$$bCurrent Contents - Engineering, Computing and Technology
000811184 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5
000811184 9201_ $$0I:(DE-Juel1)VDB62$$kZAM$$lZentralinstitut für Angewandte Mathematik$$x0
000811184 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x1
000811184 980__ $$ajournal
000811184 980__ $$aVDB
000811184 980__ $$aI:(DE-Juel1)VDB62
000811184 980__ $$aI:(DE-Juel1)JSC-20090406
000811184 980__ $$aUNRESTRICTED
000811184 981__ $$aI:(DE-Juel1)JSC-20090406