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Rudolph / Feitelson

Job Scheduling Strategies for Parallel Processing

IPPS '97 Workshop, Geneva, Switzerland, April 5, 1997, Proceedings

Medium: Buch
ISBN: 978-3-540-63574-1
Verlag: Springer Berlin Heidelberg
Erscheinungstermin: 27.08.1997
Lieferfrist: bis zu 10 Tage

This book constitutes the strictly refereed post-workshop proceedings of the 1997 IPPS Workshop on Job Scheduling Strategies for Parallel Processing held in Geneva, Switzerland, in April 1997, as a satelite meeting of the IEEE/CS International Parallel Processing Symposium.
The 12 revised full papers presented were carefully reviewed and revised for inclusion in the book. Also included is a detailed introduction surveying the state of the art in the area. Among the topics covered are processor allocation, parallel scheduling, massively parallel processing, shared-memory architectures, gang scheduling, etc.


Produkteigenschaften


  • Artikelnummer: 9783540635741
  • Medium: Buch
  • ISBN: 978-3-540-63574-1
  • Verlag: Springer Berlin Heidelberg
  • Erscheinungstermin: 27.08.1997
  • Sprache(n): Englisch
  • Auflage: 1997
  • Serie: Lecture Notes in Computer Science
  • Produktform: Kartoniert
  • Gewicht: 482 g
  • Seiten: 305
  • Format (B x H x T): 155 x 235 x 18 mm
  • Ausgabetyp: Kein, Unbekannt
Autoren/Hrsg.

Herausgeber

Theory and practice in parallel job scheduling.- Using queue time predictions for processor allocation.- A historical application profiler for use by parallel schedulers.- Memory usage in the LANL CM-5 workload.- Modeling of workload in MPPs.- PScheD Political scheduling on the CRAY T3E.- An experimental evaluation of processor pool-based scheduling for shared-memory NUMA multiprocessors.- Implementing multiprocessor scheduling disciplines.- Objective-oriented algorithm for job scheduling in parallel heterogeneous systems.- Implications of I/O for gang scheduled workloads.- Improved utilization and responsiveness with gang scheduling.- Global state detection using network preemption.- Performance evaluation of gang scheduling for parallel and distributed multiprogramming.