Job Scheduling Strategies for Parallel Processing
Editat de Dror G. Feitelson, Larry Rudolphen Limba Engleză Paperback – 16 oct 1996
The book presents 15 thoroughly revised full papers accepted for inclusion on the basis of the reports of at least five program committee members. The volume is a highly competent contribution to advancing the state-of-the-art in the area of job scheduling for parallel supercomputers. Among the topics addressed are job scheduler, workload evolution, gang scheduling, multiprocessor scheduling, parallel processor allocation, and distributed memory environments.
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Specificații
ISBN-13: 9783540618645
ISBN-10: 3540618643
Pagini: 308
Ilustrații: VIII, 300 p.
Dimensiuni: 155 x 235 x 17 mm
Greutate: 0.47 kg
Ediția:1996
Editura: Springer
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3540618643
Pagini: 308
Ilustrații: VIII, 300 p.
Dimensiuni: 155 x 235 x 17 mm
Greutate: 0.47 kg
Ediția:1996
Editura: Springer
Locul publicării:Berlin, Heidelberg, Germany
Public țintă
Professional/practitionerCuprins
Toward convergence in job schedulers for parallel supercomputers.- Workload evolution on the Cornell Theory Center IBM SP2.- The EASY — LoadLeveler API project.- A batch scheduler for the Intel Paragon with a non-contiguous node allocation algorithm.- Architecture-independent request-scheduling with tight waiting-time estimations.- Packing schemes for gang scheduling.- A gang scheduling design for multiprogrammed parallel computing environments.- Implementation of gang-scheduling on workstation cluster.- Managing checkpoints for parallel programs.- Using runtime measured workload characteristics in parallel processor scheduling.- Parallel application characterization for multiprocessor scheduling policy design.- Dynamic vs. static quantum-based parallel processor allocation.- Dynamic versus adaptive processor allocation policies for message passing parallel computers: An empirical comparison.- Dynamic partitioning in different distributed-memory environments.- Locality-information-based scheduling in shared-memory multiprocessors.