Job Scheduling Strategies for Parallel Processing: 9th International Workshop, JSSPP 2003, Seattle, WA, USA, June 24, 2003, Revised Papers (Lecture Notes in Computer Science)

Feitelson, Dror [Editor]; Rudolph, Larry [Editor]; Schwiegelshohn, Uwe [Editor];

ISBN 10: 3540204059 / ISBN 13: 9783540204053

Published by Springer, 2008

About this Item

Ex-library paperback in very nice condition with the usual markings and attachments. Seller Inventory # UTD14a2459

Bibliographic Details

Title: Job Scheduling Strategies for Parallel ...

Publisher: Springer

Publication Date: 2008

Binding: Paperback

Condition: Very Good

About this title

Synopsis:

This volume contains the papers presented at the 9th workshopon Job Sched- ing Strategies for Parallel Processing, which was held in conjunction with HPDC12 and GGF8 in Seattle, Washington, on June 24, 2003. The papers went through a complete review process, with the full version being read and eva- ated by ?ve to seven members of the program committee. We would like to take this opportunity to thank the program committee, Su-Hui Chiang, Walfredo Cirne, Allen Downey, Wolfgang Gentzsch, Allan Gottlieb, Moe Jette, Richard Lagerstrom, Virginia Lo, Cathy McCann, Reagan Moore, Bill Nitzberg, Mark Squillante, and John Towns, for an excellent job. Thanks are also due to the authors for their submissions, presentations, and ?nal revisions for this volume. Finally, we would like to thank the MIT Laboratory for Computer Science and the School of Computer Science and Engineering at the Hebrew University for the use of their facilities in the preparation of these proceedings. This year we had papers on three main topics. The ?rst was continued work on conventional parallel systems, including infrastructure and scheduling al- rithms. Notable extensions include the considerationof I/O and QoSissues. The secondmajortheme wasscheduling inthe contextofgridcomputing, whichc- tinues to be an area of much activity and rapid progress.The third area was the methodological aspects of evaluating the performance of parallel job scheduling.