Job Scheduling Strategies for Parallel Processing: IPPS '96 Workshop, Honolulu, Hawaii, April 16, 1996. Proceedings

by Dror G. Feitelson (Editor), Larry Rudolph (Editor)

Overview

This book constitutes the strictly refereed post-workshop proceedings of the International Workshop on Job Scheduling Strategies for Parallel Processing, held in conjunction with IPPS '96 symposium in Honolulu, Hawaii, in April 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.

Product Details

ISBN-13:	9783540618645
Publisher:	Springer Berlin Heidelberg
Publication date:	11/25/1996
Series:	Lecture Notes in Computer Science , #1162
Edition description:	1996
Pages:	300
Product dimensions:	6.10(w) x 9.25(h) x 0.03(d)

Table of Contents

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.