Scheduling in multiprogrammed parallel systems
SIGMETRICS '88 Proceedings of the 1988 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Practical experience of run-time link reconfiguration in a multi-transputer machine
Concurrency: Practice and Experience
The performance of multiprogrammed multiprocessor scheduling algorithms
SIGMETRICS '90 Proceedings of the 1990 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Processor scheduling on multiprogrammed, distributed memory parallel computers
SIGMETRICS '93 Proceedings of the 1993 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Robust partitioning policies of multiprocessor systems
Performance Evaluation - Special issue: performance modeling of parallel processing systems
Scheduling memory constrained jobs on distributed memory parallel computers
Proceedings of the 1995 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Performance of Synchronous Parallel Algorithms with Regular Structures
IEEE Transactions on Parallel and Distributed Systems
IPPS '97 Proceedings of the 11th International Symposium on Parallel Processing
Job Characteristics of a Production Parallel Scientivic Workload on the NASA Ames iPSC/860
IPPS '95 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
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Processor scheduling in distributed-memory systems has received considerable attention in recent years. Several commercial distributed-memory systems use space-sharing processor scheduling. In space-sharing, the set of processors in a system is partitioned and each partition is assigned for the exclusive use of a job. Space-sharing policies can be divided into fixed, static, or dynamic categories. For distributed-memory systems, dynamic policies incur high overhead. Thus, static policies are considered as these policies provide a better performance than the fixed policies. Several static policies have been proposed in the literature. In a previously proposed adaptive static policy, the partition size is a function of the number of queued jobs. This policy, however, tends to underutilize the system resources. To improve the performance of this policy, we propose a new policy in which the partition size is a function of the total number of jobs in the system, as opposed to only the queued jobs. The results presented here demonstrate that the new policy performs substantially better than the original policy for the various workload and system parameters. Another major contribution is the evaluation of the performance sensitivity to job structure, variances in inter-arrival times and job service times, and network topology.