A parallel workload model and its implications for processor allocation
- 141 Downloads
We develop a workload model based on the observed behavior of parallel computers at the San Diego Supercomputer Center and the Cornell Theory Center. This model gives us insight into the performance of strategies for scheduling moldable jobs on space-sharing parallel computers. We find that Adaptive Static Partitioning (ASP), which has been reported to work well for other workloads, does not perform as well as strategies that adapt better to system load. The best of the strategies we consider is one that explicitly reduces allocations when load is high (a variation of Sevcik's (1989) A+ strategy).
KeywordsCluster Size Allocation Strategy Turnaround Time Greedy Strategy Average Cluster Size
Unable to display preview. Download preview PDF.
- S.-H. Chiang, R.K. Mansharamani and M.K. Vernon, Use of application characteristics and limited preemption for run-to-completion parallel processor scheduling policies, in: Proceedings of the 1994 ACM Sigmetrics Conference on Measurement and Modeling of Computer Systems(1994).Google Scholar
- A.B. Downey, A model for speedup of parallel programs, Technical Report CSD-97-933, University of California at Berkeley (1997), submitted to Information Processing Letters.Google Scholar
- D.G. Feitelson and B. Nitzberg, Job characteristics of a production parallel scientific workload on the NASA Ames iPSC/860, in: Job Scheduling Strategies for Parallel Processing, Lecture Notes in Computer Science, Vol. 949 (Springer, Berlin, 1995) pp. 337-360.Google Scholar
- E.W. Parsons and K.C. Sevcik, Coordinated allocation of memory and processors in multiprocessors, in: Proceedings of the ACM Sigmetrics Conference on Measurement and Modeling of Computer Systems(May 1996) pp. 57-67.Google Scholar
- E. Rosti, E. Smirni, G. Serazzi and L.W. Dowdy, Analysis of non-work-conserving processor partitioning policies, in: Job Scheduling Strategies for Parallel Processing, Lecture Notes in Computer Science, Vol. 949 (Springer, Berlin, 1995) pp. 165-181.Google Scholar
- S.K. Setia, The interaction between memory allocation and adaptive partitioning in message-passing multicomputers, in: Job Scheduling Strategies for Parallel Processing, Lecture Notes in Computer Science, Vol. 949 (Springer, Berlin, 1995) pp. 146-164.Google Scholar
- S.K. Setia and S.K. Tripathi, An analysis of several processor partitioning policies for parallel computers, Technical Report CS-TR-2684, University of Maryland (May 1991).Google Scholar
- S.K. Setia and S.K. Tripathi, A comparative analysis of static processor partitioning policies for parallel computers, in: Proceedings of the International Workshop on Modeling and Simulation of Computer and Telecommunications Systems(MASCOTS) (January 1993).Google Scholar
- K.C. Sevcik, Characterizations of parallelism in applications and their use in scheduling, Performance Evaluation Review 17(1) (May 1989) 171-180.Google Scholar
- E. Smirni, E. Rosti, L.W. Dowdy and G. Serazzi, Evaluation of multiprocessor allocation policies, Technical Report, Vanderbilt University (1993).Google Scholar
- K. Windisch, V. Lo, D. Feitelson, B. Nitzberg and R. Moore, A comparison of workload traces from two production parallel machines, in: 6th Symposium on the Frontiers of Massively Parallel Computation(1996).Google Scholar