The paper presents a performance case study of parallel jobs executing in real multi user workloads. The study is based on a measurement based model capable of predicting the completion time distribution of the jobs executing under real workloads. The model constructed is also capable of predicting the effects of system design changes on application performance. The model is a finite state, discrete time Markov model with rewards and costs associated with each state. The Markov states are defined from real measurements and represent system/workload states in which the machine has operated. The paper places special emphasis on choosing the correct number of states to represent the workload measured. Specifically, the performance of computationally bound, parallel applications executing in real workloads on an Alliant FX/80 is evaluated. The constructed model is used to evaluate scheduling policies, the performance effects of multiprogramming overhead, and the scalability of the Alliant FX/8O in real workloads. The model identifies a number of available scheduling policies which would improve the response time of parallel jobs. In addition, the model predicts that doubling the number of processors in the current configuration would only improve response time for a typical parallel application by 25%. The model recommends a different processor configuration to more fully utilize extra processors. The paper also presents empirical results which validate the model created.>

Article Free Access Share on Performance prediction and tuning on a multiprocessor Authors: R. T. Dimpsey Center for Reliable and High-Performance Computing, Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, 1101 W. Springfield Ave., Urbana, IL Center for Reliable and High-Performance Computing, Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, 1101 W. Springfield Ave., Urbana, ILView Profile , R. K. Iyer Center for Reliable and High-Performance Computing, Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, 1101 W. Springfield Ave., Urbana, IL Center for Reliable and High-Performance Computing, Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, 1101 W. Springfield Ave., Urbana, ILView Profile Authors Info & Claims ISCA '91: Proceedings of the 18th annual international symposium on Computer architectureApril 1991 Pages 190–199https://doi.org/10.1145/115952.115972Published:01 April 1991Publication History 9citation315DownloadsMetricsTotal Citations9Total Downloads315Last 12 Months10Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

In this paper, performance degradation specifically due to the multiprogramming (MP) overhead in a parallel execution environment is quantified. In addition, total system overhead is also measured. A methodology, which estimates the MP overhead present in real workloads, is illustrated with real measurements taken on an Alliant FX/80 running Xylem (Cedar's operating system). It is found that MP overhead usually consumes between 10% and 23% of the processing power available to parallel programs. Total system overhead usually consumes between 12% and 30% of the parallel environment processing power, but is found to be as high as 82.1%. The mean MP overhead is determined to be 16% which is well over half the total system overhead executed on the system (the mean system overhead is determined to be 24% of the processing power). It is found that MP overhead, total system overhead, and application completion time are all moderately correlated. Relationships between the characteristics of a workload and the overhead measurements indicate that processor utilization and to a lesser degree paging are moderately correlated with the overhead present in the workload. It is also found that MP overhead is statistically independent of the number of parallel jobs in the system, while total system overhead is not.