Modeling and measuring multiprogramming and system overheads on a shared-memory multiprocessor: Case study

This paper presents methodologies capable of quantifying multiprogramming (MP) overhead on a computer system. Two methods which quantify the lower bound on MP overhead, along with a method to determine MP overhead present in real workloads, are introduced. The techniques are illustrated by determining the percentage of parallel processing time consumed by MP overhead on Alliant multiprocessors. The real workload MP overhead measurements, as well as measurements of other overhead components such as kernel lock spinning, are then used in a comprehensive case study of performance degradation due to overheads. It is found that MP overhead accounts for well over half of the total system overhead. Kernel lock spinning is determined to be a major component of both MP and total system overhead. Correlation analysis is used to uncover underlying relationships between overheads and workload characteristics. It is found that for the workloads studied, MP overhead in the parallel environment is not statistically dependent on the number of parallel jobs being multiprogrammed. However, because of increased kernel contention, serial jobs, even those executing on peripheral processors, are responsible for variation in MP overhead.     

A measurement-based model to predict the performance impact ofsystem modifications: a case study

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