Distributed simulation performance data mining

Abstract

The performance of logical process based distributed simulation (DS) protocols like Time Warp and Chandy/Misra/Bryant is influenced by a variety of factors such as the event structure underlying the simulation model, the partitioning into submodels, the performance characteristics of the execution platform, the implementation of the simulation engine and optimizations related to the protocols. The mutual performance effects of parameters exhibit a prohibitively complex degree of interweaving, giving analytical performance investigations only relative relevance. Nevertheless, performance analysis is of utmost practical interest for the simulationist who wants to decide on the suitability of a certain DS protocol for a specific simulation model before substantial efforts are invested in developing sophisticated DS codes. Since DS performance prediction based on analytical models appears doubtful with respect to adequacy and accuracy, this work presents a prediction method based on the simulated execution o f skeletal implementations of DS protocols. Performance data mining methods based on statistical analysis and a simulation tool for DS protocols have been developed for DS performance prediction, supporting the simulationist in three types of decision problems: (i) given a simulation problem and parallel execution platform, which DS protocol promises best performance, (ii) given a simulation model and a DS strategy, which execution platform is appropriate from the performance viewpoint, and (iii) what class of simulation models is best executed on a given multiprocessor using a certain DS protocol. Methodologically, skeletons of the most important variations of DS protocols are developed and executed in the N-MAP performance prediction environment. As a mining technique, performance data is collected and analyzed based on a full factorial design. The design predictor variables are used to explain DS performance. © 2001 Elsevier Science B.V. All rights reserved.