Using Multiple Processors for Monte Carlo Analysis of System Models

Model-Based Design has become a standard in the automotive industry. In addition to the well-documented advantages that come from modeling control algorithms, [1,2,3,4] modeling plants can lead to more robust designs. Plant modeling enables engineers to test a controller with multiple plant parameters, and to simulate nominal or ideal values. Modeling variable physical parameters provides a better representation of what can be expected in production. Monte Carlo analysis is a standard method of simulating variability that occurs in real physical parameters. Automotive companies use Monte Carlo testing to ensure high quality, robust designs. Due to time and resource constraints, engineers often examine only a limited number of key parameters rather than an entire set. This leaves the design vulnerable to problems caused by missing the full potential impact of parameters that were unvaried during testing. New high-performance computing tools and multiprocessor machines have eliminated the time and resource limitations in many cases by providing the processing power needed to vary large numbers of parameters in complex dynamic models. This paper presents new methods for distributing Monte Carlo analyses of system models across multiple machines. These methods reduce testing time and enable more complete analyses, ensuring better quality when designs go into production.