Tools for Understanding, Debugging, and Simulation Performance Improvement of Equation-based Models
2013 (English)Licentiate thesis, monograph (Other academic)
Equation-based object-oriented (EOO) modelling languages provide a convenient, declarative method for describing models of cyber-physical systems.Because of the ease of use of EOO languages, large and complex models can be built with limited effort.However, current state-of-the-art tools do not provide the user with enough information when errors appear or simulation results are wrong.It is paramount that the tools give the user enough information to correct errors or understand where the problems that lead to wrong simulation results are located.However, understanding the model translation process of an EOO compiler is a daunting task that not only requires knowledge of the numerical algorithms that the tool executes during simulation, but also the complex symbolic transformations being performed.
In this work, we develop and explore methods where the EOO tool records the transformations during the translation process in order to provide better diagnostics, explanations, and analysis.This information can be used to generate better error-messages during translation.It can also be used to provide better debugging for a simulation that produces unexpected results or where numerical methods fail.
Meeting deadlines is particularly important for real-time applications.It is usually important to identify possible bottlenecks and either simplify the model or give hints to the compiler that enables it to generate faster code.When profiling and measuring execution times of parts of the model the recorded information can also be used to find out why a particular system is slow.Combined with debugging information, it is possible to find out why this system of equations is slow to solve, which helps understanding what can be done to simplify the model.
Finally, we provide a method and tool prototype suitable for speeding up simulations by compiling a simulation executable for a parallel platform by partitioning the model at appropriate places.
Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2013. , 93 p.
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1592
Simulation, modelling, debugging, profiling, optimisation, parallelism, Modelica
IdentifiersURN: urn:nbn:se:liu:diva-90096Local ID: LIU-TEK-LIC-2013:26ISBN: 978-91-7519-624-4 (print)OAI: oai:DiVA.org:liu-90096DiVA: diva2:614273
2013-05-27, Alan Turing, Hus E, Campus Valla, Linköping University, Linköping, 13:15 (English)
Zimmer, Dirk, Ph.D.
Fritzson, Peter, Professor