Automatic Parallelization of Equation-Based Simulation Programs

University dissertation from Institutionen för datavetenskap

Abstract: Modern equation-based object-oriented modeling languages which have emerged during the past decades make it easier to build models of large and complex systems. The increasing size and complexity of modeled systems requires high performance execution of the simulation code derived from such models. More efficient compilation and code optimization techniques can help to some extent. However, a number of heavy-duty simulation applications require the use of high performance parallel computers in order to obtain acceptable execution times. Unfortunately, the possible additional performance offered by parallel computer architectures requires the simulation program to be expressed in a way that makes the potential parallelism accessible to the parallel computer. Manual parallelization of computer programs is generally a tedious and error prone process. Therefore, it would be very attractive to achieve automatic parallelization of simulation programs.This thesis presents solutions to the research problem of finding practically usable methods for automatic parallelization of simulation codes produced from models in typical equationbased object-oriented languages. The methods have been implemented in a tool to automatically translate models in the Modelica modeling language to parallel codes which can be efficiently executed on parallel computers. The tool has been evaluated on several application models. The research problem includes the problem of how to extract a sufficient amount of parallelism from equations represented in the form of a data dependency graph (task graph), requiring analysis of the code at a level as detailed as individual expressions. Moreover, efficient clustering algorithms for building clusters of tasks from the task graph are also required. One of the major contributions of this thesis work is a new approach for merging fine-grained tasks by using a graph rewrite system. Results from using this method show that it is efficient in merging task graphs, thereby decreasing their size, while still retaining a reasonable amount of parallelism. Moreover, the new task-merging approach is generally applicable to programs which can be represented as static (or almost static) task graphs, not only to code from equation-based models.An early prototype called DSBPart was developed to perform parallelization of codes produced by the Dymola tool. The final research prototype is the ModPar tool which is part of the OpenModelica framework. Results from using the DSBpart and ModPar tools show that the amount of parallelism of complex models varies substantially between different application models, and in some cases can produce reasonable speedups. Also, different optimization techniques used on the system of equations from a model affect the amount of parallelism of the model and thus influence how much is gained by parallelization.