Parallel simulation of equation-based models on cudaenabled gpus

Our contributions with this work are methods and a pro-totype implementation for compiling and executing a lim-ited set of equation-based mathematical models (written in the object-oriented equation-based modeling language Mod-elica) on CUDA-enabled GPUs. We look at methods of finding parallelism in Modelica models, that can be used on the massively parallel CUDA architecture. The meth-ods have been implemented in a new back-end module of the OpenModelica compiler (an open-source Modelica com-piler). This paper shows that it is possible to automatically generate simulation code for pure continuous-time models that can be reduced to an ordinary differential equation sys-tem without algebraic loops and where the initial values of all variables and parameters are known at compile time. It is possible to get some speedup compared with simulation on a single CPU core, a (approximated) relative speedup of 4.6 was for instance obtained for one model