Systematic Performance Optimization for the Powerdevs Simulator and Models of Large-Scale Real-World Applications

As simulation models grow in size and complexity, their performance comes under increasing pressure. In order to deliver results within an acceptable timeframe, new mechanisms are required that reduce the simulation performance burden while avoiding simplifications of model behavior. We present a systematic methodology for optimizing the PowerDEVS toolkit, improving both the core simulation engine and general-purpose behavioural models in a way that they stand as independent yet compatible tasks, thanks to the strict separation between simulator and models promoted by the DEVS formalism. We tested our optimizations on a variety of models that emphasize different effects. The main target system of this work is the large-scale network model of the Data Acquisition System (DAQ) at CERN, which is used to derive hardware requirements for future accelerator upgrades. Speedups for the DAQ model more than doubles the number of experiments that can be run within the same amount of time.