We propose a novel environment for PDES that facilitates the development of highly parallel models and requires minimal understanding of parallel computing concepts. We propose four primary approaches to improving the performance of PDES. We first examine the overhead required for synchronizing events to obtain correct results in parallel and develop a new approach to the structure of model entities and mechanisms for PDES that help to reduce that overhead. Secondly, we design new adaptive synchronization strategies that exploit this new model structure to obtain better cache performance and reduce context switching overhead. We then develop techniques to optimize communication in concert with these new strategies. Finally, we study load balancing in the context of optimistic synchronization and design new approaches to fit with our other techniques. These four approaches form an integrated system for handling non-ideal simulation models. We demonstrate our techniques via a highly flexible synthetic benchmark capable of mimicking a variety of simulation behaviors, as well as with simulations of network models for very large parallel computers.
