Parallel discrete event simulations (PDES) encompass a broad range of analytical simulations. Their utility lies in their ability to model a system and provide information about its behavior in a timely manner. Current PDES methods provide limited performance improvements over sequential simulation. Many logical models for applications have fine granularity making them challenging to parallelize. In POSE, we examine the overhead required for optimistically synchronizing events. We have designed an object model based on the concept of virtualization and new adaptive optimistic methods to improve the performance of fine-grained PDES applications. These novel approaches exploit the speculative nature of optimistic protocols to improve single-processor parallel over sequential performance and achieve scalability for previously hard-to-parallelize fine-grained simulations.