The increase and diversity of simulation methods bears witness of the need for more efficient discrete event simulations in computational biology but how efficient are those methods, and how to ensure an efficient simulation for a concrete model? As the performance of simulation methods depends on the model, the simulator, and the infrastructure, general answers to those questions are likely to remain illusive; they have to be sought individually and experimentally instead. This requires configurable implementations of many algorithms, means to define and conduct meaningful experiments on them, and mechanisms for storing and analyzing observed performance data. In this paper, we first overview basic approaches for improving simulation performance and illustrate the challenges when comparing different methods. We then argue that providing all the aforementioned components in a single tool, in our case the open source modeling and simulation framework JAMES II, reveals many synergies in effectively pursuing both questions. This is exemplified by presenting results of recent studies and introducing a new component to swiftly evaluate simulator code changes against previous experimental data.
