Quantitative stochastic simulation is a useful tool for studying performance of stochastic dynamic systems, but it can consume much time and computing resources. Even with today's high speed processors, it is common for simulation jobs to take hours or days to complete. Processor speeds are increasing as technology improves, but there are limits to the speed that can be achieved with a single, serial processor. To overcome these limits, parallel or distributed computation is needed. Not only does this speed up the simulation process, in the best case proportionally to the number of processors used, but the reliability of the program can be improved by placing less reliance on a single processor. One approach to parallel simulation is to divide up the simulation model and simulate a part of it on each processor. However, depending on the nature of the model it can be very difficult to find a way of dividing it up, and if the model does not divide up readily, the gain from parallelising it will be less than proportional to the number of processors. Even in cases where the model can be parallelised easily, more work is required to implement a parallel version of the simulation than a serial one. Akaroa takes a different approach to parallel simulation, that of multiple repli- cations in parallel or MRIP [1-5]. Instead of dividing up the simulation program, multiple instances of an ordinary serial simulation program are run simultaneously on diffrent processors.