In the task farming approach, each node runs its own copy of a neural simulation (generally a detailed single-cell model). Each node and, therefore, each simulation runs totally independently of all other nodes. This method is particularly suited to examining large parameter spaces. In many of our applications, there are a wide variety of free parameters (i.e., those not defined experimentally). By using the task farming approach on these supercomputer-class machines, we can range widely across this huge parameter space looking for combinations which give biologically realistic results [Bhalla:93a] (i.e., similar to those measured experimentally). This allows us to make predictions for the future experimental measurement of these free parameters. It is also possible to run the same model many times in order to build up statistically significant summaries of the overall model behavior. The task farming approach is inherently parallel (zero communication between nodes and, therefore, linear scaling of computation with number of nodes available) and as such it is one of the most efficient programming styles available on any parallel machine (i.e., it allows the full utilization of the available computational power of the machine). This approach allows modelling in a single overnight run what would otherwise take a full year of nonstop computation on previously available computing platforms.