Parallel simulation for a fish schooling model on a general-purpose graphics processing unit

We consider an individual-based model for fish schooling which incorporates a tendency for each fish to align its position and orientation with an appropriate average of its neighbors’ positions and orientations, plus a tendency for each fish to avoid collisions. To accurately determine statistical properties of the collective motion of fish whose dynamics are described by such a model, many realizations are typically required. This carries a very high computational cost. The current generation of graphics processing units is well-suited to this task. We describe our implementation, and present computational experiments illustrating the power of this technology for this important and challenging class of problems.