Efficient Unsteady Flow Visualization with High-Order Access Dependencies

Abstract

We present a novel high-order access dependencies based model for efficient pathline computation in unsteady flow visualization. By taking longer access sequences into account to model more sophisticated data access patterns in particle tracing, our method greatly improves the accuracy and reliability in data access prediction. In our work, high-order access dependencies are calculated by tracing uniformly-seeded pathlines in both forward and backward directions in a preprocessing stage. The effectiveness of our proposed approach is demonstrated through a parallel particle tracing framework with high-order data prefetching. Results show that our method achieves higher data locality and hence improves the efficiency of pathline computation.