2016 IEEE Pacific Visualization Symposium (PacificVis) (2016)
April 19, 2016 to April 22, 2016
Jiang Zhang , Key Laboratory of Machine Perception (Ministry of Education), and School of EECS, Peking University
Hanqi Guo , Mathematics and Computer Science Division, Argonne National Laboratory
Xiaoru Yuan , Key Laboratory of Machine Perception (Ministry of Education), and School of EECS, Peking University
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 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.
J. Zhang, H. Guo and X. Yuan, "Efficient unsteady flow visualization with high-order access dependencies," 2016 IEEE Pacific Visualization Symposium (PacificVis)(PACIFICVIS), Taipei, Taiwan, 2016, pp. 80-87.