Efficient Unsteady Flow Visualization with High-Order Access Dependencies

Conference · Tue Apr 19 00:00:00 EDT 2016

DOI:https://doi.org/10.1109/PACIFICVIS.2016.7465254· OSTI ID:1366300

Zhang, Jiang; Guo, Hanqi; Yuan, Xiaoru

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.

• Research Organization: Argonne National Lab. (ANL), Argonne, IL (United States)
• Sponsoring Organization: Chinese Academy of Sciences (CAS); National Natural Science Foundation of China (NSFC); USDOE Office of Science (SC)
• DOE Contract Number: AC02-06CH11357
• OSTI ID: 1366300
• Resource Relation: Conference: 9th IEEE Pacific Visualization Symposium , 04/19/16 - 04/22/16, Taipei, TW
• Country of Publication: United States
• Language: English