skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: 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.

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

Citation Formats

Zhang, Jiang, Guo, Hanqi, and Yuan, Xiaoru. Efficient Unsteady Flow Visualization with High-Order Access Dependencies. United States: N. p., 2016. Web. doi:10.1109/PACIFICVIS.2016.7465254.
Zhang, Jiang, Guo, Hanqi, & Yuan, Xiaoru. Efficient Unsteady Flow Visualization with High-Order Access Dependencies. United States. doi:10.1109/PACIFICVIS.2016.7465254.
Zhang, Jiang, Guo, Hanqi, and Yuan, Xiaoru. Tue . "Efficient Unsteady Flow Visualization with High-Order Access Dependencies". United States. doi:10.1109/PACIFICVIS.2016.7465254. https://www.osti.gov/servlets/purl/1366300.
@article{osti_1366300,
title = {Efficient Unsteady Flow Visualization with High-Order Access Dependencies},
author = {Zhang, Jiang and Guo, Hanqi and Yuan, Xiaoru},
abstractNote = {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.},
doi = {10.1109/PACIFICVIS.2016.7465254},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2016},
month = {4}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share: