Presentation
Time:
Master’s Thesis Defense: Jin Wang
Date:
9:00 am
Avery Hall
Room: Avery 347
1144 T St
Lincoln NE 68508
Lincoln NE 68508
Additional Info: AVH
Master’s Thesis Defense: Jin Wang
Committee Members: Dr. Hongfeng Yu (Advisor), Dr. Ashok Samal, and Dr. Zhenghong Tang
“View-Dependent Data Prefetching for Interactive Visualization of Large-Scale 3D Scientific Data”
Abstract: One of the greatest challenges for today’s interactive visualization is the efficient analysis and visualization of large-scale data, and I/O simply becomes a major performance bottleneck. I propose a new data management policy FlowCache to support interactive large-scale visual analytics. I characterize users’ data access patterns according to their data-dependent and view-dependent visualization operations, and leverage application knowledge to derive a novel scheme to predict data access during the interactive operations. Based on the prediction results, I develop a data replacement policy to exploit data locality and minimize data movement across multiple levels of a memory hierarchy. I have evaluated the approach on machines with multiple hierarchical memory levels and compared it with state-of-art data replacement methods and demonstrate the effectiveness of our approach.
Committee Members: Dr. Hongfeng Yu (Advisor), Dr. Ashok Samal, and Dr. Zhenghong Tang
“View-Dependent Data Prefetching for Interactive Visualization of Large-Scale 3D Scientific Data”
Abstract: One of the greatest challenges for today’s interactive visualization is the efficient analysis and visualization of large-scale data, and I/O simply becomes a major performance bottleneck. I propose a new data management policy FlowCache to support interactive large-scale visual analytics. I characterize users’ data access patterns according to their data-dependent and view-dependent visualization operations, and leverage application knowledge to derive a novel scheme to predict data access during the interactive operations. Based on the prediction results, I develop a data replacement policy to exploit data locality and minimize data movement across multiple levels of a memory hierarchy. I have evaluated the approach on machines with multiple hierarchical memory levels and compared it with state-of-art data replacement methods and demonstrate the effectiveness of our approach.