The 13th IEEE Symposium on
Large Data Analysis and Visualization

in conjunction with IEEE VIS 2023 Melbourne, Australia
22-27 October 2023


Monday, October 23, 2023 (Australian Eastern Standard Time GMT+10)

9:00am – 9:10am Opening Remarks (Gunther Weber)
9:10am – 9:55am

Keynote Presentation

Ken Moreland
9:55am – 10:15am Best Paper
Speculative Progressive Raycasting for Memory Constrained Isosurface Visualization of Massive Volumes
Will Usher, Landon Dyken, Sidarth Kumar
10:15am – 10:45am Break
10:45am – 11:55pm

Papers Session

(Session Chair: Kristi Potter)
10:45-11:05 A Distributed-Memory Parallel Approach for Volume Rendering with Shadows
Manish Mathai, Mathew Larsen, Hank Childs
11:05-11:25 Towards Adaptive Refinement for Multivariate Functional Approximation of Scientific Data
Tom Peterka, David Lenz, Iulian Grindeanu, Vijay Mahadevan
11:25-11:40 Low-Cost Post Hoc Reconstruction of HPC Simulations at Full Resolution (Short Paper)
Ayman Yousef, Amanda Randles, Erik Draeger
11:40-11:55 Sub-Linear Time Sampling Approach for Large-Scale Data Visualization Using Reinforcement Learning (Short Paper)
Ayan Biswas, Arindam Bhattacharya, Yi-Tang Chen, Han-Wei Shen
11:45am – 12:00pm Closing Remarks (Silvio Rizzi)


Enabling Visualization at the Exascale with VTK-m
Ken Moreland, Oakridge National Laboratory

The last decade has seen a disruptive change in the construction of High-Performance Computing (HPC) systems. Driven by the economics of scaling up the compute throughput of these large devices, most of the largest HPC machines now leverage hardware accelerators, usually in the form of a GPU, that achieves high computational throughput through many coordinated parallel threads. The US Department of Energy’s Exascale Computing Program (ECP) invested heavily in updating HPC software to operate on these new HPC designs. The strategy for updating HPC visualization software centered around VTK-m, a flexible framework to simplify the implementation of visualization algorithms on GPUs and similar devices. This presentation discusses how VTK-m defines visualization algorithms, how these algorithms are ported across multiple platforms, and how VTK-m is integrated into distributed-parallel visualization software to address the largest scale visualization challenges once again.


Dr. Ken Moreland is a senior research scientist at Oak Ridge Laboratory. He received BS degrees in computer science and in electrical engineering from the New Mexico Institute of Mining and Technology in 1997. He received MS and Ph.D. degrees in computer science from the University of New Mexico in 2000 and 2004, respectively. Dr. Moreland specializes in large-scale visualization and graphics and has played an active role in the development of several HPC products including ParaView, VTK, IceT, Catalyst, Dax, and VTK-m.


Interactive Blood Flow Simulation With Deformable Cells and Walls
Nazariy Tishchenko, Nicola Ferrier, Joseph Insley, Victor A. Mateevitsi, Michael E. Papka, Silvio Rizzi, Jifu Tan

Topological Data Analysis of 3D Ablative Rayleigh-Taylor Instability Dataset for Automatic Segmentation
Fabien Vivodtzev, Alexis Casner, Laurent Masse, Luke Ceurvorst, Shahab Khan, Vladimir Smalyuk