Monday, October 23, 2023 (Australian Eastern Standard Time GMT+10)
| 9:00am – 9:10am |
Opening Remarks (Gunther Weber) |
| 9:10am – 9:55am |
Keynote PresentationKen MorelandDetails |
| 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.
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