High-Performance Distributed and Parallel Data Visualization
Visualizing scientific features from large-scale data can help scientists derive valuable insights effectively. As the scale of scientific data generated by experiments and simulations grows, distributed and parallel computing helps visualize large-scale data efficiently. The GRAVITY lab has designed various distributed and parallel algorithms to advance the high-performance data visualization field.Â
Publications:
Jiayi Xu, Hanqi Guo, Han-Wei Shen, Mukund Raj, Skylar W. Wurster, Tom Peterka: Reinforcement Learning for Load-balanced Parallel Particle Tracing, IEEE Transactions on Visualization and Computer Graphics 29(6): 3052-3066, 2023
Jiayi Xu, Hanqi Guo, Han-Wei Shen, Mukund Raj, Xueyun Wang, Xueqiao Xu, Zhehui Wang, Tom Peterka: Asynchronous and Load-Balanced Union-Find for Distributed and Parallel Scientific Data Visualization and Analysis, IEEE Transactions on Visualization and Computer Graphics 27 (6), 2808-2820 (2021) [Best Paper Award at IEEE PacificVis 2021 ]