Asynchronous and Load-Balanced Union-Find for Distributed and Parallel Scientific Data Visualization and Analysis
Abstract
We present a novel distributed union-find algorithm that features asynchronous parallelism and k-d tree based load balancing for scalable visualization and analysis of scientific data. Applications of union-find include level set extraction and critical point tracking, but distributed union-find can suffer from high synchronization costs and imbalanced workloads across parallel processes. In this study, we prove that global synchronizations in existing distributed union-find can be eliminated without changing final results, allowing overlapped communications and computations for scalable processing. We also use a k-d tree decomposition to redistribute inputs, in order to improve workload balancing. We benchmark the scalability of our algorithm with up to 1,024 processes using both synthetic and application data. We demonstrate the use of our algorithm in critical point tracking and super-level set extraction with high-speed imaging experiments and fusion plasma simulations, respectively.
Cite
@article{arxiv.2003.02351,
title = {Asynchronous and Load-Balanced Union-Find for Distributed and Parallel Scientific Data Visualization and Analysis},
author = {Jiayi Xu and Hanqi Guo and Han-Wei Shen and Mukund Raj and Xueyun Wang and Xueqiao Xu and Zhehui Wang and Tom Peterka},
journal= {arXiv preprint arXiv:2003.02351},
year = {2022}
}
Comments
Accepted by IEEE Transactions on Visualization and Computer Graphics (IEEE TVCG Special Issue on IEEE PacificVis 2021)