  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.
