Synthesis of systems containing application-specific as well as reprogrammable
components, such as off-the-shelf microprocessors, provides a promising approach to …

We propose a dynamically load-balanced algorithm for parallel particle tracing, which
periodically attempts to evenly redistribute particles across processes based on kd tree …

Particle advection is the fundamental kernel behind most vector field visualization methods.
Yet, the efficient parallel computation of large amounts of particle traces remains …

In this work, we present a novel access pattern estimation approach for parallel particle
tracing in flow field visualization based on deep neural networks. With strong generalization …

When computing integral curves and integral surfaces for large-scale unsteady flow fields, a
major bottleneck is the widening gap between data access demands and the available …

We explore an online reinforcement learning (RL) paradigm to dynamically optimize parallel
particle tracing performance in distributed-memory systems. Our method combines three …

Stream surfaces and streamlines are two popular methods for visualizing three-dimensional
flow fields. While several parallel streamline computation algorithms exist, relatively little …

We present a novel high-order access dependencies-based model for efficient pathline
computation in unsteady flow visualization. By taking longer access sequences into account …

Particle tracing is a very important method in flow field data visualization and analysis. By
placing particle seeds in the flow domain and tracing the trajectory of each particle, users …

There are multiple algorithms for parallelizing particle advection for scientific visualization
workloads. While many previous studies have contributed to the understanding of individual …