Interactive Selection on Calculated Attributes of Large-Scale Particle Data

Abstract

We present a GPU-based technique for efficient selection in interactive visualizations of large particle datasets. In particular, we address multiple attributes attached to particles, such as pressure, density, or surface tension. Unfortunately, such intermediate attributes are often available only during the simulation run. They are either not accessible during visualization or have to be saved as additional information along with the usual simulation data. The latter increases the size of the dataset significantly, and the required variables may not be known in advance. Therefore, we choose to compute intermediate attributes on the fly. In this way, we are even able to obtain attributes that were not calculated by the simulation but may be relevant for data analysis or debugging. We present an interactive selection technique designed for such attributes. It leverages spatial regions of the selection to efficiently compute attributes only where needed. This lazy evaluation also works for intelligent and data-driven selection, extending the region to include neighboring particles. Our technique is evaluated by measurements of performance scalability and case studies for typical usage examples.