In situ depth maps based feature extraction and tracking
Yucong Chris Ye, Yan Wang, Robert Miller, Kwan‐Liu Ma, Kenji Ono · 2015
Parallel numerical simulation is a powerful tool used by scientists to study complex problems. It has been a common practice to save the simulation output to disk and then conduct post-hoc in-depth analyses of the saved data. System I/O capabilities have not kept pace as simulations have scaled up over time, so a common approach has been to output only subsets of the data to reduce I/O. However, as we are entering the era of peta- and exa-scale computing, this sub-sampling approach is no longer acceptable because too much valuable information is lost. In situ visualization has been shown a promising approach to the data problem at extreme-scale. We present a novel in situ solution using depth maps to enable post-hoc image-based visualization and feature extraction and tracking. An interactive interface is provided to allow for fine-tuning the generation of depth maps during the course of a simulation run to better capture the features of interest. We use several applications including one actual simulation run on a Cray XE6 supercomputer to demonstrate the effectiveness of our approach.