Researchers at Argonne National Laboratory, together with several university colleagues, received awards at the 2013 IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV) held in Atlanta, Georgia, October 13-14.
The best paper award went to Nick Leaf, Venkatram Vishwanath, Joseph Insley, Mark Hereld, Michael E. Papka, and Kwan-Liu Ma for their paper “Efficient Parallel Volume Rendering of Large-Scale Adaptive Mesh Refinement Data.” The authors presented a cluster- and GPU-based parallel volume renderer and described a novel block decomposition method for improving rendering performance. The method was used in conjunction with the multiscale multiphysics code FLASH in order to gain insight into the evolution of a Type 1a supernova.
The best poster award went to Abon Chaudhuri, Teng-Yok Lee, Han-Wei Shen, and Tom Peterka for their poster “Efficient Range Distribution Query in Large-Scale Scientific Data.” Featured on the poster was a novel framework for precomputing and storing a set of data summaries that can be used to query any arbitrary region during postprocessing. The authors also presented an innovative encoding technique that utilizes the similarity present among different regions in the data, and hence, their respective distributions, in order to reduce the storage cost of the data structure.
The LDAV 2013 symposium, held in conjunction with IEEE Vis, brought together domain scientists, data analysts, visualization researchers, users, and designers to exchange ideas about the next-generation data-intensive analysis and visualization technology. Specific emphasis was on possible solutions, both short term and long term and including novel and even extreme methods for understanding and interacting with data.