Authors
Oliver Rübel
Prabhat
Kesheng Wu
Hank Childs
Jeremy Meredith
Cameron G.R. Geddes
Estelle Cormier-Michel
Sean Ahern
Gunther H. Weber
Peter Messmer
Hans Hagen
Bernd Hamann
E. Wes Bethel
Abstract
Our work combines and extends techniques from high-performance scientific data management and visualization to enable scientific researchers to gain insight from extremely large, complex, timevarying laser wakefield particle accelerator simulation data. We extend histogram-based parallel coordinates for use in visual information display as well as an interface for guiding and performing data mining operations, which are based upon multi-dimensional and temporal thresholding and data subsetting operations. To achieve very high performance on parallel computing platforms, we leverage FastBit, a state-of-the-art index/query technology, to accelerate data mining and multi-dimensional histogram computation. We show how these techniques are used in practice by scientific researchers to identify, visualize and analyze a particle beam in a large, time-varying dataset.