Information theory is widely studied and applied in various fields of computer science. Recently, it has been recognized for its potential to drive analysis and visualization of large scale scientific data. The theoretical results have motivated us to offer scientists the computing support necessary to leverage information theory for in situ analysis as well as for post-processing. Currently, we are in the process of developing a parallel C/C++ library called Information Theory Library (ITL) which provides kernels for computing various useful information theory metrics such as Shannon's entropy, conditional entropy and mutual information in a distributed environment. My talk will primarily focus on the key components of this library and its parallelization. In addition, our ongoing endeavor towards entropy-driven data organization for large vector and scalar field data will also be presented.

Bio:
Abon is a Ph.D. student in the Computer Science & Engineering department at The Ohio State University since 2006. His research interests include flow visualization, visual analytics of scientific data at large scale. He is also interested in certain aspects of information visualization and geovisualization. He is advised by Dr.Han-Wei Shen.