Title: Multi-scale Morse Theory for Science Discovery and Scalable Tools For Large Data Processing and Visualization: From Access on Handheld Devices to In-Situ Analysis

Speaker: Chris Johnson, SCI Institute Director (Overview of SCI Institute) and Valerio Pascucci, SCI Institute (Technical Talks)      

Date/Time: Thursday, January 20, 2011, 10 – 11AM       

Location: CSRI Building/Room 90 (Sandia NM)

Brief Abstract: Advanced techniques for understanding the structure of large scale data models are a crucial ingredient for the success of any modern activity in information discovery and scientific investigation both associated with data generated by sensing devices of supercomputing hardware. Developing such techniques involves a number of major challenges in qualitative and quantitative analysis and tracking of features of unprecedented complexity. Addressing these challenges requires interdisciplinary research in diverse topics including the mathematical foundations of data representations, the design of robust, efficient algorithms, and the integration with relevant applications in physics, biology, or medicine.

In this short talk, I will present the application of a discrete topological framework for the representation and analysis of large scale scientific data. Due to the combinatorial nature of this framework, we can implement the core constructs of Morse theory without the approximations and instabilities of classical numerical techniques. We use topological cancellations to build multi-scale representations that capture local and global trends present in the data. The inherent robustness of our combinatorial algorithms allows us to address the high complexity of the feature extraction problem for high resolution scientific data.

During the talk, I will provide a live demonstration of some software tools developed in this effort.

Biography: Dr. Chris Johnson founded the SCI research group in 1992 which has since grown to become the SCI Institute employing over 150 faculty, staff and students. Professor Johnson serves on several international journal editorial boards, as well as on advisory boards to several national and international research centers.

Scalable Tools For Large Data Processing and Visualization: 
From Access on Handheld Devices to In-Situ Analysis

Abstract: Management of large streams of data is one the key capabilities needed in current and future data analysis and visualization activities for a large class of application spaces. Algorithms and software tools have to scale for a wide range of deployment targets ranging from handheld devices (iPhone/iPads), to desktop computers, large visualization servers, or executed in-situ together with large scale simulations.

In this short presentation I will discuss ongoing research in scalable algorithmic techniques that can be used to process large data in movement. The aim is to achieve efficiency in modern hardware architectures where data movements account for a large part of the performance costs. I will present preliminary results in application areas including (i) data streaming for remote rendering of large scientific models, (ii) image processing for digital photography, and (ii) parallel topological segmentation for in situ analysis of combustion simulations. I will give a live demonstration of the software tools that can be deployed showing a client side rendering of large models on an iPhone or web plug-in, as well as the stream processing of large scale panoramas on a laptop.

CSRI POC: David Rogers, 505-844-5323