Will McLendon is a Senior Member of the Technical Staff in the Scalable Analysis
and Visualization group at Sandia National Laboratories in Albuquerque, NM.
His current research interests include large scale graph analysis,
geospatial-temporal analysis, remote sensing, and machine learning.
On the Use of Graph Search Techniques for the Analysis of Extreme-Scale Combustion Simulation Data
William C. McLendon III, Guarav Bansal, Peer-Timo Bremer, Jacqueline Chen, Hemanth Kolla, and Janine C. Bennett
Proceedings of the IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV'12), October, 2012
With the continuous increase in available computing resources, simulations are becoming
ever larger and more complex using bigger domains, tracking more species, and producing
more time steps. The resulting data represents both significant challenges to as well as
new opportunities for the visualization and data analysis community.
With the continuing increase in both spatial and temporal resolution, simulations now resolve highly localized and
intermittent structures and/or events such as the formation of ignition kernels.
However, such features may depend on multiple species in different parts of the domain at different time steps,
making their definition and extraction difficult. This paper introduces an intuitive framework to support the
identification, characterization, and tracking of such cross-species, cross-space, and cross-time features in large-scale
simulations. In a pre-processing step, we use topological techniques to create a set of hierarchical feature definitions
for each species of interest. Subsequently, we select a particular set of features for analysis and, using overlap-based
metrics, we generate an attributed relational graph (ARG) capturing the relationships between different variables both
within one and across multiple time steps. Finally, we leverage subgraph-isomorphism search heuristics to identify patterns
in the ARG that characterize interesting features. We demonstrate the power of this approach by analyzing a large-scale
turbulent combustion simulation.
Network Algorithms for Information Analysis Using the Titan Toolkit
William C. McLendon III, Timothy Shead, Andrew T. Wilson, Brian N. Wylie, and Jeffrey Baumes
IEEE International Carnahan Conference on Security Technology (ICCST), 2010, pp. 103-111
The analysis of networked activities is dramatically more challenging than many traditional kinds of analysis.
A network is defined by a set of entities (people, organizations, banks, computers, etc.) linked by various
types of relationships. These entities and relationships are often uninteresting alone, and only become
significant in aggregate. The analysis and visualization of these networks is one of the driving factors
behind the creation of the Titan Toolkit. Given the broad set of problem domains and the wide ranging
databases in use by the information analysis community, the Titan Toolkit's flexible, component based
pipeline provides an excellent platform for constructing specific combinations of network algorithms and
The Use of Electric Circuit Simulation for Power Grid Dynamics
D. Schoenwalkd, K. Munoz, W. McLendon III, and T. Russo
American Control Conference (ACC), 2011, pp. 1151-1156.
Traditional grid models for large-scale simulations assume linear and quasi-static behavior allowing very simple models
of the systems. In this paper, a scalable electric circuit simulation capability is presented that can capture a
significantly higher degree of fidelity including transient dynamic behavior of the grid as well as allowing scaling to
a regional and national level grid. A test case presented uses simple models, e.g. generators, transformers, transmission
lines, and loads, but with the scalability feature it can be extended to include more advanced non-linear detailed models.
The use of this scalable electric circuit simulator will provide the ability to conduct large-scale transient stability
analysis as well as grid level planning as the grid evolves with greater degrees of penetration of renewables, power
electronics, storage, distributed generation, and micro-grids.
Finding Strongly Connected Components in Distributed Graphs
William C. McLendon III, Bruce A. Hendrickson, Steven J. Plimpton, and Lawrence Rauchwerger
Journal of Parallel and Distributed Computing (JPDC), 2005, Vol 65(8), pp. 901-910
The traditional, serial, algorithm for finding the strongly connected components in a graph is based on depth first
search and has complexity which is linear in the size of the graph. Depth first search is difficult to parallelize,
which creates a need for a different parallel algorithm for this problem. We describe the implementation of a
recently proposed parallel algorithm that finds strongly connected components in distributed graphs, and discuss how
it is used in a radiation transport solver.