Publications

Chance, Frances S.

Abstract not provided.

Tran, Anh; Tranchida, Julien T.

Abstract not provided.

Schleife, Andre S.; Baczewski, Andrew D.; Hansen, Stephanie B.; Kononov, Alina K.

Abstract not provided.

Laird, Carl L.; Jalving, Jordan H.; Blakely, Logan; Eydenberg, Michael S.; Boukouvala, Fani B.; Kilwein, Zachary K.

Abstract not provided.

Younge, Andrew J.

Abstract not provided.

Leger, Michelle A.; Darling, Michael C.; Jones, Stephen T.; Matzen, Laura E.; Stracuzzi, David J.; Wilson, Andrew T.; Bueno, Denis B.; Christentsen, Matthew C.; Ginaldi, Melissa J.; Hannasch, David A.; Heidbrink, Scott H.; Howell, Breannan C.; Leger, Chris; Reedy, Geoffrey E.; Rogers, Alisa N.; Williams, Jack A.

Reverse engineering (RE) analysts struggle to address critical questions about the safety of binary code accurately and promptly, and their supporting program analysis tools are simply wrong sometimes. The analysis tools have to approximate in order to provide any information at all, but this means that they introduce uncertainty into their results. And those uncertainties chain from analysis to analysis. We hypothesize that exposing sources, impacts, and control of uncertainty to human binary analysts will allow the analysts to approach their hardest problems with high-powered analytic techniques that they know when to trust. Combining expertise in binary analysis algorithms, human cognition, uncertainty quantification, verification and validation, and visualization, we pursue research that should benefit binary software analysis efforts across the board. We find a strong analogy between RE and exploratory data analysis (EDA); we begin to characterize sources and types of uncertainty found in practice in RE (both in the process and in supporting analyses); we explore a domain-specific focus on uncertainty in pointer analysis, showing that more precise models do help analysts answer small information flow questions faster and more accurately; and we test a general population with domain-general sudoku problems, showing that adding "knobs" to an analysis does not significantly slow down performance. This document describes our explorations in uncertainty in binary analysis.

Rawlings, Edna S.; Susarla, Naresh S.; Ghouse, Jaffer H.; Laird, Carl L.; Zamarripa, Miguel A.; Bynum, Michael L.; Siirola, John D.; Miller, David A.

Abstract not provided.

Willenbring, James M.

Abstract not provided.

Mundt, Miranda M.; Milewicz, Reed M.

Abstract not provided.

Milewicz, Reed M.; Mundt, Miranda M.

Abstract not provided.

Cusentino, Mary A.; Wood, Mitchell A.; Thompson, Aidan P.

Abstract not provided.

Crockatt, Michael M.; Shadid, John N.; Pawlowski, Roger P.; Conde, Sidafa C.; Mabuza, Sibu M.; Bonilla, Jesus B.

Abstract not provided.

Steiner, Adam M.; Siefert, Christopher S.; Shipley, Gabriel A.; Redline, Erica M.; Dickens, Sara D.; Jaramillo, Rex J.; Chavez, Tom C.; Hutsel, Brian T.; Frye-Mason, Gregory C.; Peterson, Kyle J.; Bell, Kate S.; Balogun, Shuaib A.; Losego, Mark D.; Sammeth, Torin M.; Kern, Ian J.; Harjes, Cameron D.; Gilmore, Mark A.; Lehr, Jane M.

Abstract not provided.

Rajamanickam, Sivasankaran R.; Rajamanickam, Sivasankaran R.

Abstract not provided.

Mundt, Miranda R.

Abstract not provided.

Krygier, Michael K.; Torczynski, J.R.; Gallis, Michail A.

Abstract not provided.

Adams, Brian H.; Bohnhoff, William B.; Dalbey, Keith D.; Ebeida, Mohamed S.; Eddy, John E.; Eldred, Michael E.; Hooper, Russell H.; Hough, Patricia H.; Hu, Kenneth H.; Jakeman, John J.; Khalil, Mohammad K.; Maupin, Kathryn M.; Monschke, Jason A.; Ridgway, Elliott R.; Rushdi, Ahmad A.; Seidl, Daniel S.; Stephens, John A.; Swiler, Laura P.; Tran, Anh; Winokur, Justin W.

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

Smith, John D.; Galvan, Edgar

The Computer Science Research Institute (CSRI) brings university faculty and students to Sandia National Laboratories for focused collaborative research on Department of Energy (DOE) computer and computational science problems. The institute provides an opportunity for university researches to learn about problems in computer and computational science at DOE laboratories, and help transfer results of their research to programs at the labs. Some specific CSRI research interest areas are: scalable solvers, optimization, algebraic preconditioners, graph-based, discrete, and combinatorial algorithms, uncertainty estimation, validation and verification methods, mesh generation, dynamic load-balancing, virus and other malicious-code defense, visualization, scalable cluster computers, beyond Moore’s Law computing, exascale computing tools and application design, reduced order and multiscale modeling, parallel input/output, and theoretical computer science. The CSRI Summer Program is organized by CSRI and includes a weekly seminar series and the publication of a summer proceedings.

Safta, Cosmin S.; Najm, H.N.; Diaz-Ibarra, Oscar H.; Kim, Kyungjoo K.

The TChem open-source software is a toolkit for computing thermodynamic properties, source term, and source term’s Jacobian matrix for chemical kinetic models that involve gas and surface reactions.

Hentschel, Thomas W.; Kononov, Alina K.; Baczewski, Andrew D.; Cangi, Attila -.; Hansen, Stephanie B.

Abstract not provided.

Silling, Stewart A.

Abstract not provided.

Smith, John D.; Galvan, Edgar

The Computer Science Research Institute (CSRI) brings university faculty and students to Sandia National Laboratories for focused collaborative research on Department of Energy (DOE) computer and computational science problems. The institute provides an opportunity for university researches to learn about problems in computer and computational science at DOE laboratories, and help transfer results of their research to programs at the labs. Some specific CSRI research interest areas are: scalable solvers, optimization, algebraic preconditioners, graph-based, discrete, and combinatorial algorithms, uncertainty estimation, validation and verification methods, mesh generation, dynamic load-balancing, virus and other malicious-code defense, visualization, scalable cluster computers, beyond Moore’s Law computing, exascale computing tools and application design, reduced order and multiscale modeling, parallel input/output, and theoretical computer science. The CSRI Summer Program is organized by CSRI and includes a weekly seminar series and the publication of a summer proceedings.

Swiler, Laura P.

Abstract not provided.

Nichol, Jeffrey N.

Abstract not provided.

Pawlowski, Roger P.

Abstract not provided.