Publications

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Active learning methods automatically adapt data collection by selecting the most informative samples in order to accelerate machine learning. Because of this, real-world testing and comparing active learning algorithms requires collecting new datasets (adaptively), rather than simply applying algorithms to benchmark datasets, as is the norm in (passive) machine learning research. To facilitate the development, testing and deployment of active learning for real applications, we have built an open-source software system for large-scale active learning research and experimentation. The system, called NEXT, provides a unique platform for realworld, reproducible active learning research. This paper details the challenges of building the system and demonstrates its capabilities with several experiments. The results show how experimentation can help expose strengths and weaknesses of active learning algorithms, in sometimes unexpected and enlightening ways.

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A fundamental challenge for supercomputer architecture is that processors cannot be fed data from DRAM as fast as CPUs can consume it. Therefore, many applications are memory-bandwidth bound. As the number of cores per chip increases, and traditional DDR DRAM speeds stagnate, the problem is only getting worse. A variety of non-DDR 3D memory technologies (Wide I/O 2, HBM) offer higher bandwidth and lower power by stacking DRAM chips on the processor or nearby on a silicon interposer. However, such a packaging scheme cannot contain sufficient memory capacity for a node. It seems likely that future systems will require at least two levels of main memory: high-bandwidth, low-power memory near the processor and low-bandwidth high-capacity memory further away. This near memory will probably not have significantly faster latency than the far memory. This, combined with the large size of the near memory (multiple GB) and power constraints, may make it difficult to treat it as a standard cache. In this paper, we explore some of the design space for a user-controlled multi-level main memory. We present algorithms designed for the heterogeneous bandwidth, using streaming to exploit data locality. We consider algorithms for the fundamental application of sorting. Our algorithms asymptotically reduce memory-block transfers under certain architectural parameter settings. We use and extend Sandia National Laboratories' SST simulation capability to demonstrate the relationship between increased bandwidth and improved algorithmic performance. Memory access counts from simulations corroborate predicted performance. This co-design effort suggests implementing two-level main memory systems may improve memory performance in fundamental applications.

This issue features expanded versions of articles selected from the 2014 AAAI Conference on Innovative Applications of Artificial Intelligence held in Quebec City, Canada. We present a selection of four articles describing deployed applications plus two more articles that discuss work on emerging applications.

Some next generation computing devices may consist of resistive memory arranged as a crossbar. Currently, the dominant approach is to use crossbars as the weight matrix of a neural network, and to use learning algorithms that require small incremental weight updates, such as gradient descent (for example Backpropagation). Using real-world measurements, we demonstrate that resistive memory devices are unlikely to support such learning methods. As an alternative, we offer a random search algorithm tailored to the measured characteristics of our devices.

The field of machine learning strives to develop algorithms that, through learning, lead to generalization; that is, the ability of a machine to perform a task that it was not explicitly trained for. An added challenge arises when the problem domain is dynamic or non-stationary with the data distributions or categorizations changing over time. This phenomenon is known as concept drift. Game-theoretic algorithms are often iterative by nature, consisting of repeated game play rather than a single interaction. Effectively, rather than requiring extensive retraining to update a learning model, a game-theoretic approach can adjust strategies as a novel approach to concept drift. In this paper we present a variant of our Support Vector Machine (SVM) Game classifier which may be used in an adaptive manner with repeated play to address concept drift, and show results of applying this algorithm to synthetic as well as real data.

In this report, we present a thermodynamic-­based model of hydride precipitation in Zr-based claddings. The model considers the state of the cladding immediately following drying, after removal from cooling-pools, and presents the evolution of precipitate formation upon cooling as follows: The pilgering process used to form Zr-based cladding imparts strong crystallographic and grain shape texture, with the basal plane of the hexagonal α-Zr grains being strongly aligned in the rolling-­direction and the grains are elongated with grain size being approximately twice as long parallel to the rolling direction, which is also the long axis of the tubular cladding, as it is in the orthogonal directions.

This project evaluates the effectiveness of moving target defense (MTD) techniques using a new game we have designed, called PLADD, inspired by the game FlipIt [28]. PLADD extends FlipIt by incorporating what we believe are key MTD concepts. We have analyzed PLADD and proven the existence of a defender strategy that pushes a rational attacker out of the game, demonstrated how limited the strategies available to an attacker are in PLADD, and derived analytic expressions for the expected utility of the game’s players in multiple game variants. We have created an algorithm for finding a defender’s optimal PLADD strategy. We show that in the special case of achieving deterrence in PLADD, MTD is not always cost effective and that its optimal deployment may shift abruptly from not using MTD at all to using it as aggressively as possible. We believe our effort provides basic, fundamental insights into the use of MTD, but conclude that a truly practical analysis requires model selection and calibration based on real scenarios and empirical data. We propose several avenues for further inquiry, including (1) agents with adaptive capabilities more reflective of real world adversaries, (2) the presence of multiple, heterogeneous adversaries, (3) computational game theory-based approaches such as coevolution to allow scaling to the real world beyond the limitations of analytical analysis and classical game theory, (4) mapping the game to real-world scenarios, (5) taking player risk into account when designing a strategy (in addition to expected payoff), (6) improving our understanding of the dynamic nature of MTD-inspired games by using a martingale representation, defensive forecasting, and techniques from signal processing, and (7) using adversarial games to develop inherently resilient cyber systems.

In this paper, we present a fully-coupled electrical and thermal transport model for oxide memristors that solves simultaneously the time-dependent continuity equations for all relevant carriers, together with the time-dependent heat equation including Joule heating sources. The model captures all the important processes that drive memristive switching and is applicable to simulate switching behavior in a wide range of oxide memristors. The model is applied to simulate the ON switching in a 3D filamentary TaOx memristor. Simulation results show that, for uniform vacancy density in the OFF state, vacancies fill in the conduction filament till saturation, and then fill out a gap formed in the Ta electrode during ON switching; furthermore, ON-switching time strongly depends on applied voltage and the ON-to-OFF current ratio is sensitive to the filament vacancy density in the OFF state.

This is a presentation outlining a lunch and learn lecture for the Structural Simulation Toolkit, supported by Sandia National Laboratories.

Sandia has approached the analysis of big datasets with an integrated methodology that uses computer science, image processing, and human factors to exploit critical patterns and relationships in large datasets despite the variety and rapidity of information. The work is part of a three-year LDRD Grand Challenge called PANTHER (Pattern ANalytics To support High-performance Exploitation and Reasoning). To maximize data analysis capability, Sandia pursued scientific advances across three key technical domains: (1) geospatial-temporal feature extraction via image segmentation and classification; (2) geospatial-temporal analysis capabilities tailored to identify and process new signatures more efficiently; and (3) domain- relevant models of human perception and cognition informing the design of analytic systems. Our integrated results include advances in geographical information systems (GIS) in which we discover activity patterns in noisy, spatial-temporal datasets using geospatial-temporal semantic graphs. We employed computational geometry and machine learning to allow us to extract and predict spatial-temporal patterns and outliers from large aircraft and maritime trajectory datasets. We automatically extracted static and ephemeral features from real, noisy synthetic aperture radar imagery for ingestion into a geospatial-temporal semantic graph. We worked with analysts and investigated analytic workflows to (1) determine how experiential knowledge evolves and is deployed in high-demand, high-throughput visual search workflows, and (2) better understand visual search performance and attention. Through PANTHER, Sandia's fundamental rethinking of key aspects of geospatial data analysis permits the extraction of much richer information from large amounts of data. The project results enable analysts to examine mountains of historical and current data that would otherwise go untouched, while also gaining meaningful, measurable, and defensible insights into overlooked relationships and patterns. The capability is directly relevant to the nation's nonproliferation remote-sensing activities and has broad national security applications for military and intelligence- gathering organizations.

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Large-scale reactive molecular dynamics (MD) simulations of shock compression in energetic materials provide new insight in to the microscopic mechanisms leading up to detonation.

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Scientific impact: The project supports the investigation of energetic materials. This work is providing fundamental insight into initiation mechanisms in energetic materials.

End of year summary including report on project milestones, project productivity, and next steps.

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