Publications

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The observation and characterization of a single atom system in silicon is a significant landmark in half a century of device miniaturization, and presents an important new laboratory for fundamental quantum and atomic physics. We compare with multi-million atom tight binding (TB) calculations the measurements of the spectrum of a single two-electron (2e) atom system in silicon - a negatively charged (D-) gated Arsenic donor in a FinFET. The TB method captures accurate single electron eigenstates of the device taking into account device geometry, donor potentials, applied fields, interfaces, and the full host bandstructure. In a previous work, the depths and fields of As donors in six device samples were established through excited state spectroscopy of the D0 electron and comparison with TB calculations. Using self-consistent field (SCF) TB, we computed the charging energies of the D- electron for the same six device samples, and found good agreement with the measurements. Although a bulk donor has only a bound singlet ground state and a charging energy of about 40 meV, calculations show that a gated donor near an interface can have a reduced charging energy and bound excited states in the D- spectrum. Measurements indeed reveal reduced charging energies and bound 2e excited states, at least one of which is a triplet. The calculations also show the influence of the host valley physics in the two-electron spectrum of the donor.

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Automated processing, modeling, and analysis of unstructured text (news documents, web content, journal articles, etc.) is a key task in many data analysis and decision making applications. As data sizes grow, scalability is essential for deep analysis. In many cases, documents are modeled as term or feature vectors and latent semantic analysis (LSA) is used to model latent, or hidden, relationships between documents and terms appearing in those documents. LSA supplies conceptual organization and analysis of document collections by modeling high-dimension feature vectors in many fewer dimensions. While past work on the scalability of LSA modeling has focused on the SVD, the goal of our work is to investigate the use of distributed memory architectures for the entire text analysis process, from data ingestion to semantic modeling and analysis. ParaText is a set of software components for distributed processing, modeling, and analysis of unstructured text. The ParaText source code is available under a BSD license, as an integral part of the Titan toolkit. ParaText components are chained-together into data-parallel pipelines that are replicated across processes on distributed-memory architectures. Individual components can be replaced or rewired to explore different computational strategies and implement new functionality. ParaText functionality can be embedded in applications on any platform using the native C++ API, Python, or Java. The ParaText MPI Process provides a 'generic' text analysis pipeline in a command-line executable that can be used for many serial and parallel analysis tasks. ParaText can also be deployed as a web service accessible via a RESTful (HTTP) API. In the web service configuration, any client can access the functionality provided by ParaText using commodity protocols ... from standard web browsers to custom clients written in any language.

While RAID is the prevailing method of creating reliable secondary storage infrastructure, many users desire more flexibility than offered by current implementations. To attain needed performance, customers have often sought after hardware-based RAID solutions. This talk describes a RAID system that offloads erasure correction coding calculations to GPUs, allowing increased reliability by supporting new RAID levels while maintaining high performance.

A multi-laboratory ontology construction effort during the summer and fall of 2009 prototyped an ontology for counterfeit semiconductor manufacturing. This effort included an ontology development team and an ontology validation methods team. Here the third team of the Ontology Project, the Data Analysis (DA) team reports on their approaches, the tools they used, and results for mining literature for terminology pertinent to counterfeit semiconductor manufacturing. A discussion of the value of ontology-based analysis is presented, with insights drawn from other ontology-based methods regularly used in the analysis of genomic experiments. Finally, suggestions for future work are offered.

After more than 50 years of molecular simulations, accurate empirical models are still the bottleneck in the wide adoption of simulation techniques. Addressing this issue with a fresh paradigm is the need of the day. In this study, we outline a new genetic-programming based method to develop empirical models for a system purely from its energy and/or forces. While the approach was initially developed for the development of classical force-fields from ab-initio calculations, we also discuss its application to the molecular coarse-graining of methanol. Two models, one representing methanol by a single site and the other via two sites will be developed using this method. They will be validated against existing coarse-grained potentials for methanol by comparing thermophysical properties.

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This brief paper explores the development of scalable, nonlinear, fully-implicit solution methods for a stabilized unstructured finite element (FE) discretization of the 2D incompressible (reduced) resistive MHD system. The discussion considers the stabilized FE formulation in context of a fully-implicit time integration and direct-to-steady-state solution capability. The nonlinear solver strategy employs Newton-Krylov methods, which are preconditioned using fully-coupled algebraic multilevel (AMG) techniques and a new approximate block factorization (ABF) preconditioner. The intent of these preconditioners is to enable robust, scalable and efficient solution approaches for the large-scale sparse linear systems generated by the Newton linearization. We present results for the fully-coupled AMG preconditioner for two prototype problems, a low Lundquist number MHD Faraday conduction pump and moderately-high Lundquist number incompressible magnetic island coalescence problem. For the MHD pump results we explore the scaling of the fully-coupled AMG preconditioner for up to 4096 processors for problems with up to 64M unknowns on a CrayXT3/4. Using the island coalescence problem we explore the weak scaling of the AMG preconditioner and the influence of the Lundquist number on the iteration count. Finally we present some very recent results for the algorithmic scaling of the ABF preconditioner.

Dynamical systems theory provides a powerful framework for understanding the behavior of complex evolving systems. However applying these ideas to large-scale dynamical systems such as discretizations of multi-dimensional PDEs is challenging. Such systems can easily give rise to problems with billions of dynamical variables, requiring specialized numerical algorithms implemented on high performance computing architectures with thousands of processors. This talk will describe LOCA, the Library of Continuation Algorithms, a suite of scalable continuation and bifurcation tools optimized for these types of systems that is part of the Trilinos software collection. In particular, we will describe continuation and bifurcation analysis techniques designed for large-scale dynamical systems that are based on specialized parallel linear algebra methods for solving augmented linear systems. We will also discuss several other Trilinos tools providing nonlinear solvers (NOX), eigensolvers (Anasazi), iterative linear solvers (AztecOO and Belos), preconditioners (Ifpack, ML, Amesos) and parallel linear algebra data structures (Epetra and Tpetra) that LOCA can leverage for efficient and scalable analysis of large-scale dynamical systems.

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Simulating the detailed evolution of microstructure at the mesoscale is increasingly being addressed by a number of methods. Discrete element modeling and Potts kinetic Monte Carlo have achieved success in capturing different aspects of sintering well. Discrete element cannot treat the details of neck formation and other shape evolution, especially when considering particles of arbitrary shapes. Potts kMC treats the micorstructural evolution very well, but cannot incorporate complex stress states that form especially during differential sintering. A model that is capable of simulating microstructural evolution during sintering at the mesoscale and can incorporate differential stresses is being developed. This multi-physics model that can treat both interfacial energies and the inter-particle stresses will be introduced. It will be applied to simulate microstructural evolution while resolving individual particles and the stresses that develop between them due to local shrinkage. Results will be presented and the future development of this model will be discussed.

The data in many disciplines such as social networks, web analysis, etc. is link-based, and the link structure can be exploited for many different data mining tasks. In this paper, we consider the problem of temporal link prediction: Given link data for time periods 1 through T, can we predict the links in time period T + 1? Specifically, we look at bipartite graphs changing over time and consider matrix- and tensor-based methods for predicting links. We present a weight-based method for collapsing multi-year data into a single matrix. We show how the well-known Katz method for link prediction can be extended to bipartite graphs and, moreover, approximated in a scalable way using a truncated singular value decomposition. Using a CANDECOMP/PARAFAC tensor decomposition of the data, we illustrate the usefulness of exploiting the natural three-dimensional structure of temporal link data. Through several numerical experiments, we demonstrate that both matrix- and tensor-based techniques are effective for temporal link prediction despite the inherent difficulty of the problem.

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Modeling the interaction of dislocations with internal boundaries and free surfaces is essential to understanding the effect of material microstructure on dislocation motion. However, discrete dislocation dynamics methods rely on infinite domain solutions of dislocation fields which makes modeling of heterogeneous materials difficult. A finite domain dislocation dynamics capability is under development that resolves both the dislocation array and polycrystalline structure in a compatible manner so that free surfaces and material interfaces are easily treated. In this approach the polycrystalline structure is accommodated using the GFEM, and the displacement due to the dislocation array is added to the displacement approximation. Shown in figure 1 are representative results from simulations of randomly placed and oriented dislocation sources in a cubic nickel polycrystal. Each grain has a randomly assigned (unique) material basis, and available glide planes are chosen accordingly. The change in basis between neighboring grains has an important effect on the motion of dislocations since the resolved shear on available glide planes can change dramatically. Dislocation transmission through high angle grain boundaries is assumed to occur by absorption into the boundary and subsequent nucleation in the neighboring grain. Such behavior is illustrated in figure 1d. Nucleation from the vertically oriented source in the bottom right grain is due to local stresses from dislocation pile-up in the neighboring grain. In this talk, the method and implementation is presented as well as some representative results from large scale (i.e., massively parallel) simulations of dislocation motion in cubic nano-domain nickel alloy. Particular attention will be paid to the effect of grain size on polycrystalline strength.

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As computational science applications grow more parallel with multi-core supercomputers having hundreds of thousands of computational cores, it will become increasingly difficult for solvers to scale. Our approach is to use hybrid MPI/threaded numerical algorithms to solve these systems in order to reduce the number of MPI tasks and increase the parallel efficiency of the algorithm. However, we need efficient threaded numerical kernels to run on the multi-core nodes in order to achieve good parallel efficiency. In this paper, we focus on improving the performance of a multithreaded triangular solver, an important kernel for preconditioning. We analyze three factors that affect the parallel performance of this threaded kernel and obtain good scalability on the multi-core nodes for a range of matrix sizes.

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In this presentation we examine the accuracy and performance of a suite of discrete-element-modeling approaches to predicting equilibrium and dynamic rheological properties of polystyrene suspensions. What distinguishes each approach presented is the methodology of handling the solvent hydrodynamics. Specifically, we compare stochastic rotation dynamics (SRD), fast lubrication dynamics (FLD) and dissipative particle dynamics (DPD). Method-to-method comparisons are made as well as comparisons with experimental data. Quantities examined are equilibrium structure properties (e.g. pair-distribution function), equilibrium dynamic properties (e.g. short- and long-time diffusivities), and dynamic response (e.g. steady shear viscosity). In all approaches we deploy the DLVO potential for colloid-colloid interactions. Comparisons are made over a range of volume fractions and salt concentrations. Our results reveal the utility of such methods for long-time diffusivity prediction can be dubious in certain ranges of volume fraction, and other discoveries regarding the best formulation to use in predicting rheological response.

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We present a shape-first approach to finding automobiles and trucks in overhead images and include results from our analysis of an image from the Overhead Imaging Research Dataset [1]. For the OIRDS, our shape-first approach traces candidate vehicle outlines by exploiting knowledge about an overhead image of a vehicle: a vehicle's outline fits into a rectangle, this rectangle is sized to allow vehicles to use local roads, and rectangles from two different vehicles are disjoint. Our shape-first approach can efficiently process high-resolution overhead imaging over wide areas to provide tips and cues for human analysts, or for subsequent automatic processing using machine learning or other analysis based on color, tone, pattern, texture, size, and/or location (shape first). In fact, computationally-intensive complex structural, syntactic, and statistical analysis may be possible when a shape-first work flow sends a list of specific tips and cues down a processing pipeline rather than sending the whole of wide area imaging information. This data flow may fit well when bandwidth is limited between computers delivering ad hoc image exploitation and an imaging sensor. As expected, our early computational experiments find that the shape-first processing stage appears to reliably detect rectangular shapes from vehicles. More intriguing is that our computational experiments with six-inch GSD OIRDS benchmark images show that the shape-first stage can be efficient, and that candidate vehicle locations corresponding to features that do not include vehicles are unlikely to trigger tips and cues. We found that stopping with just the shape-first list of candidate vehicle locations, and then solving a weighted, maximal independent vertex set problem to resolve conflicts among candidate vehicle locations, often correctly traces the vehicles in an OIRDS scene.

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Achieving the next three orders of magnitude performance increase to move from petascale to exascale computing will require a significant advancements in several fundamental areas. Recent studies have outlined many of the challenges in hardware and software that will be needed. In this paper, we examine these challenges with respect to high-performance networking. We describe the repercussions of anticipated changes to computing and networking hardware and discuss the impact that alternative parallel programming models will have on the network software stack. We also present some ideas on possible approaches that address some of these challenges.

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We present a new model for closing a system of Lagrangian hydrodynamics equations for a two-material cell with a single velocity model. We describe a new approach that is motivated by earlier work of Delov and Sadchikov and of Goncharov and Yanilkin. Using a linearized Riemann problem to initialize volume fraction changes, we require that each material satisfy its own pdV equation, which breaks the overall energy balance in the mixed cell. To enforce this balance, we redistribute the energy discrepancy by assuming that the corresponding pressure change in each material is equal. This multiple-material model is packaged as part of a two-step time integration scheme. We compare results of our approach with other models and with corresponding pure-material calculations, on two-material test problems with ideal-gas or stiffened-gas equations of state.

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We demonstrate a new semantic method for automatic analysis of wide-area, high-resolution overhead imagery to tip and cue human intelligence analysts to human activity. In the open demonstration, we find and trace cars and rooftops. Our methodology, extended to analysis of voxels, may be applicable to understanding morphology and to automatic tracing of neurons in large-scale, serial-section TEM datasets. We defined an algorithm and software implementation that efficiently finds all combinations of image blobs that satisfy given shape semantics, where image blobs are formed as a general-purpose, first step that 'oversegments' image pixels into blobs of similar pixels. We will demonstrate the remarkable power (ROC) of this combinatorial-based work flow for automatically tracing any automobiles in a scene by applying semantics that require a subset of image blobs to fill out a rectangular shape, with width and height in given intervals. In most applications we find that the new combinatorial-based work flow produces alternative (overlapping) tracings of possible objects (e.g. cars) in a scene. To force an estimation (tracing) of a consistent collection of objects (cars), a quick-and-simple greedy algorithm is often sufficient. We will demonstrate a more powerful resolution method: we produce a weighted graph from the conflicts in all of our enumerated hypotheses, and then solve a maximal independent vertex set problem on this graph to resolve conflicting hypotheses. This graph computation is almost certain to be necessary to adequately resolve multiple, conflicting neuron topologies into a set that is most consistent with a TEM dataset.

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Developing quantum chemistry programs on the coming generation of exascale computers will be a difficult task. The programs will need to be fault-tolerant and minimize the use of global operations. This work explores the use a task-based model that uses a data-centric approach to allocate work to different processes as it applies to quantum chemistry. After introducing the key problems that appear when trying to parallelize a complicated quantum chemistry method such as coupled-cluster theory, we discuss the implications of that model as it pertains to the computational kernel of a coupled-cluster program - matrix multiplication. Also, we discuss the extensions that would required to build a full coupled-cluster program using the task-based model. Current programming models for high-performance computing are fault-intolerant and use global operations. Those properties are unsustainable as computers scale to millions of CPUs; instead one must recognize that these systems will be hierarchical in structure, prone to constant faults, and global operations will be infeasible. The FAST-OS HARE project is introducing a scale-free computing model to address these issues. This model is hierarchical and fault-tolerant by design, allows for the clean overlap of computation and communication, reducing the network load, does not require checkpointing, and avoids the complexity of many HPC runtimes. Development of an algorithm within this model requires a change in focus from imperative programming to a data-centric approach. Quantum chemistry (QC) algorithms, in particular electronic structure methods, are an ideal test bed for this computing model. These methods describe the distribution of electrons in a molecule, which determine the properties of the molecule. The computational cost of these methods is high, scaling quartically or higher in the size of the molecule, which is why QC applications are major users of HPC resources. The complexity of these algorithms means that MPI alone is insufficient to achieve parallel scaling; QC developers have been forced to use alternative approaches to achieve scalability and would be receptive to radical shifts in the programming paradigm. Initial work in adapting the simplest QC method, Hartree-Fock, to this the new programming model indicates that the approach is beneficial for QC applications. However, the advantages to being able to scale to exascale computers are greatest for the computationally most expensive algorithms; within QC these are the high-accuracy coupled-cluster (CC) methods. Parallel coupledcluster programs are available, however they are based on the conventional MPI paradigm. Much of the effort is spent handling the complicated data dependencies between the various processors, especially as the size of the problem becomes large. The current paradigm will not survive the move to exascale computers. Here we discuss the initial steps toward designing and implementing a CC method within this model. First, we introduce the general concepts behind a CC method, focusing on the aspects that make these methods difficult to parallelize with conventional techniques. Then we outline what is the computational core of the CC method - a matrix multiply - within the task-based approach that the FAST-OS project is designed to take advantage of. Finally we outline the general setup to implement the simplest CC method in this model, linearized CC doubles (LinCC).

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