We propose a method to automatically defeature a CAD model by detecting irrelevant features using a geometry-based size field and a method to remove the irrelevant features via facet-based operations on a discrete representation. A discrete B-Rep model is first created by obtaining a faceted representation of the CAD entities. The candidate facet entities are then marked for reduction by using a geometry-based size field. This is accomplished by estimating local mesh sizes based on geometric criteria. If the field value at a facet entity goes below a user specified threshold value then it is identified as an irrelevant feature and is marked for reduction. The reduction of marked facet entities is primarily performed using an edge collapse operator. Care is taken to retain a valid geometry and topology of the discrete model throughout the procedure. The original model is not altered as the defeaturing is performed on a separate discrete model. Associativity between the entities of the discrete model and that of original CAD model is maintained in order to decode the attributes and boundary conditions applied on the original CAD entities onto the mesh via the entities of the discrete model. Example models are presented to illustrate the effectiveness of the proposed approach.
Biofouling, the unwanted growth of biofilms on a surface, of water-treatment membranes negatively impacts in desalination and water treatment. With biofouling there is a decrease in permeate production, degradation of permeate water quality, and an increase in energy expenditure due to increased cross-flow pressure needed. To date, a universal successful and cost-effect method for controlling biofouling has not been implemented. The overall goal of the work described in this report was to use high-performance computing to direct polymer, material, and biological research to create the next generation of water-treatment membranes. Both physical (micromixers - UV-curable epoxy traces printed on the surface of a water-treatment membrane that promote chaotic mixing) and chemical (quaternary ammonium groups) modifications of the membranes for the purpose of increasing resistance to biofouling were evaluated. Creation of low-cost, efficient water-treatment membranes helps assure the availability of fresh water for human use, a growing need in both the U. S. and the world.
This report summarizes the Combinatorial Algebraic Topology: software, applications & algorithms workshop (CAT Workshop). The workshop was sponsored by the Computer Science Research Institute of Sandia National Laboratories. It was organized by CSRI staff members Scott Mitchell and Shawn Martin. It was held in Santa Fe, New Mexico, August 29-30. The CAT Workshop website has links to some of the talk slides and other information, http://www.cs.sandia.gov/CSRI/Workshops/2009/CAT/index.html. The purpose of the report is to summarize the discussions and recap the sessions. There is a special emphasis on technical areas that are ripe for further exploration, and the plans for follow-up amongst the workshop participants. The intended audiences are the workshop participants, other researchers in the area, and the workshop sponsors.
Red teams that address complex systems have rarely taken advantage of Modeling and Simulation (M&S) in a way that reproduces most or all of a red-blue team exchange within a computer. Chess programs, starting with IBM's Deep Blue, outperform humans in that red-blue interaction, so why shouldn't we think computers can outperform traditional red teams now or in the future? This and future position papers will explore possible ways to use M&S to augment or replace traditional red teams in some situations, the features Red Team M&S should possess, how one might connect live and simulated red teams, and existing tools in this domain.
Bioweapons and emerging infectious diseases pose formidable and growing threats to our national security. Rapid advances in biotechnology and the increasing efficiency of global transportation networks virtually guarantee that the United States will face potentially devastating infectious disease outbreaks caused by novel ('unknown') pathogens either intentionally or accidentally introduced into the population. Unfortunately, our nation's biodefense and public health infrastructure is primarily designed to handle previously characterized ('known') pathogens. While modern DNA assays can identify known pathogens quickly, identifying unknown pathogens currently depends upon slow, classical microbiological methods of isolation and culture that can take weeks to produce actionable information. In many scenarios that delay would be costly, in terms of casualties and economic damage; indeed, it can mean the difference between a manageable public health incident and a full-blown epidemic. To close this gap in our nation's biodefense capability, we will develop, validate, and optimize a system to extract nucleic acids from unknown pathogens present in clinical samples drawn from infected patients. This system will extract nucleic acids from a clinical sample, amplify pathogen and specific host response nucleic acid sequences. These sequences will then be suitable for ultra-high-throughput sequencing (UHTS) carried out by a third party. The data generated from UHTS will then be processed through a new data assimilation and Bioinformatic analysis pipeline that will allow us to characterize an unknown pathogen in hours to days instead of weeks to months. Our methods will require no a priori knowledge of the pathogen, and no isolation or culturing; therefore it will circumvent many of the major roadblocks confronting a clinical microbiologist or virologist when presented with an unknown or engineered pathogen.
This manual describes the use of the Xyce Parallel Electronic Simulator. Xyce has been designed as a SPICE-compatible, high-performance analog circuit simulator, and has been written to support the simulation needs of the Sandia National Laboratories electrical designers. This development has focused on improving capability over the current state-of-the-art in the following areas: (1) Capability to solve extremely large circuit problems by supporting large-scale parallel computing platforms (up to thousands of processors). Note that this includes support for most popular parallel and serial computers. (2) Improved performance for all numerical kernels (e.g., time integrator, nonlinear and linear solvers) through state-of-the-art algorithms and novel techniques. (3) Device models which are specifically tailored to meet Sandia's needs, including some radiation-aware devices (for Sandia users only). (4) Object-oriented code design and implementation using modern coding practices that ensure that the Xyce Parallel Electronic Simulator will be maintainable and extensible far into the future. Xyce is a parallel code in the most general sense of the phrase - a message passing parallel implementation - which allows it to run efficiently on the widest possible number of computing platforms. These include serial, shared-memory and distributed-memory parallel as well as heterogeneous platforms. Careful attention has been paid to the specific nature of circuit-simulation problems to ensure that optimal parallel efficiency is achieved as the number of processors grows. The development of Xyce provides a platform for computational research and development aimed specifically at the needs of the Laboratory. With Xyce, Sandia has an 'in-house' capability with which both new electrical (e.g., device model development) and algorithmic (e.g., faster time-integration methods, parallel solver algorithms) research and development can be performed. As a result, Xyce is a unique electrical simulation capability, designed to meet the unique needs of the laboratory.
This document is a reference guide to the Xyce Parallel Electronic Simulator, and is a companion document to the Xyce Users’ Guide. The focus of this document is (to the extent possible) exhaustively list device parameters, solver options, parser options, and other usage details of Xyce. This document is not intended to be a tutorial. Users who are new to circuit simulation are better served by the Xyce Users’ Guide.
Working with leading experts in the field of cognitive neuroscience and computational intelligence, SNL has developed a computational architecture that represents neurocognitive mechanisms associated with how humans remember experiences in their past. The architecture represents how knowledge is organized and updated through information from individual experiences (episodes) via the cortical-hippocampal declarative memory system. We compared the simulated behavioral characteristics with those of humans measured under well established experimental standards, controlling for unmodeled aspects of human processing, such as perception. We used this knowledge to create robust simulations of & human memory behaviors that should help move the scientific community closer to understanding how humans remember information. These behaviors were experimentally validated against actual human subjects, which was published. An important outcome of the validation process will be the joining of specific experimental testing procedures from the field of neuroscience with computational representations from the field of cognitive modeling and simulation.
The kinetic Monte Carlo method and its variants are powerful tools for modeling materials at the mesoscale, meaning at length and time scales in between the atomic and continuum. We have completed a 3 year LDRD project with the goal of developing a parallel kinetic Monte Carlo capability and applying it to materials modeling problems of interest to Sandia. In this report we give an overview of the methods and algorithms developed, and describe our new open-source code called SPPARKS, for Stochastic Parallel PARticle Kinetic Simulator. We also highlight the development of several Monte Carlo models in SPPARKS for specific materials modeling applications, including grain growth, bubble formation, diffusion in nanoporous materials, defect formation in erbium hydrides, and surface growth and evolution.
This report describes trans-organizational efforts to investigate the impact of chip multiprocessors (CMPs) on the performance of important Sandia application codes. The impact of CMPs on the performance and applicability of Sandia's system software was also investigated. The goal of the investigation was to make algorithmic and architectural recommendations for next generation platform acquisitions.
Currently, electrical power generation uses about 140 billion gallons of water per day accounting for over 39% of all freshwater withdrawals thus competing with irrigated agriculture as the leading user of water. Coupled to this water use is the required pumping, conveyance, treatment, storage and distribution of the water which requires on average 3% of all electric power generated. While water and energy use are tightly coupled, planning and management of these fundamental resources are rarely treated in an integrated fashion. Toward this need, a decision support framework has been developed that targets the shared needs of energy and water producers, resource managers, regulators, and decision makers at the federal, state and local levels. The framework integrates analysis and optimization capabilities to identify trade-offs, and 'best' alternatives among a broad list of energy/water options and objectives. The decision support framework is formulated in a modular architecture, facilitating tailored analyses over different geographical regions and scales (e.g., national, state, county, watershed, NERC region). An interactive interface allows direct control of the model and access to real-time results displayed as charts, graphs and maps. Ultimately, this open and interactive modeling framework provides a tool for evaluating competing policy and technical options relevant to the energy-water nexus.
Petaflops systems will have tens to hundreds of thousands of compute nodes which increases the likelihood of faults. Applications use checkpoint/restart to recover from these faults, but even under ideal conditions, applications running on more than 30,000 nodes will likely spend more than half of their total run time saving checkpoints, restarting, and redoing work that was lost. We created a library that performs redundant computations on additional nodes allocated to the application. An active node and its redundant partner form a node bundle which will only fail, and cause an application restart, when both nodes in the bundle fail. The goal of this library is to learn whether this can be done entirely at the user level, what requirements this library places on a Reliability, Availability, and Serviceability (RAS) system, and what its impact on performance and run time is. We find that our redundant MPI layer library imposes a relatively modest performance penalty for applications, but that it greatly reduces the number of applications interrupts. This reduction in interrupts leads to huge savings in restart and rework time. For large-scale applications the savings compensate for the performance loss and the additional nodes required for redundant computations.
Progress in MEMS fabrication has enabled a wide variety of force and displacement sensing devices to be constructed. One device under intense development at Sandia is a passive shock switch, described elsewhere (Mitchell 2008). A goal of all MEMS devices, including the shock switch, is to achieve a high degree of reliability. This, in turn, requires systematic methods for validating device performance during each iteration of design. Once a design is finalized, suitable tools are needed to provide quality assurance for manufactured devices. To ensure device performance, measurements on these devices must be traceable to NIST standards. In addition, accurate metrology of MEMS components is needed to validate mechanical models that are used to design devices to accelerate development and meet emerging needs. Progress towards a NIST-traceable calibration method is described for a next-generation, 2D Interfacial Force Microscope (IFM) for applications in MEMS metrology and qualification. Discussed are the results of screening several suitable calibration methods and the known sources of uncertainty in each method.
Advanced computing hardware and software written to exploit massively parallel architectures greatly facilitate the computation of extremely large problems. On the other hand, these tools, though enabling higher fidelity models, have often resulted in much longer run-times and turn-around-times in providing answers to engineering problems. The impediments include smaller elements and consequently smaller time steps, much larger systems of equations to solve, and the inclusion of nonlinearities that had been ignored in days when lower fidelity models were the norm. The research effort reported focuses on the accelerating the analysis process for structural dynamics though combinations of model reduction and mitigation of some factors that lead to over-meshing.
Shared libraries have become ubiquitous and are used to achieve great resource efficiencies on many platforms. The same properties that enable efficiencies on time-shared computers and convenience on small clusters prove to be great obstacles to scalability on large clusters and High Performance Computing platforms. In addition, Light Weight operating systems such as Catamount have historically not supported the use of shared libraries specifically because they hinder scalability. In this report we will outline the methods of supporting shared libraries on High Performance Computing platforms using Light Weight kernels that we investigated. The considerations necessary to evaluate utility in this area are many and sometimes conflicting. While our initial path forward has been determined based on this evaluation we consider this effort ongoing and remain prepared to re-evaluate any technology that might provide a scalable solution. This report is an evaluation of a range of possible methods of supporting dynamically linked executables on capability class1 High Performance Computing platforms. Efforts are ongoing and extensive testing at scale is necessary to evaluate performance. While performance is a critical driving factor, supporting whatever method is used in a production environment is an equally important and challenging task.
Application performance is determined by a combination of many choices: hardware platform, runtime environment, languages and compilers used, algorithm choice and implementation, and more. In this complicated environment, we find that the use of mini-applications - small self-contained proxies for real applications - is an excellent approach for rapidly exploring the parameter space of all these choices. Furthermore, use of mini-applications enriches the interaction between application, library and computer system developers by providing explicit functioning software and concrete performance results that lead to detailed, focused discussions of design trade-offs, algorithm choices and runtime performance issues. In this paper we discuss a collection of mini-applications and demonstrate how we use them to analyze and improve application performance on new and future computer platforms.
This report documents the results of an FY09 ASC V&V Methods level 2 milestone demonstrating new algorithmic capabilities for mixed aleatory-epistemic uncertainty quantification. Through the combination of stochastic expansions for computing aleatory statistics and interval optimization for computing epistemic bounds, mixed uncertainty analysis studies are shown to be more accurate and efficient than previously achievable. Part I of the report describes the algorithms and presents benchmark performance results. Part II applies these new algorithms to UQ analysis of radiation effects in electronic devices and circuits for the QASPR program.
Critical infrastructure resilience has become a national priority for the U. S. Department of Homeland Security. System resilience has been studied for several decades in many different disciplines, but no standards or unifying methods exist for critical infrastructure resilience analysis. Few quantitative resilience methods exist, and those existing approaches tend to be rather simplistic and, hence, not capable of sufficiently assessing all aspects of critical infrastructure resilience. This report documents the results of a late-start Laboratory Directed Research and Development (LDRD) project that investigated the development of quantitative resilience through application of control design methods. Specifically, we conducted a survey of infrastructure models to assess what types of control design might be applicable for critical infrastructure resilience assessment. As a result of this survey, we developed a decision process that directs the resilience analyst to the control method that is most likely applicable to the system under consideration. Furthermore, we developed optimal control strategies for two sets of representative infrastructure systems to demonstrate how control methods could be used to assess the resilience of the systems to catastrophic disruptions. We present recommendations for future work to continue the development of quantitative resilience analysis methods.
Data mining and machine learning techniques can be applied to computer system design to aid in optimizing design decisions, improving system runtime performance. Data mining techniques have been investigated in the context of branch prediction. Specifically, a comparison of traditional branch predictor performance has been made to data mining algorithms. Additionally, the possiblity of whether additional features available within the architectural state might serve to further improve branch prediction has been evaluated. Results show that data mining techniques indicate potential for improved branch prediction, especially when register file contents are included as a feature set.
The 9/30/2009 ASC Level 2 Scalable Analysis Tools for Sensitivity Analysis and UQ (Milestone 3160) contains feature recognition capability required by the user community for certain verification and validation tasks focused around sensitivity analysis and uncertainty quantification (UQ). These feature recognition capabilities include crater detection, characterization, and analysis from CTH simulation data; the ability to call fragment and crater identification code from within a CTH simulation; and the ability to output fragments in a geometric format that includes data values over the fragments. The feature recognition capabilities were tested extensively on sample and actual simulations. In addition, a number of stretch criteria were met including the ability to visualize CTH tracer particles and the ability to visualize output from within an S3D simulation.
The goal of this research was to combine theoretical and computational approaches to better understand the potential emergent behaviors of large-scale cyber systems, such as networks of {approx} 10{sup 6} computers. The scale and sophistication of modern computer software, hardware, and deployed networked systems have significantly exceeded the computational research community's ability to understand, model, and predict current and future behaviors. This predictive understanding, however, is critical to the development of new approaches for proactively designing new systems or enhancing existing systems with robustness to current and future cyber threats, including distributed malware such as botnets. We have developed preliminary theoretical and modeling capabilities that can ultimately answer questions such as: How would we reboot the Internet if it were taken down? Can we change network protocols to make them more secure without disrupting existing Internet connectivity and traffic flow? We have begun to address these issues by developing new capabilities for understanding and modeling Internet systems at scale. Specifically, we have addressed the need for scalable network simulation by carrying out emulations of a network with {approx} 10{sup 6} virtualized operating system instances on a high-performance computing cluster - a 'virtual Internet'. We have also explored mappings between previously studied emergent behaviors of complex systems and their potential cyber counterparts. Our results provide foundational capabilities for further research toward understanding the effects of complexity in cyber systems, to allow anticipating and thwarting hackers.
The purpose of this project was to investigate the use of Bayesian methods for the estimation of the reliability of complex systems. The goals were to find methods for dealing with continuous data, rather than simple pass/fail data; to avoid assumptions of specific probability distributions, especially Gaussian, or normal, distributions; to compute not only an estimate of the reliability of the system, but also a measure of the confidence in that estimate; to develop procedures to address time-dependent or aging aspects in such systems, and to use these models and results to derive optimal testing strategies. The system is assumed to be a system of systems, i.e., a system with discrete components that are themselves systems. Furthermore, the system is 'engineered' in the sense that each node is designed to do something and that we have a mathematical description of that process. In the time-dependent case, the assumption is that we have a general, nonlinear, time-dependent function describing the process. The major results of the project are described in this report. In summary, we developed a sophisticated mathematical framework based on modern probability theory and Bayesian analysis. This framework encompasses all aspects of epistemic uncertainty and easily incorporates steady-state and time-dependent systems. Based on Markov chain, Monte Carlo methods, we devised a computational strategy for general probability density estimation in the steady-state case. This enabled us to compute a distribution of the reliability from which many questions, including confidence, could be addressed. We then extended this to the time domain and implemented procedures to estimate the reliability over time, including the use of the method to predict the reliability at a future time. Finally, we used certain aspects of Bayesian decision analysis to create a novel method for determining an optimal testing strategy, e.g., we can estimate the 'best' location to take the next test to minimize the risk of making a wrong decision about the fitness of a system. We conclude this report by proposing additional fruitful areas of research.
We describe the implementation of a prototype fully implicit method for solving three-dimensional quasi-steady state magnetic advection-diffusion problems. This method allows us to solve the magnetic advection diffusion equations in an Eulerian frame with a fixed, user-prescribed velocity field. We have verified the correctness of method and implementation on two standard verification problems, the Solberg-White magnetic shear problem and the Perry-Jones-White rotating cylinder problem.
In this report we summarize research into new parallel algebraic multigrid (AMG) methods. We first provide a introduction to parallel AMG. We then discuss our research in parallel AMG algorithms for very large scale platforms. We detail significant improvements in the AMG setup phase to a matrix-matrix multiplication kernel. We present a smoothed aggregation AMG algorithm with fewer communication synchronization points, and discuss its links to domain decomposition methods. Finally, we discuss a multigrid smoothing technique that utilizes two message passing layers for use on multicore processors.
This report summarizes accomplishments of a three-year project focused on developing technical capabilities for measuring and modeling neuronal processes at the nanoscale. It was successfully demonstrated that nanoprobes could be engineered that were biocompatible, and could be biofunctionalized, that responded within the range of voltages typically associated with a neuronal action potential. Furthermore, the Xyce parallel circuit simulator was employed and models incorporated for simulating the ion channel and cable properties of neuronal membranes. The ultimate objective of the project had been to employ nanoprobes in vivo, with the nematode C elegans, and derive a simulation based on the resulting data. Techniques were developed allowing the nanoprobes to be injected into the nematode and the neuronal response recorded. To the authors's knowledge, this is the first occasion in which nanoparticles have been successfully employed as probes for recording neuronal response in an in vivo animal experimental protocol.
This report details the accomplishments of the 'Building More Powerful Less Expensive Supercomputers Using Processing-In-Memory (PIM)' LDRD ('PIM LDRD', number 105809) for FY07-FY09. Latency dominates all levels of supercomputer design. Within a node, increasing memory latency, relative to processor cycle time, limits CPU performance. Between nodes, the same increase in relative latency impacts scalability. Processing-In-Memory (PIM) is an architecture that directly addresses this problem using enhanced chip fabrication technology and machine organization. PIMs combine high-speed logic and dense, low-latency, high-bandwidth DRAM, and lightweight threads that tolerate latency by performing useful work during memory transactions. This work examines the potential of PIM-based architectures to support mission critical Sandia applications and an emerging class of more data intensive informatics applications. This work has resulted in a stronger architecture/implementation collaboration between 1400 and 1700. Additionally, key technology components have impacted vendor roadmaps, and we are in the process of pursuing these new collaborations. This work has the potential to impact future supercomputer design and construction, reducing power and increasing performance. This final report is organized as follow: this summary chapter discusses the impact of the project (Section 1), provides an enumeration of publications and other public discussion of the work (Section 1), and concludes with a discussion of future work and impact from the project (Section 1). The appendix contains reprints of the refereed publications resulting from this work.
Large relational datasets such as national-scale social networks and power grids present different computational challenges than do physical simulations. Sandia's distributed-memory supercomputers are well suited for solving problems concerning the latter, but not the former. The reason is that problems such as pattern recognition and knowledge discovery on large networks are dominated by memory latency and not by computation. Furthermore, most memory requests in these applications are very small, and when the datasets are large, most requests miss the cache. The result is extremely low utilization. We are unlikely to be able to grow out of this problem with conventional architectures. As the power density of microprocessors has approached that of a nuclear reactor in the past two years, we have seen a leveling of Moores Law. Building larger and larger microprocessor-based supercomputers is not a solution for informatics and network infrastructure problems since the additional processors are utilized to only a tiny fraction of their capacity. An alternative solution is to use the paradigm of massive multithreading with a large shared memory. There is only one instance of this paradigm today: the Cray MTA-2. The proposal team has unique experience with and access to this machine. The XMT, which is now being delivered, is a Red Storm machine with up to 8192 multithreaded 'Threadstorm' processors and 128 TB of shared memory. For many years, the XMT will be the only way to address very large graph problems efficiently, and future generations of supercomputers will include multithreaded processors. Roughly 10 MTA processor can process a simple short paths problem in the time taken by the Gordon Bell Prize-nominated distributed memory code on 32,000 processors of Blue Gene/Light. We have developed algorithms and open-source software for the XMT, and have modified that software to run some of these algorithms on other multithreaded platforms such as the Sun Niagara and Opteron multi-core chips.
Palacios and Kitten are new open source tools that enable applications, whether ported or not, to achieve scalable high performance on large machines. They provide a thin layer over the hardware to support both full-featured virtualized environments and native code bases. Kitten is an OS under development at Sandia that implements a lightweight kernel architecture to provide predictable behavior and increased flexibility on large machines, while also providing Linux binary compatibility. Palacios is a VMM that is under development at Northwestern University and the University of New Mexico. Palacios, which can be embedded into Kitten and other OSes, supports existing, unmodified applications and operating systems by using virtualization that leverages hardware technologies. We describe the design and implementation of both Kitten and Palacios. Our benchmarks show that they provide near native, scalable performance. Palacios and Kitten provide an incremental path to using supercomputer resources that is not performance-compromised.
