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Generating graphics to faithfully represent information can be a computationally intensive task. A way of using the Massively Parallel Processor to generate images by ray tracing is presented. This technique uses sort computation, a method of performing generalized routing interspersed with computation on a single-instruction-multiple-data (SIMD) computer.
The development and evaluation of the Knowledge Aided Display Design (KADD) system is described. Developed to investigate several designer support concepts in the context of the design of computer-generated displays, KADD's implementation uses technology from several disciplines of computer science including data base design and management, graphics, expert systems, and real-time simulation. This paper discusses KADD's goals and concepts, the implementation of the system, and the results of a two-part evaluation to determine the effectiveness of the KADD concepts.
A prototype simulation is being developed under contract to the Royal Aerospace Establishment (RAE), Farnborough, England, to assist in the discrimination of artificial space objects/debris. The methodology undertaken has been to link Object Oriented programming, intelligent knowledge based system (IKBS) techniques and advanced computer technology with numeric analysis to provide a graphical, symbolic simulation. The objective is to provide an additional layer of understanding on top of conventional classification methods. Use is being made of object and rule based knowledge representation, multiple reasoning, truth maintenance and uncertainty. Software tools being used include Knowledge Engineering Environment (KEE) and SymTactics for knowledge representation. Hooks are being developed within the SymTactics framework to incorporate mathematical models describing orbital motion and fragmentation. Penetration and structural analysis can also be incorporated. SymTactics is an Object Oriented discrete event simulation tool built as a domain specific extension to the KEE environment. The tool provides facilities for building, debugging and monitoring dynamic (military) simulations.
A finite-volume unstructured-grid FEM scheme with multistage Runge-Kutta time stepping is applied to the three-dimensional time-dependent Euler equations for inviscid flows on complex aircraft configurations undergoing structural deformation. The derivation of the model, the solution procedure, and the computer implementation are described, and results are presented graphically for a NASA Langley supersonic fighter aircraft model in steady and unsteady (harmonic oscillation in complete-vehicle bending mode) flow regimes. Good agreement between FEM predictions and experimental data is demonstrated.
This paper describes the implementation of a parallelized method of moments (MOM) code into an interactive workstation environment. The workstation allows interactive solid body modeling and mesh generation, MOM analysis, and the graphical display of results. After describing the parallel computing environment, the implementation and results of parallelizing a general MOM code are presented in detail.
The Research Institute for Advanced Computer Science (RIACS) was established by the Universities Space Research Association (USRA) at the NASA Ames Research Center (ARC) on 6 June 1983. RIACS is privately operated by USRA, a consortium of universities with research programs in the aerospace sciences, under a cooperative agreement with NASA. The primary mission of RIACS is to provide research and expertise in computer science and scientific computing to support the scientific missions of NASA ARC. The research carried out at RIACS must change its emphasis from year to year in response to NASA ARC's changing needs and technological opportunities. A flexible scientific staff is provided through a university faculty visitor program, a post doctoral program, and a student visitor program. Not only does this provide appropriate expertise but it also introduces scientists outside of NASA to NASA problems. A small group of core RIACS staff provides continuity and interacts with an ARC technical monitor and scientific advisory group to determine the RIACS mission. RIACS activities are reviewed and monitored by a USRA advisory council and ARC technical monitor. Research at RIACS is currently being done in the following areas: Parallel Computing; Advanced Methods for Scientific Computing; Learning Systems; High Performance Networks and Technology; Graphics, Visualization, and Virtual Environments.
3D graphics through the Internet needs to move beyond the current lowest common denominator of pre-computed movies, which consume bandwidth and are non-interactive. Panelists will demonstrate and compare 3D graphical tools for accessing, analyzing, and collaborating on information through the Internet and World-wide web. The "shoot-out" will illustrate which tools are likely to be the best for the various types of information, including dynamic scientific data, 3-D objects, and virtual environments. The goal of the panel is to encourage more effective use of the Internet by encouraging suppliers and users of information to adopt the next generation of graphical tools.
The International Space Station will soon provide an unparalleled research facility for studying the near- and longer-term effects of microgravity on living systems. Using the Space Station Glovebox Facility - a compact, fully contained reach-in environment - astronauts will conduct technically challenging life sciences experiments. Virtual environment technologies are being developed at NASA Ames Research Center to help realize the scientific potential of this unique resource by facilitating the experimental hardware and protocol designs and by assisting the astronauts in training. The Virtual GloveboX (VGX) integrates high-fidelity graphics, force-feedback devices and real- time computer simulation engines to achieve an immersive training environment. Here, we describe the prototype VGX system, the distributed processing architecture used in the simulation environment, and modifications to the visualization pipeline required to accommodate the display configuration.
A computer program enables generation, in the form of graphical representations of process flows with embedded natural-language policy statements, input to a suite of policy-, process-, and performance-based management (P3BM) software. This program (1) serves as an interface between users and the Hunter software, which translates the input into machine-readable form; and (2) enables users to initialize and monitor the policy-implementation process. This program provides an intuitive graphical interface for incorporating natural-language policy statements into business-process flow diagrams. Thus, the program enables users who dictate policies to intuitively embed their intended process flows as they state the policies, reducing the likelihood of errors and reducing the time between declaration and execution of policy.
Phase retrieval, the process of determining the exitpupil wavefront of an optical instrument from image-plane intensity measurements, is the baseline methodology for characterizing the wavefront for the suite of science instruments (SIs) in the Integrated Science Instrument Module (ISIM) for the James Webb Space Telescope (JWST). JWST is a large, infrared space telescope with a 6.5-meter diameter primary mirror. JWST is currently NASA's flagship mission and will be the premier space observatory of the next decade. ISIM contains four optical benches with nine unique instruments, including redundancies. ISIM was characterized at the Goddard Space Flight Center (GSFC) in Greenbelt, MD in a series of cryogenic vacuum tests using a telescope simulator. During these tests, phase-retrieval algorithms were used to characterize the instruments. The objective of this paper is to describe the Monte-Carlo simulations that were used to establish uncertainties (i.e., error bars) for the wavefronts of the various instruments in ISIM. Multiple retrieval algorithms were used in the analysis of ISIM phase-retrieval focus-sweep data, including an iterativetransform algorithm and a nonlinear optimization algorithm. These algorithms emphasize the recovery of numerous optical parameters, including low-order wavefront composition described by Zernike polynomial terms and high-order wavefront described by a point-by-point map, location of instrument best focus, focal ratio, exit-pupil amplitude, the morphology of any extended object, and optical jitter. The secondary objective of this paper is to report on the relative accuracies of these algorithms for the ISIM instrument tests, and a comparison of their computational complexity and their performance on central and graphical processing unit clusters. From a phase-retrieval perspective, the ISIM test campaign includes a variety of source illumination bandwidths, various image-plane sampling criteria above and below the Nyquist- Shannon critical sampling value, various extended object sizes, and several other impactful effects.
Accelerators, including Graphic Processing Units (GPUs) for gen- eral purpose computation, many-core designs with wide vector units (e.g., Intel Phi), have become a common component of many high performance clusters. The appearance of more stable and reliable tools tools that can automatically convert code written in high-level specifications with annotations (such as C or C++) to hardware de- scription languages (High-Level Synthesis - HLS), is also setting the stage for a broader use of reconfigurable devices (e.g., Field Pro- grammable Gate Arrays - FPGAs) in high performance system for the implementation of custom accelerators, helped by the fact that new processors include advanced cache-coherent interconnects for these components. In this chapter, we briefly survey the status of the use of accelerators in high performance systems targeted at big data analytics applications. We argue that, although the progress in the use of accelerators for this class of applications has been sig- nificant, differently from scientific simulations there still are gaps to close. This is particularly true for the ”irregular” behaviors exhibited by no-SQL, graph databases. We focus our attention on the limits of HLS tools for data analytics and graph methods, and discuss a new architectural template that better fits the requirement of this class of applications. We validate the new architectural templates by mod- ifying the Graph Engine for Multithreaded System (GEMS) frame- work to support accelerators generated with such a methodology, and testing with queries coming from the Lehigh University Benchmark (LUBM). The architectural template enables better supporting the task and memory level parallelism present in graph methods by sup- porting a new control model and a enhanced memory interface. We show that out solution allows generating parallel accelerators, pro- viding speed ups with respect to conventional HLS flows. We finally draw conclusions and present a perspective on the use of reconfig- urable devices and Design Automation tools for data analytics.
Los Alamos National Laboratory (LANL) is the primary plutonium science and manufacturing facility in the United States supporting multiple national security programs. It is responsible for completing a high variety of small-batch manufacturing tasks requiring the handling and precision machining of Special Nuclear Materials (SNM). Given the myriad of tasks that must be completed in a limited workspace, LANL intends to utilize flexible, multi-use robotic work-cells capable of completing a variety of mission critical tasks in a single glovebox with no re-tooling needed between batch jobs. This approach would allow LANL more flexibility in its production schedule and to meet its future production and research objectives. In this paper, we present a system that integrates multiple sensors and other manufacturing peripherals into existing glovebox infrastructure. Currently, a technician can place an object in the work area where vision sensors and supervisory software identify the object and its pose and automatically determine which manufacturing task to complete. Pose uncertainty is addressed in real-time by the task planner, eliminating the need for fixtures. Grasps are validated using pressure sensors, payload sensors, and grasp quality metrics to ensure that objects are not dropped or damaged during manipulation. The robot, sensors, and peripherals are controlled using open-source software built utilizing ROS (Robot Operating System), which minimizes redevelopment and allows for re-use and extensibility. Established capabilities support vision, collision-free path planning, and more, minimizing development time. For this effort, particular attention was given to the integration of general peripheral devices (drill, chuck, etc.), which is critical to efficiently utilizing ROS in manufacturing environments. This integration is achieved with an in-development framework that allows for quick integration of peripherals, including I/O devices and motors. Contact tasks utilize the Generalized Contact Control Framework (GCCF) previously developed at UT Austin. GCCF allows application developers to easily define multiple contact control tasks for a single robot. This paper summarizes the key software components necessary to develop the multi-use work-cell including existing ROS components. UT-developed software for grasp validation, peripheral integration, and contact control is presented in more depth. Finally the glovebox system is demonstrated completing a set of tasks with no fixturing necessary. Primary tasks include surface preparation/ablation, drilling, press-fitting, and sorting, as well as supporting material transfer and inspection tasks. The glovebox work-cell reduces cost, operator dose, ergonomic issues, and time loss due to material transfer between gloveboxes that only support one task. If multiple glovebox work-cells are in use, they have the potential to eliminate bottlenecks and scale production capabilities beyond current limits. (authors)
Kernel Density Estimators (KDEs) have recently been explored for use in Monte Carlo radiation transport simulations as an alternative to histogram tallies for capturing spatially-resolved quantities such as neutron flux and reaction rates. Histogram tallies suffer from large uncertainties when detailed spatial resolution of the quantity of interest is required; increasing the resolution of the underlying mesh increases the uncertainty in each histogram bin. KDEs obtain estimates of underlying densities on user-defined points, with the uncertainty at those points being independent of the spatial resolution desired since collisions and particle-track segments are allowed to contribute to the score at multiple tally points. Thus, KDEs show potential for obtaining estimates of a smoother spatially-resolved with reduced variance when compared to a histogram. However, this ability that enables KDEs to obtain lower uncertainties per particle history is more computationally expensive than using a traditional histogram bins on a structured mesh. While KDEs are capable of producing lower tally uncertainties compared to histogram binning for a given number of histories, their increased computational cost may make them less efficient. Even so, it is possible to reduce this additional cost by exporting the KDE tally routines onto Graphics Processing Units (GPUs). GPUs are commonly found on high-performance computing machines as a means of increasing performance without adding additional compute nodes. While it is difficult for existing Monte Carlo radiation transport codes to take advantage of GPUs without rewriting large portions of the code, it is possible to leverage GPUs through heterogeneous computing. This work uses a modified version of OpenMC and exports the KDE tally routines onto a GPU to obtain problem-dependent speedups ranging between 1.6 and 5.0 for the problem studied in this paper. While this methodology is applied to KDEs, the same ideas can be applied to other computationally expensive vectorizable portions of Monte Carlo algorithms. (authors)
The rapid growth and ubiquitous adoption of the internet and cyber-physical systems (CPS) have fundamentally transformed modern communication, work, and human-system interactions. While networks now form the backbone of critical digital ecosystems, enabling seamless data transmission across diverse, interconnected systems, this increased connectivity also expands the attack surface, making real-time detection of network intrusions and anomalies a pressing challenge. Detecting unusual activities within network infrastructure requires advanced data traffic analysis to differentiate between legitimate and malicious interactions. Traditional approaches to network anomaly detectionâ??such as rule-based and signature-based systemsâ??often depend on predefined patterns to identify known anomalies, limiting their effectiveness against emerging, stealthy, or previously unseen threats. These conventional methods suffer from high false alarm rates and fail to adapt to the ever-evolving nature of network traffic, particularly in large-scale, decentralized environments where data volume, velocity, and variety are constantly increasing. This dissertation presents artificial intelligence (AI)-driven approaches to anomaly detection that leverage graphics processing unit (GPU)-enabled high-performance computing (HPC) platforms for processing massive network traffic data and monitoring the components of cyber-physical systems (CPS) for potentially hazardous conditions. The research advances several key contributions: (1) Designing efficient machine learning techniques for CPS condition monitoring and anomaly detection; (2) enabling federated learning (FL) frameworks that enable distributed detection while preserving data privacy and system resilience; (3) exploring graph-based methodologies combining graph neural networks (GNN) and graph machine learning (ML) approaches for the Internet of Things (IoT) and automotive network security, and (4) performing distributed edge computing optimizations that integrate FL with scalable technologies for reduced communication overhead. Through extensive experiments, these methodologies demonstrate that complex anomaly detection and condition monitoring tasks can be achieved while balancing computational efficiency and detection accuracy through fine-grained network information processing. The frameworks developed in this research establish a robust foundation for network anomaly detection, providing scalable, adaptive, and privacy-preserving solutions for safeguarding CPS and IoT networks in an increasingly interconnected digital landscape. The practical implications of these research findings are significant, as they can inform the development of next-generation network security systems and contribute to the protection of critical infrastructure against sophisticated cyber attacks.
Computer programs were written to model the liquid oxygen loading system for the space shuttle. The programs allow selection of input data through graphic displays which schematically depict the part of the system being modeled. The computed output is also displayed in the form of graphs and printed messages. Any one of six computation options may be selected. The first four of these pertain to thermal stresses, pressure surges, cooldown times, flow rates and pressures during cooldown. Options five and six deal with possible water hammer effects due to closing of valves, steady flow and transient response to changes in operating conditions after cooldown. Procedures are given for operation of the graphic display unit and minicomputer.
The predominance of Kohn–Sham density functional theory (KS-DFT) for the theoretical treatment of large experimentally relevant systems in molecular chemistry and materials science relies primarily on the existence of efficient software implementations which are capable of leveraging the latest advances in modern high-performance computing (HPC). With recent trends in HPC leading toward increasing reliance on heterogeneous accelerator-based architectures such as graphics processing units (GPU), existing code bases must embrace these architectural advances to maintain the high levels of performance that have come to be expected for these methods. In this work, we purpose a three-level parallelism scheme for the distributed numerical integration of the exchange-correlation (XC) potential in the Gaussian basis set discretization of the Kohn–Sham equations on large computing clusters consisting of multiple GPUs per compute node. In addition, we purpose and demonstrate the efficacy of the use of batched kernels, including batched level-3 BLAS operations, in achieving high levels of performance on the GPU. We demonstrate the performance and scalability of the implementation of the purposed method in the NWChemEx software package by comparing to the existing scalable CPU XC integration in NWChem.
With the development of computer technology, computer‐aided drug design (CADD) has become an important means for drug research and development, and its representative method is virtual screening. Various virtual screening platforms have emerged in an endless stream and play great roles in the field of drug discovery. With the increasing number of compound molecules, virtual screening platforms face two challenges: low fluency and low visibility of software operations. In this article, we present an integrated and graphical drug design software eSHAFTS based on three‐dimensional (3D) molecular similarity to overcome these shortcomings. Compared with other software, eSHAFTS has four main advantages, which are integrated molecular editing and drawing, interactive loading and analysis of proteins, multithread and multimode 3D molecular similarity calculation, and multidimensional information visualization. Experiments have verified its convenience, usability, and reliability. By using eSHAFTS, various tasks can be submitted and visualized in one desktop software without locally installing any dependent plug‐ins or software. The software installation package can be downloaded for free at http://lilab.ecust.edu.cn/home/resource.html . © 2019 Wiley Periodicals, Inc.
Modern computer architectures reward added computation if it reduces algorithmic dependence, reduces data movement, increases accuracy/robustness, and improves memory accesses. The driving motive for this study is to develop a numerical algorithm that respects these constraints while improving accuracy and robustness. This study introduces the ADER-DT (Arbitrary DERivatives in time and space-differential transform) time discretization to positive-definite, weighted essentially nonoscillatory (WENO)-limited, finite volume transport on the cubed sphere in lieu of semidiscrete integrators. The cost of the ADER-DT algorithm is significantly improved from previous implementations without affecting accuracy. A new function-based WENO implementation is also detailed for use with the ADER-DT time discretization. While ADER-DT costs about 1.5 times more than a fourth-order, five-stage strong stability preserving Runge-Kutta (SSPRK4) method, it is far more computationally dense (which is advantageous on accelerators such as graphics processing units), and it has a larger effective maximum stable time step. ADER-DT errors converge more quickly with grid refinement than SSPRK4, giving 6.5 times less error in the L ∞ norm than SSPRK4 at the highest refinement level for smooth data. For nonsmooth data, ADER-DT resolves C 0 discontinuities more sharply. For a complex flow field, ADER exhibits less phase error than SSPRK4. In conclusion, improving both accuracy and robustness as well as better respecting modern computational efficiency requirements, we believe the method presented herein is competitive for efficiently transporting tracers over the sphere for applications targeting modern computing architectures.