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Advanced Computing Architecture Challenges for the Mercury Monte Carlo Particle Transport Project
Abstract not provided
Improving Performance of Uncertainty Quantification Methods on Advanced Computing Architectures.
Abstract not provided.
LLNL Monte Carlo Transport Research Efforts for Advanced Computing Architectures
Abstract not provided
A Comparison of Algebraic Multigrid Preconditioning Approaches for Sampling-Based Uncertainty Propagation on Advanced Computing Architectures.
Abstract not provided.
Improving Performance of Sampling-Based Uncertainty Quantification on Advanced Computing Architectures Through Embedded Ensemble Propagation.
Abstract not provided.
Investigating the Role of Polymer Architecture in Poly(methyl methacrylate) Depolymerization
Advanced architectures in polymers have garnered traction within the last two decades due to their distinct and tunable properties compared to linear analogs. However, the effects of architecture on nascent polymethacrylate depolymerization strategies remain underexplored. Herein, we investigate the depolymerization behavior of poly(methacrylate)-based star copolymers synthesized via a core-first approach. By incorporating either labile chain-end or pendent-group triggers, we demonstrate the first direct comparison of bulk depolymerization behaviors in star versus linear methacrylate copolymers under matched conditions. While pendent-triggered systems required higher loadings of N-(methacryloxy)phthalimide methacrylate (PhthMA) to achieve similar mass loss compared to linear copolymers, chain-end-initiated depolymerization showed enhanced efficiency of monomer liberation in the star topology. Furthermore, these findings highlight the importance of considering macromolecular architecture in designing sustainable polymers and provide actionable guidelines for installing depolymerization triggers based on polymer topology.
MABAL: a Novel Deep-Learning Architecture for Machine-Assisted Bone Age Labeling
Bone age assessment (BAA) is a commonly performed diagnostic study in pediatric radiology to assess skeletal maturity. The most commonly utilized method for assessment of BAA is the Greulich and Pyle method (Pediatr Radiol 46.9:1269–1274, 2016; Arch Dis Child 81.2:172–173, 1999) atlas. The evaluation of BAA can be a tedious and time-consuming process for the radiologist. As such, several computer-assisted detection/diagnosis (CAD) methods have been proposed for automation of BAA. Classical CAD tools have traditionally relied on hard-coded algorithmic features for BAA which suffer from a variety of drawbacks. Recently, the advent and proliferation of convolutional neural networks (CNNs) has shown promise in a variety of medical imaging applications. There have been at least two published applications of using deep learning for evaluation of bone age (Med Image Anal 36:41–51, 2017; JDI 1–5, 2017). However, current implementations are limited by a combination of both architecture design and relatively small datasets. The purpose of this study is to demonstrate the benefits of a customized neural network algorithm carefully calibrated to the evaluation of bone age utilizing a relatively large institutional dataset. In doing so, this study will aim to show that advanced architectures can be successfully trained from scratch in the medical imaging domain and can generate results that outperform any existing proposed algorithm. The training data consisted of 10,289 images of different skeletal age examinations, 8909 from the hospital Picture Archiving and Communication System at our institution and 1383 from the public Digital Hand Atlas Database. The data was separated into four cohorts, one each for male and female children above the age of 8, and one each for male and female children below the age of 10. The testing set consisted of 20 radiographs of each 1-year-age cohort from 0 to 1 years to 14–15+ years, half male and half female. The testing set included left-hand radiographs done for bone age assessment, trauma evaluation without significant findings, and skeletal surveys. A 14 hidden layer-customized neural network was designed for this study. The network included several state of the art techniques including residual-style connections, inception layers, and spatial transformer layers. Data augmentation was applied to the network inputs to prevent overfitting. A linear regression output was utilized. Mean square error was used as the network loss function and mean absolute error (MAE) was utilized as the primary performance metric. MAE accuracies on the validation and test sets for young females were 0.654 and 0.561 respectively. For older females, validation and test accuracies were 0.662 and 0.497 respectively. For young males, validation and test accuracies were 0.649 and 0.585 respectively. Finally, for older males, validation and test set accuracies were 0.581 and 0.501 respectively. The female cohorts were trained for 900 epochs each and the male cohorts were trained for 600 epochs. An eightfold cross-validation set was employed for hyperparameter tuning. Test error was obtained after training on a full data set with the selected hyperparameters. Using our proposed customized neural network architecture on our large available data, we achieved an aggregate validation and test set mean absolute errors of 0.637 and 0.536 respectively. To date, this is the best published performance on utilizing deep learning for bone age assessment. Our results support our initial hypothesis that customized, purpose-built neural networks provide improved performance over networks derived from pre-trained imaging data sets. We build on that initial work by showing that the addition of state-of-the-art techniques such as residual connections and inception architecture further improves prediction accuracy. This is important because the current assumption for use of residual and/or inception architectures is that a large pre-trained network is required for successful implementation given the relatively small datasets in medical imaging. Instead we show that a small, customized architecture incorporating advanced CNN strategies can indeed be trained from scratch, yielding significant improvements in algorithm accuracy. It should be noted that for all four cohorts, testing error outperformed validation error. One reason for this is that our ground truth for our test set was obtained by averaging two pediatric radiologist reads compared to our training data for which only a single read was used. This suggests that despite relatively noisy training data, the algorithm could successfully model the variation between observers and generate estimates that are close to the expected ground truth.
Advanced information processing system for advanced launch system: Avionics architecture synthesis
The Advanced Information Processing System (AIPS) is a fault-tolerant distributed computer system architecture that was developed to meet the real time computational needs of advanced aerospace vehicles. One such vehicle is the Advanced Launch System (ALS) being developed jointly by NASA and the Department of Defense to launch heavy payloads into low earth orbit at one tenth the cost (per pound of payload) of the current launch vehicles. An avionics architecture that utilizes the AIPS hardware and software building blocks was synthesized for ALS. The AIPS for ALS architecture synthesis process starting with the ALS mission requirements and ending with an analysis of the candidate ALS avionics architecture is described.
High temperature study of flexible silicon-on-insulator fin field-effect transistors
We report high temperature electrical transport characteristics of a flexible version of the semiconductor industry's most advanced architecture: fin field-effect transistor on silicon-on-insulator with sub-20 nm fins and high-κ/metal gate stacks. Characterization from room to high temperature (150 °C) was completed to determine temperature dependence of drain current (I{sub ds}), gate leakage current (I{sub gs}), transconductance (g{sub m}), and extracted low-field mobility (μ{sub 0}). Mobility degradation with temperature is mainly caused by phonon scattering. The other device characteristics show insignificant difference at high temperature which proves the suitability of inorganic flexible electronics with advanced device architecture.
General-Purpose Electronic System Tests Aircraft
Versatile digital equipment supports research, development, and maintenance. Extended aircraft interrogation and display system is general-purpose assembly of digital electronic equipment on ground for testing of digital electronic systems on advanced aircraft. Many advanced features, including multiple 16-bit microprocessors, pipeline data-flow architecture, advanced operating system, and resident software-development tools. Basic collection of software includes program for handling many types of data and for displays in various formats. User easily extends basic software library. Hardware and software interfaces to subsystems provided by user designed for flexibility in configuration to meet user's requirements.
NASA IVHM Technology Experiment for X-vehicles (NITEX)
The purpose of the NASA IVHM Technology Experiment for X-vehicles (NITEX) is to advance the development of selected IVHM technologies in a flight environment and to demonstrate the potential for reusable launch vehicle ground processing savings. The technologies to be developed and demonstrated include system-level and detailed diagnostics for real-time fault detection and isolation, prognostics for fault prediction, automated maintenance planning based on diagnostic and prognostic results, and a microelectronics hardware platform. Complete flight The Evolution of Flexible Insulation as IVHM consists of advanced sensors, distributed data acquisition, data processing that includes model-based diagnostics, prognostics and vehicle autonomy for control or suggested action, and advanced data storage. Complete ground IVHM consists of evolved control room architectures, advanced applications including automated maintenance planning and automated ground support equipment. This experiment will advance the development of a subset of complete IVHM.
AIAA/NASA International Symposium on Space Information Systems, 2nd, Pasadena, CA, Sept. 17-19, 1990, Proceedings. Vols. 1 & 2
These proceedings overview major space information system projects and lessons learned from current missions. Other topics include the science information system requirements for the 1990s, an information systems design approach for major programs, the technology needs and projections, the standards for space data information systems, the artificial intelligence technology and applications, international interoperability, and spacecraft data systems and architectures advanced communications. Other topics include the software engineering technology and applications, the multimission multidiscipline information system architectures, the distributed planning and scheduling systems and operations, and the computer and information systems architectures. Paper presented include prospects for scientific data analysis systems for solar-terrestrial physics in the 1990s, the Columbus data management system, data storage technologies for the future, the German aerospace research establishment, and launching artificial intelligence in NASA ground systems.
A direct-execution parallel architecture for the Advanced Continuous Simulation Language (ACSL)
A direct-execution parallel architecture for the Advanced Continuous Simulation Language (ACSL) is presented which overcomes the traditional disadvantages of simulations executed on a digital computer. The incorporation of parallel processing allows the mapping of simulations into a digital computer to be done in the same inherently parallel manner as they are currently mapped onto an analog computer. The direct-execution format maximizes the efficiency of the executed code since the need for a high level language compiler is eliminated. Resolution is greatly increased over that which is available with an analog computer without the sacrifice in execution speed normally expected with digitial computer simulations. Although this report covers all aspects of the new architecture, key emphasis is placed on the processing element configuration and the microprogramming of the ACLS constructs. The execution times for all ACLS constructs are computed using a model of a processing element based on the AMD 29000 CPU and the AMD 29027 FPU. The increase in execution speed provided by parallel processing is exemplified by comparing the derived execution times of two ACSL programs with the execution times for the same programs executed on a similar sequential architecture.
Overview of NASA Langley's Systems Analysis Capabilities
The Systems Analysis and Concepts Directorate (SACD) has been in the systems analysis business line supporting National Aeronautics and Space Administration (NASA) aeronautics, exploration, space operations and science since the 1960 s. Our current organization structure is shown in Figure 1. SACD mission can be summed up in the following statements: 1. We conduct advanced concepts for Agency decision makers and programs. 2. We provide aerospace systems analysis products such as mission architectures, advanced system concepts, system and technology trades, life cycle cost and risk analysis, system integration and pre-decisional sensitive information. 3. Our work enables informed technical, programmatic and budgetary decisions. SACD has a complement of 114 government employees and approximately 50 on-site contractors which is equally split between supporting aeronautics and exploration. SACD strives for technical excellence and creditability of the systems analysis products delivered to its customers. The Directorate office is continuously building market intelligence and working with other NASA centers and external partners to expand our business base. The Branches strive for technical excellence and credibility of our systems analysis products by seeking out existing and new partnerships that are critical for successful systems analysis. The Directorates long term goal is to grow the amount of science systems analysis business base.
A system architecture for an advanced Canadian wideband mobile satellite system
In this paper, the system architecture for an advanced Canadian ka-band geostationary mobile satellite system is described, utilizing hopping spot beams to support a 256 kbps wideband service for both N-ISDN and packet-switched interconnectivity to small briefcase-size portable and mobile terminals. An assessment is given of the technical feasibility of the satellite payload and terminal design in the post year 2000 timeframe. The satellite payload includes regeneration and on-board switching to permit single hop interconnectivity between mobile terminals. The mobile terminal requires antenna tracking and platform stabilization to ensure acquisition of the satellite signal. The potential user applications targeted for this wideband service includes: home-office, multimedia, desk-top (PC) videoconferencing, digital audio broadcasting, single and multi-user personal communications.
On the Efficient Evaluation of the Exchange Correlation Potential on Graphics Processing Unit Clusters
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.
XACC: a system-level software infrastructure for heterogeneous quantum–classical computing
Quantum programming techniques and software have advanced significantly over the past five years, with a majority focusing on high-level language frameworks targeting remote REST library APIs. As quantum computing architectures advance and become more widely available, lower-level, system software infrastructures will be needed to enable tighter, co-processor programming and access models. In this work, we present XACC, a system-level software infrastructure for quantum–classical computing that promotes a service-oriented architecture to expose interfaces for core quantum programming, compilation, and execution tasks. Additionally, we detail XACC's interfaces, their interactions, and its implementation as a hardware-agnostic framework for both near-term and future quantum–classical architectures. We provide concrete examples demonstrating the utility of this framework with paradigmatic tasks. Our approach lays the foundation for the development of compilers, associated runtimes, and low-level system tools tightly integrating quantum and classical workflows.