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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 415 records · Page 23

All sources NASA NTRS DOE OSTI

DOE OSTI2013

SIPMaP: A Tool for Modeling Irregular Parallel Computations in the Super Instruction Architecture

Performance modeling is becoming an increasingly important part of the parallel application development process, particularly for expensive computations that will be run on very high-end systems where resources are scarce. We describe a performance modeling tool SIPMaP (Super Instruction Processor Modeling and Prediction) developed for the Super-Instruction Architecture (SIA). The SIA is designed for applications where the dominant data structures are large multi-dimensional arrays and it comprises a DSL, the Super-Instruction Assembly Language (SIAL) that supports expressing algorithms in terms of blocks (tiles), and its runtime system Super Instruction Processor (SIP) that manages distribution and disk storage of the arrays. SIPMaP generates performance models from the SIAL source code. In comparison with many applications where useful performance models have been developed and reported, these programs are irregular and have other difficult to model characteristics such as extensive overlapping of communication and computation.

Computer Science↗

DOE OSTI2011

Matlab Based LOCO

The LOCO algorithm has been used by many accelerators around the world. Although the uses for LOCO vary, the most common use has been to find calibration errors and correct the optics functions. The light source community in particular has made extensive use of the LOCO algorithms to tightly control the beta function and coupling. Maintaining high quality beam parameters requires constant attention so a relatively large effort was put into software development for the LOCO application. The LOCO code was originally written in FORTRAN. This code worked fine but it was somewhat awkward to use. For instance, the FORTRAN code itself did not calculate the model response matrix. It required a separate modeling code such as MAD to calculate the model matrix then one manually loads the data into the LOCO code. As the number of people interested in LOCO grew, it required making it easier to use. The decision to port LOCO to Matlab was relatively easy. It's best to use a matrix programming language with good graphics capability; Matlab was also being used for high level machine control; and the accelerator modeling code AT, [5], was already developed for Matlab. Since LOCO requires collecting and processing a relative large amount of data, it is very helpful to have the LOCO code compatible with the high level machine control, [3]. A number of new features were added while porting the code from FORTRAN and new methods continue to evolve, [7][9]. Although Matlab LOCO was written with AT as the underlying tracking code, a mechanism to connect to other modeling codes has been provided.

43 PARTICLE ACCELERATORS↗

DOE OSTI2023

Neural Architecture Search Benchmarks: Insights and Survey

Neural Architecture Search (NAS), a promising and fast-moving research field, aims to automate the architectural design of Deep Neural Networks (DNNs) to achieve better performance on the given task and dataset. NAS methods have been very successful in discovering efficient models for various Computer Vision, Natural Language Processing, etc. The major obstacles to the advancement of NAS techniques are the demand for large computation resources and fair evaluation of various search methods. The differences in training pipeline and setting make it challenging to compare the efficiency of two NAS algorithms. A large number of NAS Benchmarks to simulate the architecture evaluation in seconds have been released over the last few years to ease the computation burden of training neural networks and can aid in the unbiased assessment of different search methods. This paper provides an extensive review of several publicly available NAS Benchmarks in the literature. We provide technical details and a deeper understanding of each benchmark and point out future directions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2012

An Advanced simulation Code for Modeling Inductive Output Tubes

During the Phase I program, CCR completed several major building blocks for a 3D large signal, inductive output tube (IOT) code using modern computer language and programming techniques. These included a 3D, Helmholtz, time-harmonic, field solver with a fully functional graphical user interface (GUI), automeshing and adaptivity. Other building blocks included the improved electrostatic Poisson solver with temporal boundary conditions to provide temporal fields for the time-stepping particle pusher as well as the self electric field caused by time-varying space charge. The magnetostatic field solver was also updated to solve for the self magnetic field caused by time changing current density in the output cavity gap. The goal function to optimize an IOT cavity was also formulated, and the optimization methodologies were investigated.

43 PARTICLE ACCELERATORS↗

DOE OSTI2023

Natural Language Processing-Enhanced Nuclear Industry Operating Experience Data Analysis: Aggregation and Interpretation of Multi-Report Analysis Results

Industry-wide operating experience is a critical source of raw data for reliability and risk model parameter estimations for nuclear power plants. A large portion of operating experience data are failure events stored as reports that contain unstructured data, such as narratives. In current practice, a failure report is usually reviewed and manually coded by analysts. The coding is based on extracting several event characteristics such as system name, component type, sub-part type, failure mode, and failure cause. Event narratives are mostly used to help understand events and extract their characteristics. In this line of research, we aim to maximize the usage of event narratives by leveraging natural language processing (NLP) methods to automatically convert an event narrative to a causal graph. This research has promise to improve physical understanding of failure initiation and propagation and to facilitate use of non-failure data (e.g., near-misses and degradations) to complement the limited data pool of failures. In our previous work, we developed an NLP tool and applied it to analyze a number of licensee event reports submitted by U.S. nuclear power plants to the Nuclear Regulatory Commission. In this paper, we will report our recent research progress in aggregating the results of multiple reports, developing network model(s), and drawing statistical insights.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

An autonomous laboratory for the accelerated synthesis of novel materials

To close the gap between the rates of computational screening and experimental realization of novel materials, we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials discovery and motivates further integration of computations, historical knowledge and robotics.

36 MATERIALS SCIENCE↗

DOE OSTI2019

A Semantic Search Capability for a Grid Model Repository

The operation of electric grids is becoming more challenging due to continued increases in wind and solar generation, the proliferation of decentralization generation resources, and cyber security threats. To support the needed research, new large scale realistic public grid models have been developed and placed in a repository for access by researchers. To assist researchers in easily finding grid models that are suitable for their specific research needs, an approach to searching numerous large grid models was evaluated, prototyped and developed. Here, the research identified multiple possible approaches to using graph database techniques and a natural language processing for the search algorithm. Results of the prototype, the final approach for production repository and the search algorithm formulation are described.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2011

Asymptotic safety: A simple example

We use the Gross-Neveu model in 2<d<4 as a simple fermionic example for Weinberg's asymptotic safety scenario: despite being perturbatively nonrenormalizable, the model defines an interacting quantum field theory being valid to arbitrarily high momentum scales owing to the existence of a non-Gaussian fixed point. Using the functional renormalization group, we study the uv behavior of the model in both the purely fermionic as well as a partially bosonized language. We show that asymptotic safety is realized at non-Gaussian fixed points in both formulations, the universal critical exponents of which we determine quantitatively. The partially bosonized formulation allows to make contact to the large-N{sub f} expansion where the model is known to be renormalizable to all orders. In this limit, the fixed-point action as well as all universal critical exponents can be computed analytically. As asymptotic safety has become an important scenario for quantizing gravity, our description of a well-understood model is meant to provide for an easily accessible and controllable example of modern nonperturbative quantum field theory.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2014

OpenSoC Fabric

Recent advancements in technology scaling have shown a trend towards greater integration with large-scale chips containing thousands of processors connected to memories and other I/O devices using non-trivial network topologies. Software simulation proves insufficient to study the tradeoffs in such complex systems due to slow execution time, whereas hardware RTL development is too time-consuming. We present OpenSoC Fabric, an on-chip network generation infrastructure which aims to provide a parameterizable and powerful on-chip network generator for evaluating future high performance computing architectures based on SoC technology. OpenSoC Fabric leverages a new hardware DSL, Chisel, which contains powerful abstractions provided by its base language, Scala, and generates both software (C++) and hardware (Verilog) models from a single code base. The OpenSoC Fabric2 infrastructure is modeled after existing state-of-the-art simulators, offers large and powerful collections of configuration options, and follows object-oriented design and functional programming to make functionality extension as easy as possible.

Source record↗

DOE OSTI2014

OpenSoC Fabric

Source record↗

DOE OSTI2018

Generation of Reversible C++ Code for Optimistic Parallel Discrete Event Simulation

The reversible execution of C/C++ code has been a target of research and engineering for more than a decade as reversible computation has become a central notion in large-scale parallel discrete event simulation (PDES). The simulation models that are implemented for PDES are of increasing complexity and size and require various language features to support abstraction, encapsulation, and composition when building a simulation model. In this paper, we focus on parallel simulation models that are written with user-defined C++ abstractions and abstractions of the C++ Standard Library. We present an approach based on incremental state saving for establishing reversibility of C++ and an evaluation for a kinetic Monte-Carlo simulation implemented in C++. Although a significant runtime overhead is introduced with our technique, it is an enormous win that it allows using the entire C++ language, and has that code automatically transformed into reversible code to enable parallel execution with the Rensselaer’s optimistic simulation system (ROSS).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2011

From Petascale to Exascale: Eight Focus Areas of R&D Challenges for HPC Simulation Environments

Programming models bridge the gap between the underlying hardware architecture and the supporting layers of software available to applications. Programming models are different from both programming languages and application programming interfaces (APIs). Specifically, a programming model is an abstraction of the underlying computer system that allows for the expression of both algorithms and data structures. In comparison, languages and APIs provide implementations of these abstractions and allow the algorithms and data structures to be put into practice - a programming model exists independently of the choice of both the programming language and the supporting APIs. Programming models are typically focused on achieving increased developer productivity, performance, and portability to other system designs. The rapidly changing nature of processor architectures and the complexity of designing an exascale platform provide significant challenges for these goals. Several other factors are likely to impact the design of future programming models. In particular, the representation and management of increasing levels of parallelism, concurrency and memory hierarchies, combined with the ability to maintain a progressive level of interoperability with today's applications are of significant concern. Overall the design of a programming model is inherently tied not only to the underlying hardware architecture, but also to the requirements of applications and libraries including data analysis, visualization, and uncertainty quantification. Furthermore, the successful implementation of a programming model is dependent on exposed features of the runtime software layers and features of the operating system. Successful use of a programming model also requires effective presentation to the software developer within the context of traditional and new software development tools. Consideration must also be given to the impact of programming models on both languages and the associated compiler infrastructure. Exascale programming models must reflect several, often competing, design goals. These design goals include desirable features such as abstraction and separation of concerns. However, some aspects are unique to large-scale computing. For example, interoperability and composability with existing implementations will prove critical. In particular, performance is the essential underlying goal for large-scale systems. A key evaluation metric for exascale models will be the extent to which they support these goals rather than merely enable them.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Models and Processes to Extract Drug-like Molecules From Natural Language Text

Researchers worldwide are seeking to repurpose existing drugs or discover new drugs to counter the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A promising source of candidates for such studies is molecules that have been reported in the scientific literature to be drug-like in the context of viral research. However, this literature is too large for human review and features unusual vocabularies for which existing named entity recognition (NER) models are ineffective. We report here on a project that leverages both human and artificial intelligence to detect references to such molecules in free text. We present 1) a iterative model-in-the-loop method that makes judicious use of scarce human expertise in generating training data for a NER model, and 2) the application and evaluation of this method to the problem of identifying drug-like molecules in the COVID-19 Open Research Dataset Challenge (CORD-19) corpus of 198,875 papers. We show that by repeatedly presenting human labelers only with samples for which an evolving NER model is uncertain, our human-machine hybrid pipeline requires only modest amounts of non-expert human labeling time (tens of hours to label 1778 samples) to generate an NER model with an F-1 score of 80.5%—on par with that of non-expert humans—and when applied to CORD’19, identifies 10,912 putative drug-like molecules. This enriched the computational screening team’s targets by 3,591 molecules, of which 18 ranked in the top 0.1% of all 6.6 million molecules screened for docking against the 3CLPro protein.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

pyTCR: A tropical cyclone rainfall model for python

pyTCR is a climatology software package developed in the Python programming language. It integrates the capabilities of several legacy physical models and increases computational efficiency to allow rapid estimation of tropical cyclone (TC) rainfall consistent with the large-scale environment. Specifically, pyTCR implements a horizontally distributed and vertically integrated model [Zhu et al., 2013] for simulating rainfall driven by TCs. Along storm tracks, rainfall is estimated by computing the cross-boundary-layer, upward water vapor transport caused by different mechanisms including frictional convergence, vortex stretching, large-scale baroclinic effect (i.e., wind shear), topographic forcing, and radiative cooling [Lu et al., 2018]. The package provides essential functionalities for modeling and interpreting spatio-temporal TC rainfall data. pyTCR requires a limited number of model input parameters, making it a convenient and useful tool for analyzing rainfall mechanisms driven by TCs. To sample rare (most intense) rainfall events that are often of great societal interest, pyTCR adapts and leverages outputs from a statistical-dynamical TC downscaling model [Lin et al., 2023] capable of rapidly generating a large number of synthetic TCs given a certain climate. As a result, pyTCR significantly reduces computational effort and improves the efficiency in capturing extreme TC rainfall events at the tail of the distributions from limited datasets. Furthermore, the TC downscaling model is forced entirely by large-scale environmental conditions from reanalysis data or coupled General Circulation Models (GCMs), simplifying the projection of TC-induced rainfall and wind speed under future climate using pyTCR. Finally, pyTCR can be coupled with hydrological and wind models to assess risks associated with independent and compound events (e.g., storm surges and freshwater flooding).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2013

Neurons to algorithms LDRD final report.

Over the last three years the Neurons to Algorithms (N2A) LDRD project teams has built infrastructure to discover computational structures in the brain. This consists of a modeling language, a tool that enables model development and simulation in that language, and initial connections with the Neuroinformatics community, a group working toward similar goals. The approach of N2A is to express large complex systems like the brain as populations of a discrete part types that have specific structural relationships with each other, along with internal and structural dynamics. Such an evolving mathematical system may be able to capture the essence of neural processing, and ultimately of thought itself. This final report is a cover for the actual products of the project: the N2A Language Specification, the N2A Application, and a journal paper summarizing our methods.

Rothganger, Fredrick H.↗

DOE OSTI2022

A fast and accurate modeling approach for water and steam thermodynamics with practical applications in district heating system simulation

In U.S. district heating (DH) systems, steam is the most common heat transport medium. Industry demand for new advanced modeling capabilities of complete steam DH systems is increasing; however, the existing models for water/steam thermodynamics are too slow for large system simulations because of computationally expensive algebraic loops that require the solution to nonlinear systems of equations. For practical applications, this work presents a novel split-medium approach that implements numerically efficient liquid water models alongside various water/steam models, breaking costly algebraic loops by decoupling mass and energy balance equations. New component models for steam DH systems are also presented. We implemented the models in the equation based Modelica language and evaluated accuracy and computing speed across multiple scales: from fundamental thermodynamic properties to complete districts featuring 10 to 200 buildings. Compared to district models with the IF97 water/steam model and equipment models from the Modelica Standard Library, the new implementation improves the scaling rate for large districts from cubic to quadratic with negligible compromise to accuracy. Additionally, for an annual simulation with 180 buildings, this translates to a computing time reduction from 33 to 1-1.5 h. These results are critically important for industry practitioners to simulate steam DH systems at large scales.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2012

Price Responsive Demand in New York Wholesale Electricity Market using OpenADR

In New York State, the default electricity pricing for large customers is Mandatory Hourly Pricing (MHP), which is charged based on zonal day-ahead market price for energy. With MHP, retail customers can adjust their building load to an economically optimal level according to hourly electricity prices. Yet, many customers seek alternative pricing options such as fixed rates through retail access for their electricity supply. Open Automated Demand Response (OpenADR) is an XML (eXtensible Markup Language) based information exchange model that communicates price and reliability information. It allows customers to evaluate hourly prices and provide demand response in an automated fashion to minimize electricity costs. This document shows how OpenADR can support MHP and facilitate price responsive demand for large commercial customers in New York City.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2018

Predicting Foreign Language Usage from English-Only Social Media Posts

Social media is known for its multicultural and multilingual interactions, a natural product of which is code-mixing. Multilingual speakers mix languages they tweet to address a different audience, express certain feelings, or attract attention. This paper presents a large-scale analysis of 6 million tweets produced by 23 thousand multilingual users speaking 11 other languages besides English. We rely on this multilingual corpus to build predictive models for a novel task – inferring non- English languages that users speak exclusively from their English tweets. We contrast the predictive power of different linguistic signals and report that lexical content and syntactic structure of English tweets are the most predictive of non-English languages that users speak on Twitter. By analyzing cross-lingual transfer – the influence of non-English languages on various levels of linguistic performance in English, we present novel findings on stylistic and syntactic variations across speakers of 11 languages.

code-swicthing↗