Results for “reference language” — Page 3

NASA NTRS1974

STRCMACS: An extensive set of Macros for structured programming in OS/360 assembly language

Two techniques are discussed that have been most often referred to as structured programming. One is that of programming with high level control structures (such as the if and while) replacing the branch instruction (goto-less programming); the other is the process of developing a program by progressively refining descriptions of components in terms of more primitive components (called stepwise refinement or top-down programming). In addition to discussing what these techniques are, it is shown why their use is advised and how both can be implemented in OS assembly language by the use of a special macro instruction package.

Barth, C. W.↗

NASA NTRS1984

An inventory of state natural resources information systems

Data bases and information systems developed and maintained by state agencies to support planning and management of environmental and nutural resources were inventoried for all 50 states, Puerto Rico, and U.S. Virgin Islands. The information obtained is assembled into a computerized data base catalog which is throughly cross-referecence. Retrieval is possible by code, state, data base name, data base acronym, agency, computer, GIS capability, language, specialized software, data category name, geograhic reference, data sources, and level of reliability. The 324 automated data bases identified are described.

Martinko, E. A.↗

NASA NTRS1977

Shuttle avionics visual display

SAV-D system is composed of display description language (DDL) and its language processor. Language provides for the description of displays in terms of static and variable references for text, special symbols, lines, and circles. SAV-D permits use of high-order English-like language to describe complete displays with increased speed and ease of coding, debugging, and modification. It also allows one to specify static and variable attribute, such as location, flash, dash, character size, and intensity.

Yoshimura, A. A.↗

DOE OSTI2021

CpRad User's Manual

This is the reference manual for users of the C programming language based proton Radiography simulation package CpRad, pronounced "see p-rad." These simulations are developed and maintained by Los Alamos National Laboratory personnel. Currently there are threaded versions, versions written for MPI, as well as versions that use GPUs. The fundamentals of our proton radiography model are well described in a review. The ray tracing in CpRad also includes the incident angle of the protons based on position in the field of view due to the matching conditions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS2013

Vectorized Rebinning Algorithm for Fast Data Down-Sampling

A vectorized rebinning (down-sampling) algorithm, applicable to N-dimensional data sets, has been developed that offers a significant reduction in computer run time when compared to conventional rebinning algorithms. For clarity, a two-dimensional version of the algorithm is discussed to illustrate some specific details of the algorithm content, and using the language of image processing, 2D data will be referred to as "images," and each value in an image as a "pixel." The new approach is fully vectorized, i.e., the down-sampling procedure is done as a single step over all image rows, and then as a single step over all image columns. Data rebinning (or down-sampling) is a procedure that uses a discretely sampled N-dimensional data set to create a representation of the same data, but with fewer discrete samples. Such data down-sampling is fundamental to digital signal processing, e.g., for data compression applications.

Dean, Bruce↗

NASA NTRSDate not supplied

Type-Safe Generic Programming in Fortran

When the Fortran language committee first collected requests for new language features for what has now become the Fortran 2023 standard, support for generic programming emerged as a top priority. However, due to the anticipated complexity, a dedicated committee subgroup was formed, and implementation of generic programming was purposely spread across two language release cycles -- aiming for what is internally referred to as F202Y. In this talk, I will begin with a brief overview of existing Fortran features that support generic programming and then proceed to a detailed examination of the plans for F202Y. This discussion will include motivating use cases, type-safe design goals, (probable) syntax, and a few simple examples. I conclude with a discussion on the likely limitations of generic programming in F202Y and my expectations for extended capabilities in the subsequent release, F203X.

Tom Clune↗

DOE OSTI2019

Ompparser

ompparser is a standalone and unified OpenMP parser, which is motivated by the facts that: 1) The differences in terms of syntax and semantics of OpenMP constructs between C/C++ and Fortran are minor. 2) Current OpenMP compilers often develop their own parsers, which represent redundant work. The features of ompparser include: 1) ompparser can be used standalone for static source code analysis, e.g. tools for semantics checking or similarity analysis between C/C++ and Fortran programs 2) omppaser can be integrated into an OpenMP compiler implementation can reduce the development efforts, and there is no need to create and maintain two separate parsers for C/C++ and Fortran. 3) ompparser provides a complete reference OpenMP grammar in the Backus-Naur Form that formally describes all the latest OpenMP language constructs. This will help users understand the rules and restriction of the OpenMP standard, which no longer contains a reference grammar in its recent versions.

Wang, Anjia↗

DOE OSTI2025

solposx: A Python package for determining solar position and atmospheric refraction

solposx is a Python package of reference algorithms for calculating the sun’s position and atmospheric refraction. The package includes 11 solar position algorithms and 6 refraction models from the past 50 years. All functions follow a standardized design pattern, making it easy to compare different algorithms. The provided algorithm implementations have been thoroughly vetted, making the package a valuable research tool and a reliable reference for implementing solar position algorithms in other programming languages or applications.

Jensen, Adam R. [Technical University of Denmark, ↗

NASA NTRS1971

Development of improved pyroelectric detectors; Literature survey of pyroelectric materials and their characteristics

The object of this program is to improve the detectivity of the pyroelectric detector with the ultimate goal of operation at or near the temperature-noise limit. Two general areas of investigation are undertaken. The first is to improve responsivity through the use of new materials. The second is directed toward reduction of noise and will be effected with improved field effect transistor characteristics, and improved electroding of the pyroelectric material. The search for new materials has begun with a review of the literature on pyroelectric materials in several languages. The compiled data includes an extensive list of references. From this, several materials have already been selected for investigation. FETs are being obtained from various manufacturers, evaluated, and selected units will be tested with pyroelectric elements as complete detectors.

Weiner, S.↗

NASA NTRS2024

Earth Independent Medical Operations (EIMO) DATASCOPE Technical Interchange Meeting 21st August 2023: Background and Summary of Discussion

An aspiration for EIMO datascope is to realize artificial intelligence-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A vision proposed to the meeting participants was that of a “system of systems,” whereby EIMO will utilize AI-supported natural language processing and machine learning techniques to synthesize embedded reference databases and real-time data streams [input vectors] from multiple data sources to continuously and seamlessly assess crew health & performance. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will ideally have a degree of mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats.

Artificial Intelligence↗

NASA NTRS1992

VIEWCACHE: An incremental pointer-base access method for distributed databases. Part 1: The universal index system design document. Part 2: The universal index system low-level design document. Part 3: User's guide. Part 4: Reference manual. Part 5: UIMS test suite

The goal of the Universal Index System (UIS), is to provide an easy-to-use and reliable interface to many different kinds of database systems. The impetus for this system was to simplify database index management for users, thus encouraging the use of indexes. As the idea grew into an actual system design, the concept of increasing database performance by facilitating the use of time-saving techniques at the user level became a theme for the project. This Final Report describes the Design, the Implementation of UIS, and its Language Interfaces. It also includes the User's Guide and the Reference Manual.

Kelley, Steve↗

DOE OSTI2023

Evaluation of OpenAI Codex for HPC Parallel Programming Models Kernel Generation

We evaluate AI-assisted generative capabilities on fundamental numerical kernels in high-performance computing (HPC), including AXPY, GEMV, GEMM, SpMV, Jacobi Stencil, and CG. We test the generated kernel codes for a variety of language-supported programming models, including (1) C++ (e.g., OpenMP [including offload], OpenACC, Kokkos, SyCL, CUDA, and HIP), (2) Fortran (e.g., OpenMP [including offload] and OpenACC), (3) Python (e.g., numpy, Numba, cuPy, and pyCUDA), and (4) Julia (e.g., Threads, CUDA.jl, AMDGPU.jl, and KernelAbstractions.jl). We use the GitHub Copilot capabilities powered by the GPT-based OpenAI Codex available in Visual Studio Code as of April 2023 to generate a vast amount of implementations given simple + + prompt variants. To quantify and compare the results, we propose a proficiency metric around the initial 10 suggestions given for each prompt. Results suggest that the OpenAI Codex outputs for C++ correlate with the adoption and maturity of programming models. For example, OpenMP and CUDA score really high, whereas HIP is still lacking. We found that prompts from either a targeted language such as Fortran or the more general purpose Python can benefit from adding code keywords, while Julia prompts perform acceptably well for its mature programming models (e.g., Threads and CUDA.jl). We expect for these benchmarks to provide a point of reference for each programming model's community. Overall, understanding the convergence of large language models, AI, and HPC is crucial due to its rapidly evolving nature and how it is redefining human-computer interactions.

Godoy, William↗

NASA NTRS1980

An overview of software design languages

The nature and use of design languages and associated processors that are used in software development are reviewed with reference to development work on the Galileo spacecraft project, a Jupiter orbiter scheduled for launch in 1984. The major design steps are identified (functional design, architectural design, detailed design, coding, and testing), and the purpose, functions and the range of applications of design languages are examined. Then the general character of any design language is analyzed in terms of syntax and semantics. Finally, the differences and similarities between design languages are illustrated by examining two specific design languages: Software Design and Documentation language and Problem Statement Language/Problem Statement Analyzer.

Callender, E. D.↗

DOE OSTI2025

ROSE Castor

ROSE Castor is a tool enabling automated verification of C++, built off of the ROSE compiler framework and the Why3 framework. Castor defines a verification language for providing specifications of C++ code, letting users perform automated functional formal verification of their C++ code. Castor is designed to target C++17, and supports a subset of the language, including classes, functions, templates, integers and booleans, pointers and references, and single inheritance. Castor currently does not support multiple or virtual inheritance, virtual functions, floating-point, threading, lambda functions, or the C++ STL, though some of these are planned in future updates. Castor ships with an in-house parser for parsing verification conditions.

Lane, PhillipA [Lawrence Livermore National Labora↗

NASA NTRS1974

Software and modifications for automated microwave spectral measurements on the Hewlett Packard 8460 spectrometer

A Hewlett-Packard 8460A spectrometer was adapted for automatic spectral searches and measurements under computer control. Software consists of a BASIC language control program and assembly language equipment drivers. Complete software listings are given including cross reference tables. Each equipment driver program is discussed, and wiring diagrams are given for the additions required for computer control.

White, W. F.↗

NASA NTRSDate not supplied

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

J Lemery↗

NASA NTRS2024

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

Medical Operations↗

DOE OSTI2018

Advanced trust region optimization strategies for glass box / black box models

We present an improved trust region filter (TRF) method for optimization of combined glass box/black box systems. Glass box systems refer to models that are easily expressed in an algebraic modeling language, providing cheap and accurate derivative information. By contrast, black box systems may be computationally expensive and derivatives are unavailable. The TRF method, as first introduced in our previous work (Eason and Biegler, AIChE J . 2016; 62:3124–3136), is able to handle hybrid systems containing both glass and black box components, which can frequently arise in chemical engineering, for example, when a multiphase reactor model is included in a flow sheet optimization problem. We discuss several recent modifications in the algorithm such as the sampling region, which maintains the algorithm's global convergence properties without requiring the trust region to shrink to zero in the limit. To benchmark the development of this optimization method, a test set of problems is generated based on modified problems from the CUTEr and COPS sets. The modified algorithm demonstrates improved performance using the test problem set. Finally, the algorithm is implemented within the Pyomo environment and demonstrated on a rigorous process optimization case study for carbon capture. © 2018 American Institute of Chemical Engineers AIChE J , 64: 3934–3943, 2018

Eason, John P.↗