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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Software stewardship and advancement of a high-performance computing scientific application: QMCPACK

Here, we provide an overview of the software engineering efforts and their impact in QMCPACK, a production-level ab-initio Quantum Monte Carlo open-source code targeting high-performance computing (HPC) systems. Aspects included are: (i) strategic expansion of continuous integration (CI) targeting CPUs, using GitHub Actions own runners, and NVIDIA and AMD GPUs used in pre-exascale systems, (ii) incremental reduction of memory leaks using sanitizers, (iii) incorporation of Docker containers for CI and reproducibility, and (iv) refactoring efforts to improve maintainability, testing coverage, and memory lifetime management. We quantify the value of these improvements by providing metrics to illustrate the shift towards a predictive, rather than reactive, maintenance approach. Our goal, in documenting the impact of these efforts on QMCPACK, is to contribute to the body of knowledge on the importance of research software engineering (RSE) for the stewardship and advancement of community HPC codes to enable scientific discovery at scale.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Rucio: Scientific Data Management

Rucio is an open-source software framework that provides scientific collaborations with the functionality to organize, manage, and access their data at scale. The data can be distributed across heterogeneous data centers at widely distributed locations. Rucio was originally developed to meet the requirements of the high-energy physics experiment ATLAS, and now is continuously extended to support the LHC experiments and other diverse scientific communities. In this article, we detail the fundamental concepts of Rucio, describe the architecture along with implementation details, and report operational experience from production usage.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2026

S4PST: Stewardship and Advancement for Programming Systems and Tools 2024-2025 Project Report

We present the "Stewardship and Advancement of Programming Systems and Tools" (S4PST) project report for the calendar years 2024 and 2025. S4PST is dedicated to the stewardship and advancement of Programming Systems and Tools (PST) mainly targeting high-performance computing (HPC) for the scientific community. The project is part of the funded software stewardship organizations (SSOs) selected by ASCR as part of the NGSST program, and a member of CASS: the Consortiumfor the Advancement of Scientific Software.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

The Hidden Job Requirements for a Software Engineer

In a world increasingly operated by computers, where innovation depends on software, the software engineer’s role is changing continuously and gaining new dimensions. In commercial software development as well as scientific research environments, the way software developers are perceived is changing, because they are more important to the business than ever before. Nowadays, their job requires skills extending beyond the regular job description posted by HR, and more is expected. To advance and thrive in their new roles, the software engineers must embrace change, and practice the themes of the new era (integration, collaboration and optimization). The challenges may be somehow intimidating for freshly graduated software engineers. Through this paper the authors hope to set them on a path for success, by helping them relinquish their fear of the unknown.

Marinovici, Maria C.↗
NASA NTRS1992

Development of a tool-set for simultaneous, multi-site observations of astronomical objects

A network of ground and space based telescopes can provide continuous observation of astronomical objects. In a 'Target of Opportunity' scenario triggered by the system, any telescope on the network may request supporting observations. We propose to develop a set of data collection and display tools to support these observations. We plan to demonstrate the usefulness of this tool set for simultaneous multi-site observations of astronomical targets. Possible candidates for the proposed demonstration include the Extreme Ultraviolet Explorer, International Ultraviolet Explorer, ALEXIS, and sounding rocket experiments. Ground based observations operated by the University of California, Berkeley; the Jet Propulsion Laboratory; and Fairborn Observatory, Mesa, Arizona will be used to demonstrate the proposed concept. Although the demonstration will involve astronomical investigations, these tools will be applicable to a large number of scientific disciplines. The software tools and systems developed as a result of our work will be made available to the scientific community.

Chakrabarti, Supriya↗
NASA NTRS1993

Development of a tool-set for simultaneous, multi-site observations of astronomical objects

A network of ground and space based telescopes can provide continuous observation of astronomical objects. In a 'Target of Opportunity' scenario triggered by the system, any telescope on the network may request supporting observations. We propose to develop a set of data collection and display tools to support these observations. We plan to demonstrate the usefulness of this toolset for simultaneous multi-site observations of astronomical targets. Possible candidates for the proposed demonstration include the Extreme Ultraviolet Explorer, International Ultraviolet Explorer, ALEXIS, and sounding rocket experiments. Ground based observations, operated by the University of California, Berkeley; the Jet Propulsion Laboratory; and Fairborn Observatory, Mesa, Arizona will be used to demonstrate the proposed concept. Although the demonstration will involve astronomical investigations, these tools will be applicable to a large number of scientific disciplines. the software tools and systems developed as a result of our work will be made available to the scientific community.

Chakrabarti, Supriya↗
DOE OSTI2013

The Many Faces of a Software Engineer in a Research Community

The ability to gather, analyze and make decisions based on real world data is changing nearly every field of human endeavor. These changes are particularly challenging for software engineers working in a scientific community, designing and developing large, complex systems. To avoid the creation of a communications gap (almost a language barrier), the software engineers should possess an ‘adaptive’ skill. In the science and engineering research community, the software engineers must be responsible for more than creating mechanisms for storing and analyzing data. They must also develop a fundamental scientific and engineering understanding of the data. This paper looks at the many faces that a software engineer should have: developer, domain expert, business analyst, security expert, project manager, tester, user experience professional, etc. Observations made during work on a power-systems scientific software development are analyzed and extended to describe more generic software development projects.

agile methodologies↗
DOE OSTI2025

Deploying and Tracking Software with NCCS Software Provisioning

The National Center for Computational Sciences (NCCS) at Oak Ridge National Laboratory has a long history of deploying ground-breaking leadership-class supercomputers for the U.S. Department of Energy. The latest in this line of supercomputers is Frontier, the first supercomputer to break the exascale barrier (1018 floating-point operations per second) on the TOP500 list. Frontier serves a wide array of scientific domains, from traditional simulation-based workloads to newer AI and Machine Learning workloads. To best serve the NCCS user community, NCCS uses Spack to deploy a comprehensive software stack of scientific software packages, providing straightforward access to these packages through Lmod Environment Modules. Maintaining a large software stack while also including multiple new compiler releases each year is a very time-consuming task. Additionally, it is not straightforward to provide a software stack alongside existing vendor-provided software such as the HPE/Cray Programming Environment (CPE), and existing CPE, Spack, and Lmod integration does not allow for multiple versions of GPU libraries such as AMD’s ROCm to be used. To address these challenges and shortcomings, NCCS has developed the NCCS Software Provisioning tool (NSP)1, a tool for deploying and monitoring software stacks on HPC systems. NSP allows NCCS to quickly and effectively provision software stacks from the ground up using template-driven recipes and configuration files. NSP is successfully deployed on Frontier and several other NCCS clusters, enabling the NCCS software team to quickly deploy software stacks for newly-released compilers, expand current software offerings, better support GPU-based software, and monitor Lmod module usage to identify unused software packages that can be removed from the software stack. In this work, we discuss the shortcomings of the previous CPE, Spack, and Lmod usage at NCCS, provide further details on the implementation and structure of NSP, then discuss the benefits that NSP provides.

Rentschler, Asa [ORNL] (ORCID:0009000597694743)↗
NASA NTRS1995

AstroNet: A Tool Set for Simultaneous, Multi-Site Observations of Astronomical Objects

Earth-based, fully automatic "robotic" telescopes have been in routine operation for a number of years. As their number grows and their distribution becomes global, increasing attention is being given to forming networks of various sorts that will allow them, as a group, to make observations 24 hours a day in both hemispheres. We have suggested that telescopes based in space be part of this network. We further suggested that any telescope on this network be capable of asking, almost in real time, that other robotic telescopes perform support observations for them. When a target of opportunity required support observations, the system would determine which telescope(s) in the network would be most appropriate to make the observations and formulate a request to do so. Because the network would be comprised of telescopes located in widely distributed regions, this system would guarantee continuity of observations This report summarizes our efforts under this contract. We proposed to develop a set of data collection and display tools to aid simultaneous observation of astronomical targets from a number of observing sites. We planned to demonstrate the usefulness of this toolset for simultaneous multi-site observation of astronomical targets. Possible candidates or the proposed demonstration included the Extreme Ultraviolet Explorer (EUVE), International Ultraviolet Explorer (IUE), and ALEXIS, sounding rocket experiments. Ground-based observatories operated by the University of California, Berkeley, the Jet Propulsion Laboratory, and Fairborn Observatory in Mesa, Arizona were to be used to demonstrate the proposed concept. Although the demonstration was to have involved astronomical investigations, the tools were to have been applicable to a large number of scientific disciplines. The software tools and systems developed as a result of the work were to have been made available to the scientific community.

Chakrabarti, Supriya↗
NASA NTRS2010

Constraints and Opportunities in GCM Model Development

Over the past 30 years climate models have evolved from relatively simple representations of a few atmospheric processes to complex multi-disciplinary system models which incorporate physics from bottom of the ocean to the mesopause and are used for seasonal to multi-million year timescales. Computer infrastructure over that period has gone from punchcard mainframes to modern parallel clusters. Constraints of working within an ever evolving research code mean that most software changes must be incremental so as not to disrupt scientific throughput. Unfortunately, programming methodologies have generally not kept pace with these challenges, and existing implementations now present a heavy and growing burden on further model development as well as limiting flexibility and reliability. Opportunely, advances in software engineering from other disciplines (e.g. the commercial software industry) as well as new generations of powerful development tools can be incorporated by the model developers to incrementally and systematically improve underlying implementations and reverse the long term trend of increasing development overhead. However, these methodologies cannot be applied blindly, but rather must be carefully tailored to the unique characteristics of scientific software development. We will discuss the need for close integration of software engineers and climate scientists to find the optimal processes for climate modeling.

Schmidt, Gavin↗
DOE OSTI2022

Unified Language Frontend for Physic-Informed AI/ML

Artificial intelligence and machine learning (AI/ML) are becoming important tools for scientific modeling and simulation as in several other fields such as image analysis and natural language processing. ML techniques can leverage the computing power available in modern systems and reduce the human effort needed to configure experiments, interpret and visualize results, draw conclusions from huge quantities of raw data, and build surrogates for physics based models. Domain scientists in fields like fluid dynamics, microelectronics and chemistry can automate many of their most difficult and repetitive tasks or improve the design times by use of the faster ML-surrogates. However, modern ML and traditional scientific highperformance computing (HPC) tend to use completely different software ecosystems. While ML frameworks like PyTorch and TensorFlow provide Python APIs, most HPC applications and libraries are written in C++. Direct interoperability between the two languages is possible but is tedious and error-prone. In this work, we show that a compiler-based approach can bridge the gap between ML frameworks and scientific software with less developer effort and better efficiency. We use the MLIR (multi-level intermediate representation) ecosystem to compile a pre-trained convolutional neural network (CNN) in PyTorch to freestanding C++ source code in the Kokkos programming model. Kokkos is a programming model widely used in HPC to write portable, shared-memory parallel code that can natively target a variety of CPU and GPU architectures. Our compiler-generated source code can be directly integrated into any Kokkosbased application with no dependencies on Python or cross-language interfaces.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

SciDAC Visualization and Analytics Center for Enabling Technologies

This project focuses on leveraging scientific visualization and analytics software technology as an enabling technology for increasing scientific productivity and insight. Advances in computational technology have resulted in an "information big bang," which in turn has created a significant data understanding challenge. This challenge is widely acknowledged to be one of the primary bottlenecks in contemporary science. The vision for our Center is to respond directly to that challenge by adapting, extending, creating when necessary and deploying visualization and data understanding technologies for our science stakeholders. Using an organizational model as a Visualization and Analytics Center for Enabling Technologies (VACET), we are well positioned to be responsive to the needs of a diverse set of scientific stakeholders in a coordinated fashion using a range of visualization, mathematics, statistics, computer and computational science and data management technologies.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1992

The Compton Observatory Science Workshop

The Compton Observatory Science Workshop was held in Annapolis, Maryland on September 23-25, 1991. The primary purpose of the workshop was to provide a forum for the exchange of ideas and information among scientists with interests in various areas of high energy astrophysics, with emphasis on the scientific capabilities of the Compton Observatory. Early scientific results, as well as reports on in-flight instrument performance and calibrations are presented. Guest investigator data products, analysis techniques, and associated software were discussed. Scientific topics covered included active galaxies, cosmic gamma ray bursts, solar physics, pulsars, novae, supernovae, galactic binary sources, and diffuse galactic and extragalactic emission.

Shrader, Chris R.↗
NASA NTRS2006

EOS MLS Science Data Processing System: A Description of Architecture and Capabilities

This paper describes the architecture and capabilities of the Science Data Processing System (SDPS) for the EOS MLS. The SDPS consists of two major components--the Science Computing Facility and the Science Investigator-led Processing System. The Science Computing Facility provides the facilities for the EOS MLS Science Team to perform the functions of scientific algorithm development, processing software development, quality control of data products, and scientific analyses. The Science Investigator-led Processing System processes and reprocesses the science data for the entire mission and delivers the data products to the Science Computing Facility and to the Goddard Space Flight Center Earth Science Distributed Active Archive Center, which archives and distributes the standard science products.

Microwave Limb Sounder (MLS)↗
DOE OSTI2026

Reference Hardware and Software Architecture for a Solenoid Pulser for Scientific Instrumentation at the National Ignition Facility

High current, high voltage solenoid pulser power systems for the generation of magnetic fields in a solenoid is not a radical or new concept. Despite being a cornerstone of scientific and weapons research for over 100 years, a commercial off the shelf unit easily configurable for a broad set of applications has yet to appear on the open market. Pulser systems are instead designed for specific target requirements. For gigawatt to terawatt systems, this is unavoidable. For megawatt systems however, many hundreds of thousands of dollars are wasted designing pulser systems from the ground up for clients, when it is possible to simply architect a modular system once that can have its capacitors swapped for many possible target applications. The National Ignition Facility (NIF) at the Lawrence Livermore National Laboratory (LLNL) is one such institution that would benefit from such a system, having bought such pulsers for several imaging systems. Ideally, all such a system would require is only a bachelors-level circuit analysis to find the correct capacitor values, as well as a written procedure to calibrate the software to correctly deliver the voltages required by the application. In this work, systems architecture and software engineering techniques are used to design and analyze such a system. Such a design, it is the hope of this author, will be used in scientific and engineering purposes in the future to build solenoid pulsers at the National Ignition Facility and other institutions to drastically cut development costs of scientific instrumentation.

42 ENGINEERING↗
DOE OSTI2020

Investigating Scientific Data Change with User Research Methods

Scientific datasets are continually expanding and changing due to fluctuations with instruments, quality assessment and quality control processes, and modifications to software pipelines. Datasets include minimal information about these changes or their effects requiring scientists manually assess modifications through a number of labor intensive and ad-hoc steps. The Deduce project is investigating data change to develop metrics, methods, and tools that will help scientists systematically identify and make decisions around data changes. Currently, there is a lack of understanding, and common practices, for identifying and evaluating changes in datasets since systematically measuring and managing data change is under explored in scientific work. We are conducting user research to address this need by exploring scientist's conceptualizations, behaviors, needs, and motivations when dealing with changing datasets. Our user research utilizes multiple methods to produce foundational, generative insights and evaluate research products produced by our team. In this paper, we detail our user research process and outline our findings about data change that emerge from our studies. Our work illustrates how scientific software teams can push beyond just usability testing user interfaces or tools to better probe the underlying ideas they are developing solutions to address.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1994

A study of software standards used in the avionics industry

Within the past decade, software has become an increasingly common element in computing systems. In particular, the role of software used in the aerospace industry, especially in life- or safety-critical applications, is rapidly expanding. This intensifies the need to use effective techniques for achieving and verifying the reliability of avionics software. Although certain software development processes and techniques are mandated by government regulating agencies, no one methodology has been shown to consistently produce reliable software. The knowledge base for designing reliable software simply has not reached the maturity of its hardware counterpart. In an effort to increase our understanding of software, the Langley Research Center conducted a series of experiments over 15 years with the goal of understanding why and how software fails. As part of this program, the effectiveness of current industry standards for the development of avionics is being investigated. This study involves the generation of a controlled environment to conduct scientific experiments on software processes.

Hayhurst, Kelly J.↗
NASA NTRSDate not supplied

Space Flown Rodent Liver RNA Sequencing Data for Machine Learning in Space Biology Research

High-throughput nucleic acid sequencing (DNA-seq, RNA-seq) has become widespread in biomedical research due to the growing availability and affordability of these assays. Data analysis has been accelerated in recent years by the adoption of artificial intelligence (AI) and machine learning (ML) techniques by biomedical researchers. In space biology research, RNAseq datasets from space-flown experimental samples are critical for characterizing the gene expression aberrations associated with exposure to spaceflight stressors. However, space biological experiments tend to be very low sample size, so identifying proper AI/ML algorithms for sequencing data analysis is an ongoing challenge since these algorithms typically require large sample size. The NASA Science Mission Directorate (SMD) has started the “Benchmark Initiative for AI/ML”, focused on creating datasets meant for three main applications: 1) scientific benchmarking, which finds the best algorithm for a specific problem; 2) application benchmarking, which measures algorithm performance against a set of parameters; and 3) system benchmarking, which evaluates performance of hardware and software architecture. These scientific benchmarks consist of an AI-ready dataset and a reference implementation on a specific scientific question. In this work, we focused on generating standardized datasets to allow the scientific community to benchmark AI/ML algorithms in the domain of space biology. We present here a standardized, AI-ready, publicly available benchmark dataset for space biology RNA-seq data as a collaboration between the NASA AI4LS (Artificial Intelligence for Life Sciences) working group. and NASA’s SMD. This dataset consists of space-flown and ground control mouse liver found in the NASA GeneLab omics database. However, to amplify the small sample number (n=112 samples) for ML purposes, we employ Gaussian noise and a generative adversarial network to extend this dataset to 6,000 synthetic samples, matching the original gene expression characteristics.

James Casaletto↗