SEARCH · Engineering Papers

Results for “Scientific Computing”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,369 records · Page 76

All sources NASA NTRS DOE OSTI

NASA NTRS2022

Building a Transdisciplinary, Exascale-Capable Workforce for Geospace Science

Key Points: ●First-principles, self-consistent geospace modeling will require at least exascale-level computing capabilities; however, the technical skills necessary to develop such simulation codes are not taught as part of Heliophysics training/PhD programs. ●Developing exascale Heliophysics codes will require transdisciplinary collaborations between physicists, computer scientists, software engineers, data scientists, and applied mathematics. Such teams must be persistent and formed around specific skills, not specific problems. ●We must have stable, positive long-term career outcomes for Heliophysics scientific developers in order to retain knowledge of and promote future innovation in scientific algorithm development and advanced computing techniques. One such way to do this is by funding long-term scientific programming groups, akin to Heliophysics DRIVE Centers. ●We must have a balance between promoting innovation via funding short-term closed-source development and allowing the open-source community to benefit from and build on the newest scientific modeling techniques.

C Bard↗

DOE OSTI2025

Reliable and Efficient Machine Learning (Final Technical Report)

Modern scientific experiments generate massive amounts of data at a pace much faster than humans can manually analyze. While machine learning has revolutionized commercial data analysis (such as recommending movies or recognizing faces), applying these tools to complex scientific discovery is challenging because scientific answers must be precise, interpretable, and adhere to physical laws. The research under this project aims to develop new mathematical tools and computer algorithms specifically designed for scientific applications. Major progress has been made in automatically cleaning and deconstructing messy experimental data, analyzing the visual information of physical phenomena, determining the underlying physical variables, and providing rig orous mathematical analysis of interesting algorithms and concepts widely used in machine learning. This project addressed the critical gap between our ability to generate massive scientific data and our ability to extract interpretable information from it. We established mathematical foundations for Scientific Machine Learning (SciML) aimed at effective data analytics and automated discovery. Our work focused on three core objectives: (1) developing reliable feature extraction methods for dynamic high-dimensional data, (2) establishing mathematical foundations for discovering dynamics via neural networks, and (3) creating rigorous optimization techniques for these models. Key outcomes come from two fronts. On the practical side, they include the development of algorithms that significantly enhance the extraction of signals from field data, as well as the capability to handle situations that exhibit smooth variations or physical stretching due to temperature changes. They also include the creation of an automated framework for discovering fundamental state variables from raw experimental data, demonstrating the ability to identify intrinsic physical dimensions without prior knowledge of the governing laws. On the theoretical front, the research results in theoretical advances in Optimal Transport, a widely used notion in SciML, specifically regarding functions with fixed-size nodal sets, provide sharp bounds relevant to uncertainty quantification. Meanwhile, the outcomes also include the establishment of convergence theories for nonlocal gradient descent methods, enabling robust optimization with noisy data in high-dimensional settings commonly encountered in scientific modeling. The project also helps creating opportunities to train the next generation of researchers, equipping them with the necessary technical skills for today’s workplace and preparing them for future advances.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

xSDK Foundations: Toward an Extreme-scale Scientific Software Development Kit

Here, extreme-scale computational science increasingly demands multiscale and multiphysics formulations. Combining software developed by independent groups is imperative: no single team has resources for all predictive science and decision support capabilities. Scientific libraries provide high-quality, reusable software components for constructing applications with improved robustness and portability. However, without coordination, many libraries cannot be easily composed. Namespace collisions, inconsistent arguments, lack of third-party software versioning, and additional difficulties make composition costly. The Extreme-scale Scientific Software Development Kit (xSDK) defines community policies to improve code quality and compatibility across independently developed packages (hypre, PETSc, SuperLU, Trilinos, and Alquimia) and provides a foundation for addressing broader issues in software interoperability, performance portability, and sustainability. The xSDK provides turnkey installation of member software and seamless combination of aggregate capabilities, and it marks first steps toward extreme-scale scientific software ecosystems from which future applications can be composed rapidly with assured quality and scalability.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2003

Software for Planning Scientific Activities on Mars

Mixed-Initiative Activity Plan Generator (MAPGEN) is a ground-based computer program for planning and scheduling the scientific activities of instrumented exploratory robotic vehicles, within the limitations of available resources onboard the vehicle. MAPGEN is a combination of two prior software systems: (1) an activity-planning program, APGEN, developed at NASA s Jet Propulsion Laboratory and (2) the Europa planner/scheduler from NASA Ames Research Center. MAPGEN performs all of the following functions: Automatic generation of plans and schedules for scientific and engineering activities; Testing of hypotheses (or what-if analyses of various scenarios); Editing of plans; Computation and analysis of resources; and Enforcement and maintenance of constraints, including resolution of temporal and resource conflicts among planned activities. MAPGEN can be used in either of two modes: one in which the planner/scheduler is turned off and only the basic APGEN functionality is utilized, or one in which both component programs are used to obtain the full planning, scheduling, and constraint-maintenance functionality.

Ai-Chang, Mitchell↗

DOE OSTI2013

The genome portal of the Department of Energy Joint Genome Institute: 2014 updates

The U.S. Department of Energy (DOE) Joint Genome Institute (JGI), a national user facility, serves the diverse scientific community by providing integrated high-throughput sequencing and computational analysis to enable system-based scientific approaches in support of DOE missions related to clean energy generation and environmental characterization. The JGI Genome Portal (http://genome.jgi.doe.gov) provides unified access to all JGI genomic databases and analytical tools. The JGI maintains extensive data management systems and specialized analytical capabilities to manage and interpret complex genomic data. A user can search, download and explore multiple data sets available for all DOE JGI sequencing projects including their status, assemblies and annotations of sequenced genomes. In this paper, we describe major updates of the Genome Portal in the past 2 years with a specific emphasis on efficient handling of the rapidly growing amount of diverse genomic data accumulated in JGI.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2007

Tracking-Data-Conversion Tool

Object Oriented Data Technology (OODT) is a software framework for creating a Web-based system for exchange of scientific data that are stored in diverse formats on computers at different sites under the management of scientific peers. OODT software consists of a set of cooperating, distributed peer components that provide distributed peer-topeer (P2P) services that enable one peer to search and retrieve data managed by another peer. In effect, computers running OODT software at different locations become parts of an integrated data-management system.

Flora-Adams, Dana↗

NASA NTRS2007

Software Framework for Peer Data-Management Services

Object Oriented Data Technology (OODT) is a software framework for creating a Web-based system for exchange of scientific data that are stored in diverse formats on computers at different sites under the management of scientific peers. OODT software consists of a set of cooperating, distributed peer components that provide distributed peer-to-peer (P2P) services that enable one peer to search and retrieve data managed by another peer. In effect, computers running OODT software at different locations become parts of an integrated data-management system.

Hughes, John↗

DOE OSTI2024

Quantum-Hardware Focused Application Performance Benchmarks (Final Technical Report)

Quantum computers promise to transform how we do scientific calculations. In this project, we benchmark different current quantum computers for solving chemistry problems and find ways to build noise-resilient implementations. We also use the characterization techniques developed to test ion trap quantum computers.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2016

Neuromorphic Computing, Architectures, Models, and Applications. A Beyond-CMOS Approach to Future Computing, June 29-July 1, 2016, Oak Ridge, TN

The White House and Department of Energy have been instrumental in driving the development of a neuromorphic computing program to help the United States continue its lead in basic research into (1) Beyond Exascale—high performance computing beyond Moore’s Law and von Neumann architectures, (2) Scientific Discovery—new paradigms for understanding increasingly large and complex scientific data, and (3) Emerging Architectures—assessing the potential of neuromorphic and quantum architectures. Neuromorphic computing spans a broad range of scientific disciplines from materials science to devices, to computer science, to neuroscience, all of which are required to solve the neuromorphic computing grand challenge. In our workshop we focus on the computer science aspects, specifically from a neuromorphic device through an application. Neuromorphic devices present a very different paradigm to the computer science community from traditional von Neumann architectures, which raises six major questions about building a neuromorphic application from the device level. We used these fundamental questions to organize the workshop program and to direct the workshop panels and discussions. From the white papers, presentations, panels, and discussions, there emerged several recommendations on how to proceed.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

ACM TOMS replicated computational results initiative

In this study, the scientific community relies on the peer review process for assuring the quality of published material, the goal of which is to build a body of work we can trust. Computational journals such as The ACM Transactions on Mathematical Software (TOMS) use this process for rigorously promoting the clarity and completeness of content, and citation of prior work. At the same time, it is unusual to independently confirm computational results.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Deep learning for computational chemistry

The rise and fall of artificial neural networks is well documented in the scientific literature of both the fields of computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on “deep” neural networks. Within the last few years, we have seen the transformative impact of deep learning the computer science domain, notably in speech recognition and computer vision, to the extent that the majority of practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties as compared to traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including QSAR, virtual screening, protein structure modeling, QM calculations, materials synthesis and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non neural networks state-of-the-art models across disparate research topics, and deep neural network based models often exceeded the “glass ceiling” expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a useful tool and may grow into a pivotal role for various challenges in the computational chemistry field.

Computational Chemistry↗

DOE OSTI2021

High-Performance Deep Learning Toolbox for Genome-Scale Prediction of Protein Structure and Function

Computational biology is one of many scientific disciplines ripe for innovation and acceleration with the advent of high-performance computing (HPC). In recent years, the field of machine learning has also seen significant benefits from adopting HPC practices. In this work, we present a novel HPC pipeline that incorporates various machine-learning approaches for structure-based functional annotation of proteins on the scale of whole genomes. Our pipeline makes extensive use of deep learning and provides computational insights into best practices for training advanced deep-learning models for high-throughput data such as proteomics data. We showcase methodologies our pipeline currently supports and detail future tasks for our pipeline to envelop, including large-scale sequence comparison using SAdLSA and prediction of protein tertiary structures using AlphaFold2.

Gao, Mu↗

DOE OSTI2018

A Job Sizing Strategy for High-Throughput Scientific Workflows

The user of a computing facility must make a critical decision when submitting jobs for execution: how many resources (such as cores, memory, and disk) should be requested for each job? If the request is too small, the job may fail due to resource exhaustion; if the request is too big, the job may succeed, but resources will be wasted. This decision is especially important when running hundreds of thousands of jobs in a high throughput workflow, which may exhibit complex, long tailed distributions of resource consumption. In this paper, we present a strategy for solving the job sizing problem: (1) applications are monitored and measured in user-space as they run; (2) the resource usage is collected into an online archive; and (3) jobs are automatically sized according to historical data in order to maximize throughput or minimize waste. We evaluate the solution analytically, and present case studies of applying the technique to high throughput physics and bioinformatics workflows consisting of hundreds of thousands of jobs, demonstrating an increase in throughput of 10-400 percent compared to naive approaches.

Tovar, Benjamin (ORCID:0000000252942281)↗

DOE OSTI2024

Globus service enhancements for exascale applications and facilities

Many extreme-scale applications require the movement of large quantities of data to, from, and among leadership computing facilities, as well as other scientific facilities and the home institutions of facility users. These applications, particularly when leadership computing facilities are involved, can touch upon edge cases (e.g., terabyte files) that had not been a focus of previous Globus optimization work, which had emphasized rather the movement of many smaller (megabyte to gigabyte) files. We report here on how automated client-driven chunking can be used to accelerate both the movement of large files and the integrity checking operations that have proven to be essential for large data transfers. In conclusion, we present detailed performance studies that provide insights into the benefits of these modifications in a range of file transfer scenarios.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1998

Applying Fortran 90 and Object-Oriented Techniques to Scientific Applications

High-performance parallel computing is having a profound impact on the size and complexity of physical problems which can be modeled. This impact has been a long time in coming, because the learning curve in adapting to this new world of computing is steeper than was imagined.

Fortran 90 parallel computing C++↗

NASA NTRS2012

Frameworks Coordinate Scientific Data Management

Jet Propulsion Laboratory computer scientists developed a unique software framework to help NASA manage its massive amounts of science data. Through a partnership with the Apache Software Foundation of Forest Hill, Maryland, the technology is now available as an open-source solution and is in use by cancer researchers and pediatric hospitals.

Source record↗

NASA NTRS2005

Performance Comparison of Mainframe, Workstations, Clusters, and Desktop Computers

A performance evaluation of a variety of computers frequently found in a scientific or engineering research environment was conducted using a synthetic and application program benchmarks. From a performance perspective, emerging commodity processors have superior performance relative to legacy mainframe computers. In many cases, the PC clusters exhibited comparable performance with traditional mainframe hardware when 8-12 processors were used. The main advantage of the PC clusters was related to their cost. Regardless of whether the clusters were built from new computers or whether they were created from retired computers their performance to cost ratio was superior to the legacy mainframe computers. Finally, the typical annual maintenance cost of legacy mainframe computers is several times the cost of new equipment such as multiprocessor PC workstations. The savings from eliminating the annual maintenance fee on legacy hardware can result in a yearly increase in total computational capability for an organization.

Farley, Douglas L.↗

DOE OSTI2025

OLCF’s Advanced Computing Ecosystem (ACE): FY25 Update for Ongoing Efforts

The advent of widespread use of artificial intelligence (AI) and machine learning (ML) models in science, coupled with fast data production rates of scientific instruments strain the traditional batch-oriented high-performance computing (HPC) environment. As scientific exploration continues to require more data and faster processing and analysis, new emerging technologies and capabilities to enable cross-facility and time-sensitive workflows are required for seamless integration of HPC and experimental facilities. The Advanced Computing Ecosystem (ACE) is a strategic initiative within the Oak Ridge Leadership Computing Facility (OLCF) established in 2024 to support the development of cutting-edge technologies to advance computational research and infrastructure at OLCF and across the Department of Energy (DOE). Several DOE initiatives are spearheading the evolution of the scientific landscape by blurring facility boundaries and connecting the user facilities to advance scientific capabilities and ensure energy dominance. The DOE Integrated Research Infrastructure (IRI) program is one example that is laying a foundation to support complex cross-facility workflows. The IRI program aims to integrate diverse computational resources, data infrastructures, and scientific instruments to facilitate collaboration and accelerate scientific discovery. The Interconnected Science Ecosystem (INTERSECT) initiative at Oak Ridge National Laboratory (ORNL) is another example that aims to revolutionize scientific research through AI-driven, interconnected autonomous laboratories and research facilities. Finally, the American Science Cloud (AmSC), recently announced in the “One Big Beautiful Bill”, aims to leverage prior infrastructure efforts of the IRI and automation and AI efforts of INTERSECT (and others) to build a federated, AI-augmented AmSC platform to unify the DOE’s computing, experimental, and data resources to catalyze scientific innovation.

97 MATHEMATICS AND COMPUTING↗