Atalanta Program Computer Science Overview [Slides]
This student intern presentation gives an overview of computer science and computer engineering for high school students.
97 MATHEMATICS AND COMPUTING↗
SEARCH · Engineering Papers
Results for “COMPUTATIONAL SCIENCE”
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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Computational Science - Scientific Visualization: Closeout Certifications; CSSV Newsletter Volume I; CSSV Newsletter Volume II; CSSV Activity Journal; CSSV Final Financial Report (Final Technical Progress Report)
This Project's third year of implementation in 2007-2008, the final year, as designated by Elizabeth City State University (ECSU), in cooperation with the National Association of Mathematicians (NAM) Inc., in an effort to promote research and research training programs in computational science & scientific visualization (CSSV). A major goal of the Project was to attract the energetic and productive faculty, graduate and upper division undergraduate students of diverse ethnicities to a program that investigates science and computational science issues of long-term interest to the Department of Energy (DoE) and the nation. The breadth and depth of computational science & scientific visualization and the magnitude of resources available are enormous for permitting a variety of research activities. ECSU's Computational Science-Science Visualization Center will serve as a conduit for directing users to these enormous resources.
97 MATHEMATICS AND COMPUTING↗
A Tale of Two Systems: Flexibility of Usage of Kraken and Nautilus at the National Institute for Computational Sciences
The National Institute for Computational Sciences (NICS) at the University of Tennessee currently operates two computational resources for the eXtreme Science and Engineering Discovery Environment (XSEDE), Kraken, a 112,896-core Cray XT5 for general purpose computation, and Nautilus, a 1,024-core SGI Altix UV 1000 for data analysis and visualization. We analyze a year's worth of accounting logs for Kraken and Nautilus to understand how users take advantage of these two systems and how analysis jobs differ from general HPC computation We find that researchers take advantage of the flexibility offered by these systems, running a wide variety of jobs at many scales and using the full range of core counts and available memory for their jobs. The jobs on Nautilus tend to use less walltime and more memory per core than the jobs run on Kraken. Additionally, researchers are more likely to run interactive jobs on Nautilus than on Kraken. Small jobs experience a good quality of service on both systems. This information can be used for the management and allocation of time on existing HPC and analysis systems as well as for planning for deploying future HPC and analysis systems.
Szczepanski, Amy F↗
Scalable quantum computational science: A perspective from block-encodings and polynomial transformations
Significant developments made in quantum hardware and error correction recently have been driving quantum computing toward practical utility. However, gaps remain between abstract quantum algorithmic development and practical applications in computational sciences. In this perspective article, we propose several properties that scalable quantum computational science methods should possess. We further discuss how block-encodings and polynomial transformations can potentially serve as a unified framework with the desired properties. Recent advancements on these topics are presented, including the construction and assembly of block-encodings, and various generalizations of quantum signal processing (QSP) algorithms to perform polynomial transformations. The scalability of QSP methods on parallel and distributed quantum architectures is also highlighted. Promising applications in simulation and observable estimation in chemistry, physics, and optimization problems are presented. We hope this perspective serves as a gentle introduction to state-of-the-art quantum algorithms for the computational science community and inspires future development of scalable quantum computational science methodologies that bridge theory and practice.
Bayesian inference↗
Integrating Data Management and Collaborative Sharing with Computational Science Processes
Structured Scientific Data Management - the management of storage, access, usage, lifecycle, content and meaning for scientific data - is not as commonly employed in computational science as it is in other fields of scientific endeavor. However, where it has been co-developed and integrated with the computational science research it has had a transformational influence on the scientific work. These infrastructures enabled not only new research previously impossible, but also helped to speed up the research process and improved the quality of the research output. Good data management systems are capable of facilitating effective scientific collaborations on a group, institutional, national or international level, through the easy sharing of resources and results. Today as computational science is becoming more data rich and collaborative, integrated scientific data management is becoming an essential tool for every computational science research and production environment. This chapter will describe the fundamental principles and components of a good data management system, provide real world examples of successful implementations and provides an outlook on future developments. We conclude with a short section on how to get started for those whose interest has been peaked by this chapter
42 ENGINEERING↗
RAPIDS2: A SciDAC Institute for Computer Science, Data, and Artificial Intelligence
The objective of RAPIDS2 SciDAC Institute for Computer Science and Data is to assist Office of Science (SC) application teams in overcoming computer science and data challenges in the use of DOE supercomputing resources to achieve science breakthroughs.
97 MATHEMATICS AND COMPUTING↗
In Situ Visualization for Computational Science
In situ visualization, i.e., visualizing simulation data as it is generated, is an emerging processing paradigm in response to trends in the area of high-performance computing. This paradigm holds great promise in its ability to access increased spatio-temporal resolution and leverage extensive computational power. However, the paradigm is also widely viewed as limiting when it comes to exploration-oriented use cases and further will require visualization systems to become more and more complicated and constrained. Additionally, there are many open research topics within situ visualization. The Dagstuhl seminar 18271 "In Situ Visualization for Computational Science" brought together researchers and practitioners from three communities (computational science, high-performance computing, and scientific visualization) to share interesting findings, to identify lines of open research, and to determine a medium-term research agenda that addresses the most pressing problems. This report summarizes the outcomes and findings of the seminar.
Computational Science↗
Toward a Compatible Reproducibility Taxonomy for Computational and Computing Sciences
Reproducibility is an essential ingredient of the scientific enterprise. The ability to reproduce results builds trust that we can rely on the results as foundations for future scientific exploration. Presently, the fields of computational and computing sciences provide two opposing definitions of reproducible and replicable. In computational sciences, reproducible research means authors provide all necessary data and computer codes to run analyses again, so others can re-obtain the results (J. Claerbout et al., 1992). The concept was adopted and extended by several communities, where it was distinguished from replication: collecting new data to address the same question, and arriving at consistent findings (Peng et al. 2006). The Association of Computing Machinery (ACM), representing computer science and industry professionals, recently established a reproducibility initiative, adopting essentially opposite definitions. The purpose of this report is to raise awareness of the opposite definitions and propose a path to a compatible taxonomy.
97 MATHEMATICS AND COMPUTING↗
In Situ Visualization for Computational Science (Dagstuhl Seminar 18271)
In situ visualization, i.e., visualizing simulation data as it is generated, is an emerging processing paradigm in response to trends in the area of high-performance computing. This paradigm holds great promise in its ability to access increased spatio-temporal resolution and leverage extensive computational power. However, the paradigm is also widely viewed as limiting when it comes to exploration-oriented use cases and further will require visualization systems to become more and more complicated and constrained. Additionally, there are many open research topics with in situ visualization. The Dagstuhl seminar 18271 “In Situ Visualization for Computational Science” brought together researchers and practitioners from three communities (computational science, high-performance computing, and scientific visualization) to share interesting findings, to identify lines of open research, and to determine a medium-term research agenda that addresses the most pressing problems. This report summarizes the outcomes and findings of the seminar.
97 MATHEMATICS AND COMPUTING↗
Educational Consortium for Energy-related Data Science & Computation in Building Engineering Programs
The project spearheaded by Pennsylvania State University aims to address the growing need for integrating energy-focused computation and data science into building engineering education. As the demand for energy-efficient building designs and operations increases, the educational sector must adapt to equip future engineers with the necessary skills. This initiative responds to this need by developing a consortium that unites multiple institutions to enhance curriculum development, dataset curation, and resource sharing, thereby ensuring students are well-prepared for the evolving energy sector. The primary goal of the project is to establish a consortium that will develop and disseminate educational materials and training programs focused on energy-related data science and computation. Key accomplishments include the creation of a beta website for resource sharing, the development of training programs and standalone modules, and the curation of datasets accessible to the public. This effort will culminate in a curriculum that incorporates advanced modeling technologies and data science skills into building engineering programs.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Algebraic Multigrid: A Meeting of Math and Computer Science
Talk at UNM 20th Annual Computer Science Student Conference 2025
Siefert, Christopher [Sandia National Laboratories↗
Computational Science and Innovation
Simulations - utilizing computers to solve complicated science and engineering problems - are a key ingredient of modern science. The U.S. Department of Energy (DOE) is a world leader in the development of high-performance computing (HPC), the development of applied math and algorithms that utilize the full potential of HPC platforms, and the application of computing to science and engineering problems. An interesting general question is whether the DOE can strategically utilize its capability in simulations to advance innovation more broadly. In this article, I will argue that this is certainly possible.
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Support for the Core Research Activities and Studies of the Computer Science and Telecommunications Board (DE-SC0020446 Final Technical Report)
Supported the core operations of the National Academies' Computer Science and Telecommunications Board (CSTB). Helped support planning and conducting of board meetings, identification of priority topics in computer science and other areas of computing and communications technologies, and oversight for CSTB's portfolio of studies and convenings. Activities shaped and overseen included: a workshop on Al for scientific discovery; collaborative work with other Academies units on a study on foundational research gaps and future directions for digital twins; a study on current capabilities, future prospects, and governance of facial recognition technologies, a study on post- exascale computing; a study on fostering responsible computing research, collaborative work with other Academies units on automated research workflows for accelerated scientific discovery; a study on meeting federal cybersecurity workforce needs, and a study of the ecosystem driving information technology innovation.
97 MATHEMATICS AND COMPUTING↗
In Situ Visualization for Computational Science
In situ visualization, i.e., visualizing simulation data as it is generated, is an emerging processing paradigm in response to trends in the area of high-performance computing. This paradigm holds great promise in its ability to access increased spatio-temporal resolution and leverage extensive computational power. However, the paradigm is also widely viewed as limiting when it comes to exploration-oriented use cases and further will require visualization systems to become more and more complicated and constrained. Additionally, there are many open research topics with in situ visualization. The Dagstuhl seminar 18271 "In Situ Visualization for Computational Science" brought together researchers and practitioners from three communities (computational science, high-performance computing, and scientific visualization) to share interesting findings, to identify lines of open research, and to determine a medium-term research agenda that addresses the most pressing problems. This report summarizes the outcomes and findings of the seminar.
Bauer, Andy↗
Core Research Activities and Studies of the Computer Science and Telecommunications Board
Lists activities of the Computer Science and Telecommunications Board and summarizes research results partly enabled by this award.
97 MATHEMATICS AND COMPUTING↗
Research and Education in Computational Science and Engineering
Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments— including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers—is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.
computational science and engineering↗
The U.S. Department of Energy Computational Science Graduate Fellowship, 1991-2021: Follow-Up Study Shows Major Impact on Recipients and the Scientific Workforce
Since 1991, the U.S. Department of Energy Computational Science Graduate Fellowship (DOE CSGF) has addressed DOE National Laboratory needs as well as demands in the national workforce for trained professionals in computational science and engineering. Sponsored by the Department of Energy's Office of Science and the National Nuclear Security Administration, the DOE CSGF supports doctoral students in the pursuit of novel scientific or engineering discoveries using high-performance computing (HPC) resources. To meet the program’s core requirements, recipients participate in multidisciplinary studies, carry out at least one 12-week DOE laboratory research practicum, and contribute to an annual program review where the fellows present their research for sponsor review. The Krell Institute, which as managed the fellowship on behalf of the DOE since 1997, has commissioned several follow-up studies to examine the DOE CSGF recipients’ characteristics, fellows’ outcomes and professional accomplishments, alumni’s career paths and achievements, and recipients’ impact on national priorities through research and education.
97 MATHEMATICS AND COMPUTING↗
The U.S. Department of Energy Computational Science Graduate Fellowship, 1991-2021: Follow-Up Study Shows Major Impact on Recipients and the Scientific Workforce
Since 1991, the U.S. Department of Energy Computational Science Graduate Fellowship (DOE CSGF) has addressed DOE National Laboratory needs as well as demands in the national workforce for trained professionals in computational science and engineering. Sponsored by the Department of Energy's Office of Science and the National Nuclear Security Administration, the DOE CSGF supports doctoral students in the pursuit of novel scientific or engineering discoveries using high-performance computing (HPC) resources. To meet the program’s core requirements, recipients participate in multidisciplinary studies, carry out at least one 12-week DOE laboratory research practicum, and contribute to an annual program review where the fellows present their research for sponsor review. The Krell Institute, which as managed the fellowship on behalf of the DOE since 1997, has commissioned several follow-up studies to examine the DOE CSGF recipients’ characteristics, fellows’ outcomes and professional accomplishments, alumni’s career paths and achievements, and recipients’ impact on national priorities through research and education.
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