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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Fostering Computational Thinking within Elementary Classrooms through a Research-Practice Partnership: A Strategy for Broadening Participation in Computer Science

This paper describes a research-practice partnership involving twenty elementary teachers and a support team of university researchers and K-12 learning specialists. Over five years, the partnership explored ways to highlight and to expand computational thinking within math and science instruction. The project sought to increase student awareness, confidence, and fluency with computational thinking as a problem-solving approach, with particular attention to students from groups historically underrepresented in computer science. Based on feedback from across the partnership – together with analysis of student attitude and problem-solving data – this experience report identifies successes, challenges, and recommendations for future study.

Broadening participation↗
DOE OSTI2013

ALACRITY: Analytics-Driven Lossless Data Compression for Rapid In-Situ Indexing, Storing, and Querying. In: Transactions on Large-Scale Data- and Knowledge-Centered Systems X. Lecture Notes in Computer Science, vol 8220

High-performance computing architectures face nontrivial data processing challenges, as computational and I/O components further diverge in performance trajectories. For scientific data analysis in particular, methods based on generating heavyweight access acceleration structures, e.g. indexes, are becoming less feasible for ever-increasing dataset sizes. We present ALACRITY, demonstrating the effectiveness of a fused data and index encoding of scientific, floating-point data in generating lightweight data structures amenable to common types of queries used in scientific data analysis. We exploit the representation of floating-point values by extracting significant bytes, using the resulting unique values to bin the remaining data along fixed-precision boundaries. To optimize query processing, we use an inverted index, mapping each generated bin to a list of records contained within, allowing us to optimize query processing with attribute range constraints. Overall, the storage footprint for both index and data is shown to be below numerous configurations of bitmap indexing, while matching or outperforming query performance.

Compression Ratio↗
DOE OSTI2021

Parallel Multigrid in Time and Space for Extreme-Scale Computational Science

The coming massive parallelism of exascale computing presents a pressing challenge for the many DOE simulations of time-dependent partial differential equations, which typically use traditional sequential time stepping methods. Since this traditional approach is inherently serial, it presents a sequential bottleneck when moving to exascale computing, because future performance gains will come through greater concurrency, not faster clock speeds. Thus, the goal of this work is to research parallelism in time, i.e., methods that compute multiple time values simultaneously, not sequentially. The focus will be on hyperbolic and chaotic problems of programmatic interest to DOE, with the goal of enabling scalable simulations of time-dependent hyperbolic and chaotic problems on future architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Experiences with High-Level Programming Directives for Porting Applications to GPUs. In: Facing the Multicore--Challenge II, Lecture Notes in Computer Science.

HPC systems now exploit GPUs within their compute nodes to accelerate program performance. As a result, high-end application development has become extremely complex at the node level. In addition to restructuring the node code to exploit the cores and specialized devices, the programmer may need to choose a programming model such as OpenMP or CPU threads in conjunction with an accelerator programming model to share and manage the different node resources. This comes at a time when programmer productivity and the ability to produce portable code has been recognized as a major concern. In order to offset the high development cost of creating CUDA or OpenCL kernels, directives have been proposed for programming accelerator devices, but their implications are not well known. In this paper, we evaluate the state of the art accelerator directives to program several applications kernels, explore transformations to achieve good performance, and examine the expressivity and performance penalty of using high-level directives versus CUDA. We also compare our results to OpenMP implementations to understand the benefits of running the kernels in the accelerator versus CPU cores.

CUDA Implementation↗
DOE OSTI2022

Computer Science Research Needs for Parallel Discrete Event Simulation (PDES)

Historically, scientific computing efforts have demonstrated the clear need for, and effective use of, supercomputing with traditional time-stepped simulations. Nevertheless, there are several areas in the mission spaces of the U.S. Department of Energy and other agencies waiting to tap advanced computing research using a different, discrete event style of modeling, simulation, and analysis. These span a wide spectrum of applications including energy grid resilience, urban planning and policy, transportation science, building technologies, emergency response and planning, environmental impact analysis, computational epidemiology, Internet communications, cyber security, and cyber-physical systems, to name only a few. Even within traditional scientific applications, the role of discrete event modes of execution is increasing in the form of new event-based mathematical solvers such as quantized state integration methods and discrete-continuous hybrid system solvers. Co-design of advanced supercomputing hardware systems is another area that exploits discrete event simulation at its core for effective analyses. Complex systems, entity behaviors and interconnections play a significant role in all these applications, which are mapped to large-scale models with discrete event formulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

The Early Years and Evolution of the DOE Computational Science Graduate Fellowship Program

The U.S. Department of Energy Computational Graduate Fellowship Program, celebrating 30 years of existence in 2021, is one of the most successful graduate fellowships in the world as well as one of the longest running programs in the U.S. Department of Energy. Furthermore, this article discusses the conception, early years and evolution of the fellowship over the past thirty years.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Parallel Multigrid in Time and Space for Extreme-Scale Computational Science: Chaotic and Hyperbolic Problems

The coming massive parallelism of exascale computing presents a pressing challenge for the many DOE simulations of time-dependent partial differential equations (PDEs), which typically use traditional sequential time stepping methods. Since this traditional approach is inherently serial, it presents a sequential bottleneck when moving to exascale computing, because future performance gains will come through greater concurrency, not faster clock speeds. Thus, the goal of this work is to research parallelism in time, i.e., methods that compute multiple time values simultaneously, not sequentially. The focus will be on hyperbolic and chaotic problems of interest to DOE, with the goal of enabling scalable simulations of time-dependent hyperbolic and chaotic problems on future architectures. The chosen methodology for solving these problems parallel-in-time is multigrid, because multigrid (when it works) is a powerful, optimal, and scalable solver for discretized PDEs. Multigrid is already commonly used in many DOE simulations for scalably and optimally solving space-only PDE problems. The areas of hyperbolic and chaotic problems are chosen because of their relevance to problems of programmatic interest to DOE. However, these problems are also well-known to be difficult for parallelin-time methods, with the most common method, parareal, diverging in many cases. The current stateof-the-art for parallel-in-time at LLNL is the multigrid reduction in time (MGRIT) XBraid package, which also struggles for such problems, while still showing some improvement over parareal. In summary, new methods are needed for an efficient parallel-in-time scheme for hyperbolic and chaotic problems, and this work shall research promising new multigrid methods in this area. In particular, we take inspiration from the Least Squares Shadowing (LSS by Wang) approach for solving chaotic problems. Here, an optimization approach is able to find “well-conditioned” shadow trajectories/solutions to the original “ill-conditioned” chaotic problem. Thus, the new multigrid methods researched here also arise in an optimization context.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Computational Science at Los Alamos National Laboratory (Rev. 1) [Slides]

High Performance Computing (HPC) enables problem solving at the grandest scales, on the most complex challenges that face humanity. The problems often tackled by scientific computing are: Very complex; Cool; Using the fastest, most powerful supercomputers on the planet; Often important, impactful, effecting real lives.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Improving the Advancement of Women in Computer and Computational Science Research with the CRA-W Career Mentoring Workshops (Final Report)

The mission of the Computing Research Association’s Committee on Widening Participation in Computing Research (CRA-WP) is to widen the participation and improve the access, opportunities, and positive experiences of individuals from groups underrepresented in computing research and education. CRA-WP programs serve this overarching goal at all career stages; in addition, CRA-WP, through the formation of the CRA Center for Evaluating the Research Pipeline (CERP), has developed a methodology for thoroughly evaluating the success of its programs by comparing a nationwide sample of students, researchers, and faculty (non-participants) to program participants. Achieving these objectives requires that an increasing number of individuals from populations underrepresented in computing start and progress to the next stage while understanding and supporting the myriad computing pathways. CRA-WP offers programs for participants from undergraduate to senior professional levels. Different career stages need different types of interventions, and the goal of all Alliance program activities can be described within the unifying framework of Social Cognitive Career Theory, which finds that interest in and choice of a particular career path will be increased by interventions that improve one or more of the following: (1) outcome expectations (understanding and valuing the rewards of a particular outcome), (2) self-efficacy (a belief that one can successfully achieve an outcome), and (3) social supports that help one persist and overcome obstacles.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Combinatorial Algorithms to Enable Computational Science and Engineering: Work from the CSCAPES Institute

This final progress report summarizes the work accomplished at the Combinatorial Scientific Computing and Petascale Simulations Institute. We developed Zoltan, a parallel mesh partitioning library that made use of accurate hypergraph models to provide load balancing in mesh-based computations. We developed several graph coloring algorithms for computing Jacobian and Hessian matrices and organized them into a software package called ColPack. We developed parallel algorithms for graph coloring and graph matching problems, and also designed multi-scale graph algorithms. Three PhD students graduated, six more are continuing their PhD studies, and four postdoctoral scholars were advised. Six of these students and Fellows have joined DOE Labs (Sandia, Berkeley), as staff scientists or as postdoctoral scientists. We also organized the SIAM Workshop on Combinatorial Scientific Computing (CSC) in 2007, 2009, and 2011 to continue to foster the CSC community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Final report- UFL - RAPIDS2: A SciDAC Institute for Computer Science, Data, and Artificial Intelligence

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Havery Mudd 2014-2015 Computer Science Conduit Clinic. Final Report

Conduit, a new open-source library developed at Lawrence Livermore National Laboratories, provides a C++ application programming interface (API) to describe and access scientific data. Conduit’s primary use is for inmemory data exchange in high performance computing (HPC) applications. Our team tested and improved Conduit to make it more appealing to potential adopters in the HPC community. We extended Conduit’s capabilities by prototyping four libraries: one for parallel communication using MPI, one for I/O functionality, one for aggregating performance data, and one for data visualization.

97 MATHEMATICS AND COMPUTING↗