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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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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

The MPI_T events interface: An early evaluation and overview of the interface

Understanding the behavior of parallel applications that use the Message Passing Interface (MPI) is critical for optimizing communication performance. Performance tools for MPI currently rely on the PMPI Profiling Interface or the MPI Tool Information Interface, MPI_T, for portably collecting information for performance measurement and analysis. While tools using these interfaces have proven to be extremely valuable for performance tuning, these interfaces only provide synchronous information, i.e., when an MPI or an MPI_T function is called. There is currently no option for collecting information about asynchronous events from within the MPI library. Here, we propose a callback-driven interface for event notification from MPI implementations. Our approach is integrated in the existing MPI_T interface and provides a portable API for tools to discover and register for events of interest. We implement our MPI_T Events interface in Open MPI and demonstrate its functionality and usability with a small logging tool (MEL) as well as an early integration into the comprehensive measurement infrastructure Score-P.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Refining HPCToolkit for application performance analysis at exascale

As part of the US Department of Energy’s Exascale Computing Project (ECP), Rice University has been refining its HPCToolkit performance tools to better support measurement and analysis of applications executing on exascale supercomputers. To efficiently collect performance measurements of GPU-accelerated applications, HPCToolkit employs novel non-blocking data structures to communicate performance measurements between tool threads and application threads. To attribute performance information in detail to source lines, loop nests, and inlined call chains, HPCToolkit performs parallel analysis of large CPU and GPU binaries involved in the execution of an exascale application to rapidly recover mappings between machine instructions and source code. To analyze terabytes of performance measurements gathered during executions at exascale, HPCToolkit employs distributed-memory parallelism, multithreading, sparse data structures, and out-of-core streaming analysis algorithms. To support interactive exploration of profiles up to terabytes in size, HPCToolkit’s hpcviewer graphical user interface uses out-of-core methods to visualize performance data. The result of these efforts is that HPCToolkit now supports collection, analysis, and presentation of profiles and traces of GPU-accelerated applications at exascale. These improvements have enabled HPCToolkit to efficiently measure, analyze and explore terabytes of performance data for executions using as many as 64K MPI ranks and 64K GPU tiles on ORNL’s Frontier supercomputer. HPCToolkit’s support for measurement and analysis of GPU-accelerated applications has been employed to study a collection of open-science applications developed as part of ECP. This paper reports on these experiences, which provided insight into opportunities for tuning applications, strengths and weaknesses of HPCToolkit itself, as well as unexpected behaviors in executions at exascale.

Adhianto, Laksono↗
DOE OSTI2015

Simplified Method for Modeling the Impact of Arbitrary Partial Shading Conditions on PV Array Performance: Preprint

It is often difficult to model the effects of partial shading conditions on PV array performance, as shade losses are nonlinear and depend heavily on a system's particular configuration. This work describes and implements a simple method for modeling shade loss: a database of shade impact results (loss percentages), generated using a validated, detailed simulation tool and encompassing a wide variety of shading scenarios. The database is intended to predict shading losses in crystalline silicon PV arrays and is accessed using basic inputs generally available in any PV simulation tool. Performance predictions using the database are within 1-2% of measured data for several partially shaded PV systems, and within 1% of those predicted by the full, detailed simulation tool on an annual basis. The shade loss database shows potential to considerably improve performance prediction for partially shaded PV systems.

14 SOLAR ENERGY↗
DOE OSTI2020

Performance and usability enhancements for continuous subgraph matching queries on graph-structured data

A query graph, which includes vertices and edges, represents a query on graph-structured data. The query graph is decomposed into query subgraphs. A network analysis tool performs continuous subgraph matching queries to facilitate analysis of computer network traffic, social media events, or other streams of data represented as a dynamic data graph (graph-structured data). This can help identify emerging trends in the data. Some features of the network analysis tool enhance performance by effectively utilizing distributed computing resources (including processing cores and memory at different nodes of a cluster) to speed up the process of updating the dynamic data graph and detecting matches of query subgraphs. Features of a query graph building tool enhance usability by providing intuitive ways to specify query graphs and their subgraphs. Features of a results visualization tool enhance usability by providing an intuitive way to present the results of continuous subgraph matching queries.

Choudhury, Sutanay↗
DOE OSTI2024

SOMA: Observability, monitoring, and in situ analytics for exascale applications

With the rise of exascale systems and large, data-centric workflows, the need to observe and analyze high performance computing (HPC) applications during their execution is becoming increasingly important. HPC applications are typically not designed with online monitoring in mind, therefore, the observability challenge lies in being able to access and analyze interesting events with low overhead while seamlessly integrating such capabilities into existing and new applications. We explore how our service-based observation, monitoring, and analytics (SOMA) approach to collecting and aggregating both application-specific diagnostic data and performance data addresses these needs. Furthermore, we present our SOMA framework and demonstrate its viability with LULESH, a hydrodynamics proxy application. Then we focus on Astaroth, a multi-GPU library for stencil computations, highlighting the integration of the TAU and APEX performance tools and SOMA for application and performance data monitoring.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

High Performance Computing Systems Tools, Visualization, and Management

High Performance Computing (HPC) systems are complex setups of servers, storage devices, network switches, and cables that are specifically designed to accommodate hundreds of users running highly computationally intensive applications at a time. These applications require numerous softwares, licenses, and various levels of storage as well. All of these resources must be monitored and managed by HPC administrators, which presents a daunting task. In this project, I created numerous software tools as part of an HPC Visualization and Management system, which is now used by HPC administrators on a daily basis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

BAMCensus (The Behavior and Advanced Mobility Census Dataset Aggregator) [SWR-25-120]

This software is a high-performance tool developed in Rust for downloading and processing large-scale geospatial datasets, specifically focusing on US Census data. It is designed to address scaling limitations found in existing tools, such as R's [tidycensus](https://walker-data.com/tidycensus/), by providing performant streaming dataset JOIN operations between various US Census datasets (like ACS and LEHD) and their corresponding geometries stored on the TIGER/Lines web server. The tool automates the process of joining these data sources, returning aggregated data to the user based on a specified census GEOID type. The tool automates the process of joining these data sources, returning aggregated data to the user based on a specified census GEOID type. Its primary motivation stems from the need for a high-performance solution to combine spatial datasets with graph traversals within the context of mobility analysis tooling being developed at NREL's Behavior and Advanced Mobility (BAM) group.

Fitzgerald, Robert [National Renewable Energy Labo↗
DOE OSTI2025

mvBayesR

SAND2025-11559O The mvBayesR tool performs multivariate Bayesian analysis on generic data. It includes tools for regression modeling, diagnosis, basis decomposition, sensitivity analysis, and visualization. The tool compiles state-of-the-art methodology into one easy-to-use package. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Tucker, James [Sandia National Lab. (SNL-CA), Live↗
DOE OSTI2025

Exploring Large Language Model Agents in Cybersecurity: A Literature Review with Experiments

The accelerated development and integration of large language model (LLM) agents have led researchers and developers to explore their effectiveness in cybersecurity, specifically with penetration testing (pentesting). Recent research efforts have attempted to use LLM agents to automate the process of pentesting because of the cost and time requirements that are required to perform a manual review. However, not all of the tools perform as expected. This paper reviews some of the newest and most popular autonomous pentesting frameworks, highlighting the capabilities and limitations of each one with the goal of providing the components needed to successfully and effectively build an autonomous pentesting agent in the future.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Measurement and analysis of GPU-accelerated applications with HPCToolkit

To address the challenge of performance analysis on the US DOE’s forthcoming exascale supercomputers, Rice University has been extending its HPCToolkit performance tools to support measurement and analysis of GPU-accelerated applications. To help developers understand the performance of accelerated applications as a whole, HPCToolkit’s measurement and analysis tools attribute metrics to calling contexts that span both CPUs and GPUs. To measure GPU-accelerated applications efficiently, HPCToolkit employs a novel wait-free data structure to coordinate monitoring and attribution of GPU performance. To help developers understand the performance of complex GPU code generated from high-level programming models, HPCToolkit constructs sophisticated approximations of call path profiles for GPU computations. To support fine-grained analysis and tuning, HPCToolkit uses PC sampling and instrumentation to measure and attribute GPU performance metrics to source lines, loops, and inlined code. To supplement fine-grained measurements, HPCToolkit can measure GPU kernel executions using hardware performance counters. To provide a view of how an execution evolves over time, HPCToolkit can collect, analyze, and visualize call path traces within and across nodes. Finally, on NVIDIA GPUs, HPCToolkit can derive and attribute a collection of useful performance metrics based on measurements using GPU PC samples. Here, we illustrate HPCToolkit’s new capabilities for analyzing GPU-accelerated applications with several codes developed as part of the Exascale Computing Project.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

SYMBIOSYS: A Methodology for Performance Analysis of Composable HPC Data Services

Microservices are a powerful new way of building, customizing, and deploying distributed services owing to their flexibility and maintainability. Several large-scale distributed platforms have emerged to serve the growing needs of data-centric workloads and services in commercial computing. Concurrently, high-performance computing (HPC) systems and software are rapidly evolving to meet the demands of diversified applications and heterogeneity. The interplay of hardware factors, software configuration parameters, and the flexibility offered with a microservice architecture makes it nontrivial to estimate the optimal service instantiation for a given application workload. Further, this problem is exacerbated when considering that these services operate in a dynamic and heterogeneous HPC environment. An optimally integrated service can be vastly more performant than a haphazardly integrated one. Existing performance tools for HPC either fail to understand the request-response model of communication inherent to microservices or they operate within a narrow scope, limiting the insight that can be gleaned from employing them in isolation. We propose a methodology for integrated performance analysis of HPC microservices frameworks and applications called SYMBIOSYS. We describe its design and implementation within the context of the Mochi framework. This integration is achieved by combining distributed callpath profiling and tracing with a performance data exchange strategy that collects fine-grained, low-level metrics from the RPC communication library and network layers. The result is a portable, low-overhead performance analysis setup that provides a holistic profile of the dependencies among microservices and how they interact with the Mochi RPC software stack. Using HEPnOS, a production-quality Mochi data service, we demonstrate the low-overhead operation of SYMBIOSYS at scale and use it to identify the root causes of poorly performing service configurations.

microservices↗
DOE OSTI2021

BeeSwarm: Enabling Parallel Scaling Performance Measurement in Continuous Integration for HPC Applications

Testing is one of the most important steps in software development–it ensures the quality of software. Continuous Integration (CI) is a widely used testing standard that can report software quality to the developer in a timely manner during development progress. Performance, especially scalability, is another key factor for High Performance Computing (HPC) applications. There are many existing profiling and performance tools for HPC applications, but none of these are integrated into CI tools. In this work, we propose BeeSwarm, an HPC container based parallel scaling performance system that can be easily applied to the current CI test environments. BeeSwarm is mainly designed for HPC application developers who need to monitor how their applications can scale on different compute resources. We demonstrate BeeSwarm using a multi-physics HPC application with Travis CI, GitLab CI and GitHub Actions while using ChameleonCloud and Google Compute Engine as the compute backends. Finally, our results show that BeeSwarm can be used for scalability and performance testing of HPC applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Tailoring Thermal and Mechanical Performance Through Multimaterial Laser Powder Directed Energy Deposition of Copper and 17-4PH Stainless Steel

This study investigates the additive manufacturing (AM) processing, microstructural evolution, and resulting mechanical and thermal properties of multimaterial components combining 17-4PH stainless steel and pure copper (Cu) fabricated via laser powder directed energy deposition (LP-DED). Conventional tooling steels exhibit limited thermal conductivity, significantly constraining production throughput in high-volume processes. Incorporating Cu, with its superior thermal conductivity, could significantly enhance tool performance, though Cu and steel present metallurgical incompatibilities when processed via AM. A systematic investigation was conducted across compositions ranging from 0 to 100 wt% Cu, revealing critical thresholds influencing solidification behavior, defect formation, microstructure, hardness, and thermal transport. Optical microscopy, electron backscatter diffraction (EBSD), hardness testing, and thermal conductivity measurements provided comprehensive process–structure–property correlations. Severe hot cracking occurred at low-Cu contents (6–25 wt%), aligning generally well with crack susceptibility modeling, with an unexpected discrepancy at 25 wt%. Porosity remained low (≥99% dense) throughout the compositional spectrum. EBSD analysis revealed a transformation from columnar martensitic structures at low-Cu contents to equiaxed FCC Cu-dominated structures at higher Cu concentrations, highlighting the complex microstructural transitions driven by Cu-induced changes in solidification and phase stability. Hardness decreased from 330 HV (pure 17-4PH) to 62 HV (pure Cu), consistent with microstructural changes. Concurrently, thermal conductivity improved substantially from 13.5 W/m K to 367.9 W/m K, emphasizing Cu’s dominant role in thermal transport. The findings highlight the feasibility of leveraging compositional gradients between 17-4PH and Cu to achieve tailored tooling with optimized thermal and mechanical performance.

17-4PH↗
DOE OSTI2018

Overcoming low EIA performance - A diagnostic tool for the deliberate development of EIA system capacities in low and middle income countries

Highlights: • A tool for rapid assessment of EIA performance is presented. • The tool focusses on EIA systems in low and middle income countries. • This tool aims to enhance the capacities of the main EIA organizations. • Mechanisms are identified to maintain and further develop the enhanced capacities.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

SUPPORTING THE INDUSTRY BY DEVELOPING A DESIGN GUIDANCE FOR COMPUTER-BASED PROCEDURES FOR FIELD WORKERS

The paper-based procedures currently used for nearly all activities in the commercial nuclear power industry have a long history of ensuring safe operation of the plants. However, there is potential to greatly increase efficiency and safety by improving how the human interacts with the procedures, which can be achieved through the use of computer-based procedures (CBPs). A CBP system offers a vast variety of improvements, such as context driven job aids, integrated human performance tools and dynamic step presentation. As a step toward the goal of improving procedure use performance, the U.S. Department of Energy Light Water Reactor Sustainability Program researchers, together with the nuclear industry, have been investigating the possibility and feasibility of replacing current paper-based procedures with CBPs. The main purpose of the CBP research conducted at the Idaho National Laboratory was to provide design guidance to the nuclear industry to be used by both utilities and vendors. After studying existing design guidance for CBP systems, the researchers concluded that the majority of the existing guidance is intended for control room CBP systems, and does not necessarily address the challenges of designing CBP systems for instructions carried out in the field. Further, the guidance is often presented on a high level, which leaves the designer to interpret what is meant by the guidance and how to specifically implement it. The authors developed a design guidance to provide guidance specifically tailored to instructions that are carried out in the field based.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Supporting the industry by developing a design guidance for computer-based procedures for field workers - 274

The paper-based procedures currently used for nearly all activities in the commercial nuclear power industry have a long history of ensuring safe operation of the plants. However, there is potential to greatly increase efficiency and safety by improving how the human interacts with the procedures, which can be achieved through the use of computer-based procedures (CBPs). A CBP system offers a vast variety of improvements, such as context driven job aids, integrated human performance tools and dynamic step presentation. As a step toward the goal of improving procedure use performance, the U.S. Department of Energy Light Water Reactor Sustainability Program researchers, together with the nuclear industry, have been investigating the feasibility of replacing current paper-based procedures with CBPs. The main purpose of the CBP research for nuclear field workers conducted at the Idaho National Laboratory was to provide design guidance to the nuclear industry to be used by both utilities and vendors. After studying existing design guidance for CBP systems, the researchers concluded that the majority of the existing guidance is intended for control room CBP systems, and does not necessarily address the challenges of designing CBP systems for instructions carried out in the field. Further, the guidance is often presented on a high level, which leaves the designer to interpret what is meant by the guidance and how to specifically implement it. The authors developed a design guidance to provide guidance specifically tailored to instructions that are carried out in the field based. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

PV Shading Database [SWR-16-36]

The software is a simplified method for modeling the impact of arbitrary partial shading conditions on PV array performance. The software implements a simple method for modeling shade loss: a database of shade impact results (loss percentages), generated using a validated, detailed simulation tool and encompassing a wide variety of shading scenarios. The database is intended to predict shading losses in crystalline silicon PV arrays and is accessed using basic inputs generally available in any PV simulation tool. Performance predictions using the database are within 1-2% of measured data for several partially shaded PV systems, and within 1% of those predicted by the full, detailed simulation tool on an annual basis. The shade loss database shows potential to considerably improve performance prediction for partially shaded PV systems.

Janzou, Steven↗
DOE OSTI2025

Quantum Computing Strategy 2026

Quantum computing (QC) is a rapidly maturing technology with the potential for revolutionary impacts on stockpile stewardship science and national security. Recent developments in fault-tolerant architectures have compressed vendor roadmaps, and predictions of a production-ready quantum computer by the mid-2030s are becoming increasingly credible. This strategy provides a roadmap for integrating QC into the Advanced Simulation and Computing (ASC) program by investing in four strategic focus areas: 1. Develop Capabilities in Mission-Relevant Quantum Applications: ASC will prioritize developing quantum-ready applications in mission areas that have shown significant promise for quantum advantage, including simulations of materials in extreme environments, nuclear dynamics, solving linear and nonlinear partial differential equations, and uncertainty quantification. These applications directly support stockpile stewardship science and modernization objectives. 2. Conduct R&D in Algorithms, Software, and Hardware: Sustained research into quantum algorithms, robust software tools, and quantum hardware is essential. ASC will develop efficient quantum algorithms; invest in quantum compilers, debuggers, and performance tools; and explore specialized quantum hardware tailored to NNSA’s unique requirements. 3. Engage with Vendors and Partners: Early and active collaboration with commercial quantum hardware vendors and academic partners is critical. Through testbeds, co-design agreements, and quantum demonstration facilities, ASC will influence hardware design, gain early access to emerging technologies, and ensure that quantum platforms evolve to meet mission needs. 4. Build Knowledge, Experience, and Workforce: Expanding and upskilling the quantum-trained workforce is essential to long-term success. This includes hiring, internal training, university outreach, and postdoctoral support to ensure ASC maintains the expertise required to operate, program, and integrate quantum systems as they become available. While quantum computing will never replace classical computing, it has the potential to solve certain problems with speed and accuracy that would be unachievable using any conceivable classical high-performance computing (HPC) system. By investing strategically in QC, ASC will help propel the emergent QC industry, maintain U.S. technological leadership, ensure mission readiness, and position itself to rapidly adopt quantum technologies as they mature.

97 MATHEMATICS AND COMPUTING↗