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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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DOE OSTI2016

An Integrated Software Package to Enable Predictive Simulation Capabilities

The power grid is increasing in complexity due to the deployment of smart grid technologies. Such technologies vastly increase the size and complexity of power grid systems for simulation and modeling. This increasing complexity necessitates not only the use of high-performance-computing (HPC) techniques, but a smooth, well-integrated interplay between HPC applications. This paper presents a new integrated software package that integrates HPC applications and a web-based visualization tool based on a middleware framework. This framework can support the data communication between different applications. Case studies with a large power system demonstrate the predictive capability brought by the integrated software package, as well as the better situational awareness provided by the web-based visualization tool in a live mode. Test results validate the effectiveness and usability of the integrated software package.

Chen, Yousu↗

DOE OSTI2024

Visualizing an Exascale Data Center Digital Twin: Considerations, Challenges and Opportunities

Digital twins are an excellent tool to model, visualize, and simulate complex systems, to understand and optimize their operation. In this work, we present the technical challenges of real-time visualization of a digital twin of the Frontier supercomputer.We show the initial prototype and current state of the twin and highlight technical design challenges of visualizing such a large High Performance Computing (HPC) system. The goal is to understand the use of augmented reality as a primary way to extract information and collaborate on digital twins of complex systems. This leverages the spatio-temporal aspect of a 3D representation of a digital twin, with the ability to view historical and real-time telemetry, triggering simulations of a system state and viewing the results, which can be augmented via dashboards for details. Finally, we discuss considerations and opportunities for augmented reality of digital twins of large-scale, parallel computers.

Maiterth, Matthias↗

DOE OSTI2021

DXT Explorer v0.1

DTX Explorer is a tool to generate interactive data visualizations of Darshan I/O traces collected from HPC applications. Its goal is to provide an easy and interactive way for researchers and developers to explore their application's I/O behavior and detect possible I/O bottlenecks that are impacting performance.

Bez, Jean Luca↗

DOE OSTI2022

DXT Explorer v2.0

Bez, JeanLuca↗

DOE OSTI2020

Managing Dynamic Workflows in BEE

BEE is a powerful tool for: Managing and visualizing scientific workflows; Simplifying workflow execution on HPC and cloud platforms. BEE supports much of the CWL specification. Did not support execution of complex ”scattering” workflows. By introducing the PseudoTask: Can generate tasks to run on variable number of inputs; BEE is another step closer to supporting the entire CWL specification; BEE can now support parallelized workflows with scattering tasks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Advanced Computing Annual Report 2022

National Renewable Energy Laboratory's (NREL) high-performance computing (HPC), efficient data center operation, and state-of-the-art data visualization powered over 300 clean energy projects in Fiscal Year 2022 (FY22). 555 scientists and engineers utilized HPC resources to assess and integrate data into their research. The report highlights the arrival of new HPC Kestrel in 2023, the contributions of the Insight Center, and researchers looking into bias in artificial intelligence, alongside key research collaborations using advanced computing.

advanced computing↗

DOE OSTI2016

In Situ Visualization and Production of Extract Databases

Simulations running at high concurrency on HPC systems generate large volumes of data that are impractical to write to disk due to time and storage constraints. Applications often adapt by saving data infrequently, resulting in datasets with poor temporal resolution. This can make datasets difficult to interpret during post hoc visualization and analysis, or worse, it can lead to lost science. In Situ visualization and analysis can enable efficient production of small data products such as rendered images or surface extracts that consist of polygonal geometry plus fields. These data products are far smaller than their source data and can be processed much more economically in a traditional post hoc workflow using far fewer computational resources. We used the SENSEI and Libsim in situ infrastructures to implement rendering workflow and surface data extraction workflows in the AVF-LESLIE combustion code. Finally, these workflows were then demonstrated at high levels of concurrency and showed significant data reductions and limited impact on the simulation runtime.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Quantifying Uncertainty in HPC Job Queue Time Predictions

High Performance Computing (HPC) has developed at an unprecedented pace in recent decades. This growth has demanded corresponding development in the area of HPC Operational Data Analytics (ODA), which encompasses a wide range of data analysis techniques, ML/AI efforts, tools, and visualizations. Published studies in ODA offer a variety of practical ways to inform HPC users, administrators, procurement managers, and other stakeholders. Uncertainty analysis, however, is rare in the related published literature. For instance, we identify only 1 out of 14 existing studies focused on job queue time prediction that investigates the uncertainty aspect of their proposed predictions. We recognize the utmost importance uncertainty quantification can have in such predictive analytics solutions, with consequences in how users interpret information they receive, and attempt to bridge this gap. With the goal of improving access to such insights, we develop a process for determining upper and lower bounds of the predicted queue times of a regression model at a specified confidence level. Our current research is focused on the uncertainty in predicting job queue times, yet our approach may be employed in predicting other metrics.

HPC↗

DOE OSTI2018

Job Management with mpi_jm

About this book: This book constitutes the refereed post-conference proceedings of 13 workshops held at the 33rd International ISC High Performance 2018 Conference, in Frankfurt, Germany, in June 2018: HPC I/O in the Data Center, HPC-IODC 2018; Workshop on Performance and Scalability of Storage Systems, WOPSSS 2018; 13th Workshop on Virtualization in High-Performance Cloud Computing, VHPC 2018; Third International Workshop on In Situ Visualization, WOIV 2018; 4th International Workshop on Communication Architectures for HPC, Big Data, Deep Learning and Clouds at Extreme Scale, ExaComm 2018; International Workshop on OpenPOWER for HPC, IWOPH 2018; IXPUG Workshop: Many-Core Computing on Intel Processors; Workshop on Sustainable Ultrascale Computing Systems; Approximate and Transprecision Computing on Emerging Technologies, ATCET 2018; First Workshop on the Convergence of Large-Scale Simulation and Artificial Intelligence; Third Workshop for Open Source Supercomputing, OpenSuCo 2018; First Workshop on Interactive High-Performance Computing; Workshop on Performance Portable Programming Models for Accelerators, P^3MA 2018. The 53 full papers included in this volume were carefully reviewed and selected from 80 submissions. They cover all aspects of research, development, and application of large-scale, high performance experimental and commercial systems. Topics include HPC computer architecture and hardware; programming models, system software, and applications; solutions for heterogeneity, reliability, power efficiency of systems; virtualization and containerized environments; big data and cloud computing; and artificial intelligence.

Berkowitz, Evan↗

DOE OSTI2019

Parallel streaming between heterogeneous HPC resources for real-time analysis

Performing analysis or generating visualizations concurrently with high performance simulations can yield great benefits compared to post-processing data. Writing and reading large volumes of data can be reduced or eliminated, thereby producing an I/O cost savings. One such method for concurrent simulation and analysis is in transit - streaming data from the resource running the simulation to a separate resource running the analysis. In transit analysis can be beneficial since computational resources may not have certain resources needed for visualization and analysis (e.g. GPUs) and to reduce the impact of performing analysis tasks to the run time of the simulation. When sending and receiving data in transit, data redistribution mechanisms are needed in order to support heterogeneous data layouts that may be required by the simulation and analysis applications. The work described in this paper compares two mechanisms for on-the-fly data redistribution when streaming data in parallel between two distributed memory applications. Finally, our results show that it is often more advantageous to stream data in the same layout as the sender and redistribute data amongst processes on the receiving end than to stream data in the final layout needed by the receiver.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

From Data to Models and Analytics: An Integrated Scientific Computing Platform to Accelerate Environmental Research SBIR Phase I (Final Technical Report)

Phase I research was conducted to investigate the technical feasibility of an integrated open source platform GeoLib to provide turnkey solutions for model-data integration for environmental systems science (ESS), using modern web technologies, advances in HPC and cloud computing and high-performance visualization. A key feature of our platform is that it minimizes and automates data movement between data storage and computing systems. Another critical feature of the design is that it enables users to perform high-fidelity post-processing by performing high-performance visualization server-side and in-situ on the compute cluster close to the data. The investigation, which was performed over a 12-month period ending April 2019, centered on developing prototype software to demonstrate the key enabling technologies required for this system. This included: (i) rapid access to data from multiple remote sources, via a unified data interface; (ii) software for scalable geospatial visualization of raster, vector, and point cloud data on the web; (iii) development of an easy-to-use API for creating simulation and modeling workflows; (iv) remote data visualization and processing on middle-tier servers; and (v) remote data processing on high-performance computing systems. A critical part of this work is that all of these capabilities be accessible to end users through standard desktop tools such as Project Jupyter, which is widely adopted for ESS simulation and modeling.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Computational Math Problems for a Clean Energy Future

Cutting edge computational mathematics are ubiquitous in renewable energy research. Problems in resilient and reliable electric grid operations, infrastructure planning, wind farm yaw control, and more demand sophisticated and scalable computational tools that enable the transition of renewable energy technologies from proof of concept to deployment into our energy system. The mission of the Computational Science Center at NREL is to lead the lab's efforts to solve energy challenges using high-performance computing (HPC), computational science, applied mathematics, scientific data management, visualization, and informatics. In this poster, we provide a short overview of three areas of computational mathematics research at NREL: wind power scenario generation for stochastic grid operations and infrastructure planning, improved rational function approximations for electromagnetic transients codes, and wind farm yaw control using a combination of the Alternating Direction Method of Multipliers (ADMM) and reinforcement learning (RL). Increasing penetrations of renewable energy into power grids motivate the investigation of new approaches to characterizing uncertainty for five-minute economic dispatch problems. Similarly, as the penetration of distributed energy resources on power grids increases, it becomes important to revisit our methods of modelling transient phenomena, i.e. electromagnetic transients programs. Finally, the combination of ADMM and RL for wind farm yaw control presented here can potentially increase the efficiency of the deployed distributed controllers by orders of magnitude.

ADMM↗

DOE OSTI2015

Advanced simulation capability for environmental management, integrated tool-sets and simulator to enhance public communication - 15186

The U.S. Department of Energy (US DOE) Office of Environmental Management (EM), Office of Soil and Groundwater Remediation, is supporting multi-laboratory development of the Advanced Simulation Capability for Environmental Management (ASCEM). ASCEM is an open source and modular computing framework that incorporates new advances and tools for predicting contaminant fate and transport in natural and engineered systems. ASCEM facilitates transparent, efficient and integrated approaches to modeling and site characterization, and provides robust and standardized assessments of performance and risk for EM cleanup and closure activities. Site subsurface simulation modeling and performance assessments are complex and involve sensitivity evaluations and uncertainty quantification to better understand the risks at a site. The ASCEM tool-set was designed in close collaboration with the sites and other potential end-users (both the public and regulators) to allow better communication and understanding of these complex modeling efforts. ASCEM's integrated tool-sets provide a user interface and tools for managing work-flows, including conceptual model development, management of data and metadata for model input, sensitivity analysis, model calibration and uncertainty analysis, model execution on diverse computational platforms, and processing of model output, including visualization. The hierarchical and modular design of ASCEM HPC simulator Amanzi easily lends itself to translating this conceptual model of a site into a numerical model that can easily be spatially and temporally discretized to run on the HPC simulator. Together this integrated work-flow allows for a seamless and integrated analysis tracking all provenance information and modeling uncertainties. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2017

Capturing provenance as a diagnostic tool for workflow performance evaluation and optimization

In extreme-scale computing environments such as the DOE Leadership Computing Facilities scientific workflows are routinely used to coordinate software processes for the execution of complex, computational applications that perform in-silico experiments. Monitoring the performance of workflows without also simultaneously tracking provenance is not sufficient to understand variations between runs, configurations, versions of a code, and between changes in an implemented stack, and systems, i.e. the variability of performance metrics data in their historical context. We take a provenance-based approach and demonstrate that provenance is useful as a tool for evaluating and optimizing workflow performance in extreme-scale HPC environments. We present Chimbuko, a framework for the analysis and visualization of the provenance of performance. Chimbuko implements a method for the evaluation of workflow performance from multiple components that enables the exploration of performance metrics data at scale.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Quantifying the Impact of Advanced Web Platforms on High Performance Computing Usage

The deployment of Science Gateways for High Performance Computing (HPC) systems can alter long-accepted usage patterns on supercomputing systems in positive ways as an ever-increasing number of users migrate their workflows to HPC systems. Idaho National Laboratory (INL) has deployed two separate advanced web platforms, Open OnDemand and NICE DCV, for integration with HPC resources to improve web accessibility for HPC users. Researchers conducted a multi-year study on how HPC usage pat- terns changed in the presence of these platforms. This work reports the results of that study and quantifies the observed impacts, including adoption by visualization and Jupyter Notebook/Lab users, decreased job submission friction, rapid uptake of HPC by Windows users, and increased overall system utilization. The most significant impacts were observed from the deployment of Open OnDemand, and this work also identifies some best practices for Open OnDemand deployment for HPC datacenters.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Integrated System and Application Continuous Performance Monitoring and Analysis Capability

Scientific applications run on high-performance computing (HPC) systems are critical for many national security missions within Sandia and the NNSA complex. However, these applications often face performance degradation and even failures that are challenging to diagnose. To provide unprecedented insight into these issues, the HPC Development, HPC Systems, Computational Science, and Plasma Theory & Simulation departments at Sandia crafted and completed their FY21 ASC Level 2 milestone entitled "Integrated System and Application Continuous Performance Monitoring and Analysis Capability." The milestone created a novel integrated HPC system and application monitoring and analysis capability by extending Sandia's Kokkos application portability framework, Lightweight Distributed Metric Service (LDMS) monitoring tool, and scalable storage, analysis, and visualization pipeline. The extensions to Kokkos and LDMS enable collection and storage of application data during run time, as it is generated, with negligible overhead. This data is combined with HPC system data within the extended analysis pipeline to present relevant visualizations of derived system and application metrics that can be viewed at run time or post run. This new capability was evaluated using several week-long, 290-node runs of Sandia's ElectroMagnetic Plasma In Realistic Environments ( EMPIRE ) modeling and design tool and resulted in 1TB of application data and 50TB of system data. EMPIRE developers remarked this capability was incredibly helpful for quickly assessing application health and performance alongside system state. In short, this milestone work built the foundation for expansive HPC system and application data collection, storage, analysis, visualization, and feedback framework that will increase total scientific output of Sandia's HPC users.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Integrated System and Application Continuous Performance Monitoring and Analysis Capability (Final)

Scientific applications run on high-performance computing (HPC) systems are critical for many national security missions within Sandia and the NNSA complex. However, these applications often face performance degradation and even failures that are challenging to diagnose. To provide unprecedented insight into these issues, the HPC Development, HPC Systems, Computational Science, and Plasma Theory & Simulation departments at Sandia crafted and completed their FY21 ASC Level 2 milestone entitled "Integrated System and Application Continuous Performance Monitoring and Analysis Capability." The milestone created a novel integrated HPC system and application monitoring and analysis capability by extending Sandia’s Kokkos application portability framework, Lightweight Distributed Metric Service (LDMS) monitoring tool, and scalable storage, analysis, and visualization pipeline. The extensions to Kokkos and LDMS enable collection and storage of application data during run time, as it is generated, with negligible overhead. This data is combined with HPC system data within the extended analysis pipeline to present relevant visualizations of derived system and application metrics that can be viewed at run time or post run. This new capability was evaluated using several week-long, 290-node runs of Sandia’s ElectroMagnetic Plasma In Realistic Environments (EMPIRE) modeling and design tool and resulted in 1TB of application data and 50TB of system data. EMPIRE developers remarked this capability was incredibly helpful for quickly assessing application health and performance alongside system state. In short, this milestone work built the foundation for expansive HPC system and application data collection, storage, analysis, visualization, and feedback framework that will increase total scientific output of Sandia’s HPC users.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

Rasterization with Data-Parallel Primitives

Parallel rasterization can suffer from race conditions during fragment generation, which is traditionally addressed by using specialized hardware accessible via vendor graphics APIs. Unfortunately, graphics APIs are increasingly problematic on high-performance computers, either because they are not provided or because of concerns about dependencies with in situ visualization. In response, we present a hardware-agnostic rasterization algorithm that handles race conditions using only data-parallel primitives (DPPs), enabling efficient rendering on HPC systems without graphics API dependencies and aligning with recent efforts to deliver visualization software with DPPs. Our evaluation consists of three phases: (1) evaluating portability across different CPU and GPU architectures, (2) evaluating competitiveness with a community standard, and (3) evaluating performance across varying workloads and available parallelism. The supporting experiments run on both AMD and NVIDIA GPUs, considering data sets as large as 460 million triangles and 160 million pixels. While performance generally falls short of graphics API baselines, it achieves interactive frame rates on most workloads. As a result, we conclude our approach is a viable solution for rasterization on high-performance computers since our approach is portably performant across different architectures without the need for specialized vendor support.

Buckley, Makani [University of Oregon] (ORCID:0009↗