Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Summit”

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.

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

At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A Community Roadmap for Scientific Workflows Research and Development

The landscape of workflow systems for scientific applications is notoriously convoluted with hundreds of seemingly equivalent workflow systems, many isolated research claims, and a steep learning curve. To address some of these challenges and lay the groundwork for transforming workflows research and development, the WorkflowsRI and ExaWorks projects partnered to bring the international workflows community together. This paper reports on discussions and findings from two virtual “Workflows Community Summits” (January and April, 2021). The overarching goals of these workshops were to develop a view of the state of the art, identify crucial research challenges in the workflows community, articulate a vision for potential community efforts, and discuss technical approaches for realizing this vision. To this end, participants identified six broad themes: FAIR computational workflows; AI workflows; exascale challenges; APIs, interoperability, reuse, and standards; training and education; and building a workflows community. We summarize discussions and recommendations for each of these themes.

Ferreira Da Silva, Rafael↗
DOE OSTI2021

Combined Structural Analysis of Core and Image Log of TGH 76-31 South East of Mt Baker, Washington State

Despite active volcanism, few geothermal energy resources have been developed in the Cascades Range. Temperature Gradient Hole 76-31 was drilled to ~440 m measured depth to probe for zones where fractures provide fluid conduits that transport deep volcanic heat to shallow depths that could support baseload, carbon neutral electrical generation. These zones were predicted by a Play Fairway assessment (PFA) of resource potential along a zone 11 km west-southwest of the summit of Mount Baker Volcano. Rock core, temperature logs, and an acoustic image log were obtained. By comparison to outcrops, the core has been initially interpreted as the Chilliwack group comprised of partially metamorphosed basaltic to andesitic volcanics. Core mapping reveals complex, steeply dipping networks of fractures and brecciation along slickensided strike slip faults; clay alteration is common in many of these structures. The majority of fractures are thoroughly healed by layers of chlorite and calcite; preservation of pyrite indicates anoxic conditions. The majority of fracture porosity resides in very dense fractures a few centimeters or less in length. The image log provides good insights into attitude of fractures that fully transect the core, but generally underestimates fracture density. The combination of complex, non-planar fracture zones containing many short fractures and healing promote misinterpretation of natural fracture attitude and density in the image log. The healing and anoxic minerals are consistent with the conductive temperature gradient measured in the well below a shallow isothermal zone, although, several fractures are open or only partially healed and resulted in fluid entries into the well. Here, the equilibrated measured temperature gradient of 64ºC/km and calculated heat flow of 145 mW/m2 is more than twice the regional average, indicating local influence of the Mt Baker magmatic system at the Little Park Creek TGH site.

Cascades↗
DOE OSTI2022

Improving the Performance of DGEMM with MoA and Cache-Blocking: Preprint

The goal of this paper is to demonstrate performance enhancements of the high performance dense linear algebra matrix-matrix multiply DGEMM kernel, widely implemented by vendors in the basic linear algebra subroutine BLAS library. The mathematics of arrays (MoA) paradigm due to Mullin (1988) results in contiguous memory accesses in combination with Church-Rosser complete language constructs optimized for target processor architectures [3]. Our performance studies demonstrate that the MoA implementation of DGEMM combined with optimal cache-blocking strategies results in at least a 25% performance gain on both Intel Xeon Skylake and IBM Power-9 processors over the vendor supplied Intel MKL and IBM ESSL basic linear algebra libraries. Results are presented for the NREL Eagle and ORNL Summit supercomputers.

cache-blocking↗
DOE OSTI2022

Anderson Acceleration for Distributed Training of Deep Learning Models

Anderson acceleration (AA) is an extrapolation technique that has recently gained interest in the deep learning (DL) community to speed-up the sequential training of DL models. However, when performed at large scale, the DL training is exposed to a higher risk of getting trapped into steep local minima of the training loss function, and standard AA does not provide sufficient acceleration to escape from these steep local minima. This results in poor generalizability and makes AA ineffective. To restore AA’s advantage to speed-up the training of DL models on large scale computing platforms, we combine AA with an adaptive moving average procedure that boosts the training to escape from steep local minima. By monitoring the relative standard deviation between consecutive iterations, we also introduce a criterion to automatically assess whether the moving average is needed. We applied the method to the following DL instantiations for image classification: (i) ResNet50 trained on the open-source CIFAR100 dataset and (ii) ResNet50 trained on the open-source ImageNet1k dataset. Numerical results obtained using up to 1,536 NVIDIA V100 GPUs on the OLCF supercomputer Summit showed the stabilizing effect of the moving average on AA for all the problems above.

Lupo Pasini, Massimiliano↗
DOE OSTI2022

Overview of Integrated Pathway Analyses to Meet the Hydrogen Energy Earthshot Goal

This slide deck was presented at the 2021 DOE Hydrogen Shot Summit on Aug. 31, 2021. The presentation describes two ongoing NETL system analysis efforts: "Comparison of Commercial, State-of-the-Art, Fossil-Based Hydrogen Production Technologies" (nearing completion), and "Hydrogen Energy Earthshot Initiative Screening Analysis" (which has recently started). The former study will detail costs and greenhouse gas emissions profiles of current commercial technologies (e.g., reforming and gasification routes). The latter will focus on our efforts to identify pathways that may meet the goal of the DOE Hydrogen Energy Earthshot Initiative (i.e., $1/kg H2 by 2030) as well as low carbon emissions.

Lewis, Eric↗
DOE OSTI2021

Stable Parallel Training of Wasserstein Conditional Generative Adversarial Neural Networks : *Full/Regular Research Paper submission for the symposium CSCI-ISAI: Artificial Intelligence

We use a stable parallel approach to train Wasserstein Conditional Generative Adversarial Neural Networks (W-CGANs). The parallel training reduces the risk of mode collapse and enhances scalability by using multiple generators that are concurrently trained, each one of them focusing on a single data label. The use of the Wasserstein metric reduces the risk of cycling by stabilizing the training of each generator. We apply the approach on the CIFAR10 and the CIFAR100 datasets, two standard benchmark datasets with images of the same resolution, but different number of classes. Performance is assessed using the inception score, the Fréchet inception distance, and image quality. An improvement in inception score and Fréchet inception distance is shown in comparison to previous results obtained by performing the parallel approach on deep convolutional conditional generative adversarial neural networks (DC-CGANs). Weak scaling is attained on both datasets using up to 100 NVIDIA V100 GPUs on the OLCF supercomputer Summit.

Lupo Pasini, Massimiliano↗
DOE OSTI2022

Global Energy Policy to Achieve the United States’ Nationally Determined Contributions: Potential Pathway

In 2021, the United States declared its Nationally Determined Contribution (NDC) in preparation for the COP26 climate summit, setting goals to achieve zero emission electricity by 2035, and reducing net emissions by 50–52% compared to 2005 levels. The current U.S. energy policies were found to not be adequate to meet these goals, thus it is necessary to draft a pathway to achieving these goals through policy changes. This study focused on foreign energy policies to analyze the most effective policies in a host of sectors. By identifying the most ambitious and effective policies from around the world in each sector, this study was able to suggest a policy pathway that can potentially achieve both of goals set in the NDC. In guiding the development of the proposed policy pathway, this study analyzed policies from Australia, Canada, California, China, Denmark, France, Germany, Japan, Russia, South Korea, Sweden, the United Kingdom, and the United States. This study found that the most drastic CO2 reductions came from low-carbon electricity policy, but contributions were also made from carbon capture, reforestation, hydrogen, energy efficiency, and climate smart agriculture.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Two-link Staggered Quark Smearing in QUDA

Gauge covariant smearing based on the 3D lattice Laplacian can be used to create extended operators that have better overlap with hadronic ground states. For staggered quarks, we make use of two-link parallel transport to preserve taste properties. We have implemented the procedure in QUDA. We present the performance of this code on the NVIDIA A100 GPUs in Indiana University's Big Red 200 supercomputer and on the AMD MI250X GPUs in Oak Ridge Leadership Computer Facility's (OLCF's) Crusher and discuss its scalability. We also study the performance improvement from using NVSHMEM on OLCF's Summit. Reusing precomputed two-link products for all sources and sinks, it reduces the total smearing time for a baryon correlator measurement by a factor of 100-120 as compared with the original MILC code and reduces the overall time by 60-70%.

Gottlieb, Steven↗
DOE OSTI2022

Toward designing effective exascale scientific computing workflows: experiences and best practices

Many fields within scientific computing have embraced advances in big-data analysis and machine learning, which often requires the deployment of large, distributed and complicated workflows that may combine training neural networks, performing simulations, running inference, and performing database queries and data analysis in asynchronous, parallel and pipelined execution frameworks. Such a shift has brought into focus the need for scalable, efficient workflow management solutions with reproducibility, error and provenance handling, traceability, and checkpoint-restart capabilities, among other needs. Here, we discuss challenges and best-practices for deploying exascale-generation computational science workflows on resources at the Oak Ridge Leadership Computing Facility (OLCF). We present our experiences with large-scale deployment of distributed workflows on the Summit supercomputer, including for bioinformatics and computational biophysics, materials science, and deep learning model optimization. We also present problems and solutions created by working within a Python-centric software base on traditional HPC systems, and discuss steps that will be required before the convergence of HPC, AI, and data science can be fully realized. Our results point to a wealth of exciting new possibilities for harnessing this convergence to tackle new scientific challenges.

Coletti, Mark↗
DOE OSTI2023

Idaho National Laboratory Energy Cybersecurity Programs Update

This brief presentation provides a status update on three Idaho National Laboratory energy cybersecurity programs of particular interest to NERC Reliability and Security Technical Committee annual in-person Security Groups summit. Public information on the following three programs is included: Cybersecurity for Operational Technology Environments (CyOTE™) program Cyber-Informed Engineering (CIE) Cyber Testing for Resilient Industrial Control Systems (CyTRICS) program, and associated high-level information on the Energy Software Bill of Materials POC, Executive Order 14017, and the Energy Cyber Sense Act

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Wind Energy Accomplishments and Year-End Performance Report: Fiscal Year 2023

As the United States' largest source of renewable energy, wind is already playing a vital role in the nation's shift to electricity generated from sustainable resources. Scientists, engineers, and analysts at the U.S. Department of Energy's (DOE's) National Renewable Energy Laboratory (NREL) are propelling our country closer to this clean energy future with new efficient, reliable, and cost-competitive wind technologies and deployment strategies. In Fiscal Year 2023 (FY 2023), NREL received national acclaim for recent wind technology breakthroughs, applauded sustained decades of progress in wind energy research, and heralded new initiatives to support the next generation of wind development. This report and NREL's achievements of the last year set the stage for innovative research to come. Two NREL software innovations used to assess wind technology options were recognized with R&D 100 Awards. The Renewable Energy Potential (reV) model helps planners, developers, and researchers calculate wind energy capacity, generation, and cost for specific facilities, fleets and scenarios. NREL's Simulation and Emulation for Advanced Systems validates transmission and distribution solutions to minimize implementation risks. Both of these tools can be applied to a range of renewable solutions to cut costs and improve the reliability of systems in even the most remote locations. The laboratory commemorated landmark anniversaries for wind energy research facilities and programs managed by NREL. The National Wind Technology Center (NWTC) celebrated 30 years as a world-class hub for renewable energy research. An important component of the NWTC on NREL's Flatirons Campus, the one-of-a-kind 7-megawatt (MW) Controllable Grid Interface wind energy research apparatus, turned 10 this year. The Collegiate Wind Competition completed its tenth annual competition in May 2023. Six years after the grand challenges in wind energy science were first compiled, NREL brought together more than 100 wind energy experts from 15 countries to revisit the field's most pressing research needs. Other key new research initiatives examined ways to simultaneously address the needs for skilled wind energy workers and equitable job opportunities. NREL released the first national-level report evaluating offshore wind industry workforce gaps and ways to build a large, diverse pool of job candidates. The lab also co-hosted the International Partnering Forum's Offshore Wind Workforce Summit as part of its ongoing efforts to address the special challenges of that industry sector. NREL research and development (R&D) for the DOE Wind Energy Technologies Office (WETO) continues to build momentum in its efforts to combat climate change, create clean energy jobs, and promote energy justice. This report provides more detail on these top achievements and other accomplishments made by NREL and its partners during FY 2023 (between Oct. 1, 2022, and Sept. 30, 2023).

accomplishments↗
DOE OSTI2017

Indonesian Highly Enriched Uranium-Bearing Waste Elimination - 17393

The Department of Energy's National Nuclear Security Administration (NNSA) and the Y-12 National Security Complex (Y-12) have been working with PT Industri Nuklir Indonesia (PT Inuki), an Indonesian medical isotope producer, since 2014 to disposition all highly enriched uranium (HEU) stored at the government-owned Research Center for Science and Technology (PUSPIPTEK) near Serpong, Indonesia. To eliminate the 1.5 kilograms of liquid HEU at the facility, Y-12 proposed a method to down-blend the leftover process solutions with a depleted uranium solution made on-site to render low-enriched uranium (LEU). This method is essentially irreversible since the uranium enrichment is degraded using a molecular level of intimate mixing in aqueous solution. This cooperation was tied to a presidential commitment made at the 2016 Nuclear Security Summit to down-blend all remaining HEU in Indonesia by September 2016. While all unirradiated HEU was down-blended to LEU in March 2016, the campaign to down-blend the 1.4 kilograms of irradiated HEU began in late July 2016 and was completed in August, making Indonesia and all of Southeast Asia free of HEU. As part of this cooperation, Y-12 and NNSA also proposed a subsequent disposition option for the LEU solution whereby the irradiated and unirradiated solutions will be chemically manipulated to prepare a dry LEU oxide suitable for interim storage and yield a uranium-free liquid stream that can be discarded as radioactive waste. In the fall of 2016, Y-12 and NNSA plan to begin developing the LEU solution disposition methodology with the Indonesian facility using small-scale batch-wise methods within existing hot cells, involving straightforward precipitation, ion exchange, decantation, and calcination methods. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Towards Lightweight Data Integration Using Multi-Workflow Provenance and Data Observability

Modern large-scale scientific discovery requires multidisciplinary collaboration across diverse computing facilities, including High Performance Computing (HPC) machines and the Edge-to-Cloud continuum. Integrated data analysis plays a crucial role in scientific discovery, especially in the current AI era, by enabling Responsible AI development, FAIR, Reproducibility, and User Steering. However, the heterogeneous nature of science poses challenges such as dealing with multiple supporting tools, cross-facility environments, and efficient HPC execution. Building on data observability, adapter system design, and provenance, we propose MIDA: an approach for lightweight runtime Multi-workflow Integrated Data Analysis. MIDA defines data observability strategies and adaptability methods for various parallel systems and machine learning tools. With observability, it intercepts the dataflows in the background without requiring instrumentation while integrating domain, provenance, and telemetry data at runtime into a unified database ready for user steering queries. We conduct experiments showing end-to-end multi-workflow analysis integrating data from Dask and MLFlow in a real distributed deep learning use case for materials science that runs on multiple environments with up to 276 GPUs in parallel. We show near-zero overhead running up to 100,000 tasks on 1,680 CPU cores on the Summit supercomputer.

Santos Souza, Renan↗
DOE OSTI2020

Lessons Learned from Demolition of Hanford's Plutonium Finishing Plant - 20507

U.S. Department of Energy (DOE) contractor CH2M Hill Plateau Remediation Company (CHPRC), a Jacobs owned company, is making significant progress demolishing the Plutonium Finishing Plant (PFP), which has long been known as one of the most hazardous buildings in the DOE Environmental Management Complex (DOE Complex). The facility is located on the Hanford Site and produced plutonium metal during the Cold War. Production was stopped in 1989, the facility was formally shut down in 1996, and material processing was completed in 2004. After approximately 25 years of demolition preparations, open-air demolition of the main portions of the Hazard Category II nuclear facility began in November 2016. To prepare the PFP for demolition, CHPRC employees performed some of the most hazardous work across the DOE Complex. In addition to extensive demolition preparations, including a wide-reaching communication strategy across the Hanford Site, CHPRC implemented robust controls and monitoring during demolition, and when necessary, adjusted demolition practices and sequence to maintain employee safety and project efficiency. Demolition of the main PFP building began in November 2016. The technical complexity, high hazards and radiological concerns have all led to a difficult demolition environment. Following a spread of contamination at the project site beyond the radiological boundaries in December 2017, work was halted and a recovery plan was generated to allow the demolition work to continue. After the implementation of additional controls and concurrence from regulatory agencies and DOE, the demolition was ready to resume work and reinitiated in August 2018. There are a number of lessons learned from the demolition activities and recovery plan process. This paper allows CHPRC and DoE's Richland Operations Office (RL) to share lessons learned and progress to date with other challenging and hazardous projects across the DOE Complex. Understanding the risk acceptance level is critical to execution of any open-air demolition project and is unique to each facility. Four key lessons learned from this project include: 1) plan execution issues, 2) stakeholder communication issues, 3) watching for summit fever and 4) minimizing exposed contaminated surfaces during demolition. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Getting to 100%: Six Strategies for the Challenging Last 10%

This presentation summarizes the challenge of decarbonizing the last 10%" of the power system and discusses six strategies for addressing that challenge. This presentation was given at the IRA, BIL, and the Future of Energy: A Summit to Support State Implementation" workshop in Washington, D.C., in April 2024.

carbon capture↗
DOE OSTI2024

Distributed Multi-GPU Community Detection on Exascale Computing Platforms

Community detection is a fundamental operation in graph mining, and by uncovering hidden structures and patterns within complex systems it helps solve fundamental problems pertaining to social networks, such as information diffusion, epidemics, and recommender systems. Scaling graph algorithms for massive networks becomes challenging on modern distributed-memory multi-GPU (Graphics Processing Unit) systems due to limitations such as irregular memory access patterns, load imbalances, higher communication-computation ratios, and cross-platform support. We present a novel algorithm HiPDPL-GPU (Distributed Parallel Louvain) to address these challenges. We conduct experiments involving different partitioning techniques to achieve an optimized performance of HiPDPL-GPU on the two largest supercomputers: Frontier and Summit. Remarkably, HiPDPL-GPU processes a graph with 4.2 billion edges in less than 3 minutes using 1024 GPUs. Qualitatively, the performance of HiPDPL-GPU is similar or better compared to other state-of-the-art CPU- and GPU-based implementations. While prior GPU implementations have predominantly employed CUDA, our first-of-its-kind implementation for community detection is cross-platform, accommodating both AMD and NVIDIA GPUs.

Sattar, Naw Safrin↗
DOE OSTI2024

Scaling and performance portability of the particle-in-cell scheme for plasma physics applications through mini-apps targeting exascale architectures

We perform a scaling and performance portability study of the particle-in-cell scheme for plasma physics applications through a set of mini-apps we name "Alpine", which can make use of exascale computing capabilities. The mini-apps are based on Independent Parallel Particle Layer, a framework that is designed around performance portable and dimension independent particles and fields. We benchmark the simulations with varying parameters such as grid resolutions (5123 to 20483) and number of simulation particles (109 to 1011) with the following mini-apps: weak and strong Landau damping, bump-on-tail and two-stream instabilities, and the dynamics of an electron bunch in a charge-neutral Penning trap. We show strong and weak scaling and analyze the performance of different components on several pre-exascale architectures such as Piz-Daint, Cori, Summit and Perlmutter. While the scaling and portability study helps identify the performance critical components of the particle-in-cell scheme in the current state-of-the-art computing architectures, the mini-apps by themselves can be used to develop new algorithms and optimize their high performance implementations targeting exascale architectures.

Muralikrishnan, Sriramkrishnan↗