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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Ad Hoc File Systems for High-Performance Computing

Storage backends of parallel compute clusters are still based mostly on magnetic disks, while newer and faster storage technologies such as flash-based SSDs or non-volatile random access memory (NVRAM) are deployed within compute nodes. Including these new storage technologies into scientific workflows is unfortunately today a mostly manual task, and most scientists therefore do not take advantage of the faster storage media. One approach to systematically include nodelocal SSDs or NVRAMs into scientific workflows is to deploy ad hoc file systems over a set of compute nodes, which serve as temporary storage systems for single applications or longer-running campaigns. This paper presents results from the Dagstuhl Seminar 17202 “Challenges and Opportunities of User-Level File Systems for HPC” and discusses application scenarios as well as design strategies for ad hoc file systems using node-local storage media. The discussion includes open research questions, such as how to couple ad hoc file systems with the batch scheduling environment and how to schedule stage-in and stage-out processes of data between the storage backend and the ad hoc file systems. Also presented are strategies to build ad hoc file systems by using reusable components for networking and how to improve storage device compatibility. Various interfaces and semantics are presented, for example those used by the three ad hoc file systems BeeOND, GekkoFS, and BurstFS. Their presentation covers a range from file systems running in production to cutting-edge research focusing on reaching the performance limits of the underlying devices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Optimization of distributed compute resources utilization in the CMS Global Pool

The CMS Submission Infrastructure is the primary system for managing computing resources for CMS workflows, including data processing, simulation, and analysis. It integrates geographically distributed resources from Grid, HPC, and cloud providers into federated pools managed by HTCondor and Glidein- WMS, for a total of around 500k CPU cores. This system dynamically manages workloads based on priorities defined by the collaboration. Additionally, CMS scheduling strategies must be flexible to handle multiple concurrent workloads while considering changing processing demands and resource availability from various providers.Efficient utilization of vast amounts of distributed compute resources is a key element for the success of the scientific programs of the LHC experiments. Optimizing the system is essential to maximize resource efficiency and fully utilize the distributed computing power. The CMS Submission Infrastructure team thus systematically investigates sources of inefficiency in workload scheduling to reduce their impact. In addition, a strategy of pilot overloading has been introduced to compensate for other inefficiency sources, thereby optimizing resource utilization and enhancing computational throughput.

Mascheroni, Marco [UC, San Diego (main)]↗
DOE OSTI2024

Autoencoder-Based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter

The CMS detector is a general-purpose apparatus that detects high-energy collisions produced at the LHC. Online data quality monitoring of the CMS electromagnetic calorimeter is a vital operational tool that allows detector experts to quickly identify, localize, and diagnose a broad range of detector issues that could affect the quality of physics data. A real-time autoencoder-based anomaly detection system using semi-supervised machine learning is presented enabling the detection of anomalies in the CMS electromagnetic calorimeter data. A novel method is introduced which maximizes the anomaly detection performance by exploiting the time-dependent evolution of anomalies as well as spatial variations in the detector response. The autoencoder-based system is able to efficiently detect anomalies, while maintaining a very low false discovery rate. The performance of the system is validated with anomalies found in 2018 and 2022 LHC collision data. In addition, the first results from deploying the autoencoder-based system in the CMS online data quality monitoring workflow during the beginning of Run 3 of the LHC are presented, showing its ability to detect issues missed by the existing system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Transmission Operator Workflows for Real-Time Reliability Studies: A Review of Control Room Practices and Naturalistic Decision Making

This report provides an overview of real-time reliability study tools and their use by power system operators in the control room environment. After introducing some of the nuances of the control room environment and the differences in perspectives between power system engineers and operators, the roles and responsibilities of key entities involved in RTCA workflows are introduced. These are specifically the transmission system operator (TOP) and reliability coordinator (RC), which are required to run tools such as real-time contingency analysis (RTCA) as part of a real-time reliability assessment every 30 minutes, as dictated by a series of standards issued by the North American Electric Reliability Corporation (NERC). The process by which power systems operators operate the grid is discussed in terms of naturalistic decision making (NDM) and the recognition-primed decision-making (RPD) model. This cognitive model describe how experts working in high-risk, high-stress environments make safety-critical decisions under uncertainty and time pressure. For power system operators, the mental simulations involved in the traditional RPD model are supplemented by physics-based simulations using numerical tools, such as RTCA, to improve situational awareness and effectiveness of control actions. Next, a generic workflow is introduced to describe operator decision making for running RTCA tools and responding to system violations on a pre-contingent basis. The types of analysis performed and control actions chosen by power system operators are described in detail. The overall high-level workflow is then expanded in subsequent sections, with special attention given to high-voltage violations, low-voltage violations, and thermal overloads. Each type of violation is described in detail, with explanations of common causes, impacts on equipment and customers, and mitigation strategies. An additional workflow diagram is provided for each type of violation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

First-generation college student forges ahead, now key to Lab's mission

Fatima Woody was just 17 and a student at Pojoaque Valley High School when she first started her career as a Los Alamos Neutron Science Center receptionist. Now she's in a crucial role that keeps plutonium pit production and other mission processes operating with as little interruption as possible. Over nearly four decades, Fatima has gradually advanced from her initial positions as a receptionist and administrative secretary to become a computer technician, then a computer system professional who specializes in project management. Today, she coordinates the workflow for nearly two dozen deployed information technology technicians who keep the computer systems and networks operating at the high-tech complex that houses the Lab's Plutonium Facility.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Real‐time XFEL data analysis at SLAC and NERSC: A trial run of nascent exascale experimental data analysis

X‐ray scattering experiments using free electron lasers (XFELs) are a powerful tool to determine the molecular structure and function of unknown samples (such as COVID‐19 viral proteins). XFEL experiments are a challenge to computing in two ways: (i) due to the high cost of running XFELs, a fast turnaround time from data acquisition to data analysis is essential to make informed decisions on experimental protocols; (ii) data‐collection rates are growing exponentially, requiring new scalable algorithms. Here we report our experiences analyzing data from two experiments at the Linac Coherent Light Source (LCLS) during September 2020. Raw data were analyzed on NERSC's Cori XC40 system, using the Superfacility paradigm: our workflow automatically moves raw data between LCLS and NERSC, where it is analyzed using the software package CCTBX. We achieved real time data analysis with a turnaround time from data acquisition to full molecular reconstruction in as little as 10 min—sufficient time for the experiment's operators to make informed decisions. By hosting the data analysis on Cori, and by automating LCLS‐NERSC interoperability, we achieved a data analysis rate which matches the data acquisition rate. Completing data analysis within 10 min is a first for XFEL experiments and an important milestone if we are to keep up with data‐collection trends.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
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 OSTI2021

Enabling modern data discovery for atmospheric measurements

The Atmospheric Radiation Measurement (ARM) user facility is a US Department of Energy Office of Science user facility that is managed and operated through a collaborative effort led by nine US Department of Energy national laboratories. The ARM Data Center, located at Oak Ridge National Laboratory, is responsible for the timely collection, processing, and delivery of data products to the scientific community. The ARM Data Center holds more than 11,000 data products, including metadata collected from field campaigns, instruments, value-added products, and principal investigator–contributed data. These data sets are checked for successful transfer (for most data, this transfer is carried out automatically via the network; however, some of the largest data sets and some of the most remote sites require manual shipping of hard disks) and both the data and metadata are processed to a standard format, which is an ARM-standardized structure, via the Network Common Data Form. The Network Common Data Form is a self-describing binary format with many compatible software tools. Once processed, the data are cataloged, stored in the ARM Data Archive, and made discoverable through association with an array of metadata-characterizing information, such as location and measurement classification. These metadata enable powerful search capabilities through the ARM Data Center Data Discovery interface. This paper discusses the workflow of how the new discovery system has been redesigned from user requirements and how the data are distributed to the scientific community.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Dimensional Control over Metal Halide Perovskite Crystallization Guided by Active Learning

Metal halide perovskite (MHP) derivatives, a promising class of optoelectronic materials, have been synthesized with a range of dimensionalities that govern their optoelectronic properties and determine their applications. We demonstrate a data-driven approach combining active learning and high-throughput experimentation to discover, control, and understand the formation of phases with different dimensionalities in the morpholinium (morph) lead iodide system. Using a robot-assisted workflow, we synthesized and characterized two novel MHP derivatives that have distinct optical properties: a one-dimensional (1D) morphPbI 3 phase ([C 4 H 10 NO][PbI 3 ]) and a two-dimensional (2D) (morph) 2 PbI 4 phase ([C 4 H 10 NO] 2 [PbI 4 ]). To efficiently acquire the data needed to construct a machine learning (ML) model of the reaction conditions where the 1D and 2D phases are formed, data acquisition was guided by a diverse-mini-batch-sampling active learning algorithm, using prediction confidence as a stopping criterion. Querying the ML model uncovered the reaction parameters that have the most significant effects on dimensionality control. Based on these insights, we discuss possible reaction schemes that may selectively promote the formation of morph-Pb-I phases with different dimensionalities. The data-driven approach presented here, including the use of additives to manipulate dimensionality, will be valuable for controlling the crystallization of a range of materials over large reaction-composition spaces.

36 MATERIALS SCIENCE↗
DOE OSTI2022

PlasmidMaker is a versatile, automated, and high throughput end-to-end platform for plasmid construction

Abstract Plasmids are used extensively in basic and applied biology. However, design and construction of plasmids, specifically the ones carrying complex genetic information, remains one of the most time-consuming, labor-intensive, and rate-limiting steps in performing sophisticated biological experiments. Here, we report the development of a versatile, robust, automated end-to-end platform named PlasmidMaker that allows error-free construction of plasmids with virtually any sequences in a high throughput manner. This platform consists of a most versatile DNA assembly method using Pyrococcus furiosus Argonaute ( Pf Ago)-based artificial restriction enzymes, a user-friendly frontend for plasmid design, and a backend that streamlines the workflow and integration with a robotic system. As a proof of concept, we used this platform to generate 101 plasmids from six different species ranging from 5 to 18 kb in size from up to 11 DNA fragments. PlasmidMaker should greatly expand the potential of synthetic biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Transformational Regional-Scale Earthquake Simulations with the DOE EarthQuake SIMulation Exascale Framework

Earthquakes present worldwide risk to economic and human safety. The 2023 earthquakes in Turkiye provided a reminder of the potential for catastrophic consequences with 50,700 deaths and 15.7 million people affected. The ability to predict ground motions and infrastructure damage for earthquakes continues to be a challenging problem for scientists and engineers. Until now, estimates of ground motions have been performed empirically by looking at sparse data from past earthquakes. This approach can provide statistical information on intensity amplitudes but cannot inform site-specific ground motions essential to developing the most effective resilience. Interest has grown in large-scale computational models to simulate earthquakes at regional scale. The U.S. Department of Energy EarthQuake SIMulation (EQSIM) framework was developed for regional-scale earthquake simulations at unprecedented fidelity, taking advantage of emerging GPU-accelerated systems. This article describes the EQSIM workflow and demonstrates regional-scale simulations with the new computational capability available to scientists in their quest to mitigate future disasters.

58 GEOSCIENCES↗
DOE OSTI2021

PlasmidMaker: a Versatile, Automated, and High Throughput End-to-End Platform for Plasmid Construction

Plasmids are used extensively in basic and applied biology. However, design and construction of plasmids, specifically the ones carrying complex genetic information, remains one of the most time-consuming, labor-intensive, and rate-limiting steps in performing sophisticated biological experiments. Here, we report the development of a versatile, robust, automated end-to-end platform named PlasmidMaker that allows error-free construction of plasmids with virtually any sequences in a high-throughput manner. This platform consists of a most versatile DNA assembly method using Pyrococcus furiosus Argonaute ( Pf Ago)-based artificial restriction enzymes, a user-friendly frontend for plasmid design, and a backend that streamlines the workflow and integration with a robotic system. As a proof of concept, we used this platform to generate ~100 plasmids from six different species ranging from 5 to 18 kb in size from up to 11 DNA fragments within 3 days. PlasmidMaker should greatly expand the potential of synthetic biology.

Enghiad, Behnam↗
DOE OSTI2021

Real-Time XFEL Data Analysis at SLAC and NERSC: a Trial Run of Nascent Exascale Experimental Data Analysis

X-ray scattering experiments using Free Electron Lasers (XFELs) are a powerful tool to determine the molecular structure and function of unknown samples (such as COVID-19 viral proteins). XFEL experiments are a challenge to computing in two ways: i) due to the high cost of running XFELs, a fast turnaround time from data acquisition to data analysis is essential to make informed decisions on experimental protocols; ii) data collection rates are growing exponentially, requiring new scalable algorithms. Here we report our experiences analyzing data from two experiments at the Linac Coherent Light Source (LCLS) during September 2020. Raw data were analyzed on NERSC's Cori XC40 system, using the Superfacility paradigm: our workflow automatically moves raw data between LCLS and NERSC, where it is analyzed using the software package CCTBX. We achieved real time data analysis with a turnaround time from data acquisition to full molecular reconstruction in as little as 10 min -- sufficient time for the experiment's operators to make informed decisions. By hosting the data analysis on Cori, and by automating LCLS-NERSC interoperability, we achieved a data analysis rate which matches the data acquisition rate. Furthermore, completing data analysis with 10 mins is a first for XFEL experiments and an important milestone if we are to keep up with data collection trends.

Blaschke, Johannes P.↗
DOE OSTI2023

Development of Prototypical District-Scale Models: Preprint

The U.S. has set the climate goal to achieve net-zero greenhouse gas emissions by 2050. District-scale solutions, which include scale-specific opportunities for energy and emissions savings, can be investigated and implemented to help accelerate decarbonization and progress toward this goal. However, there is currently a lack of district-scale models of buildings and community energy systems that can be used to evaluate potential district-scale technologies and strategies across a range of representative community types. This initial work aims to define and develop prototype district models that can be adapted to support the planning, design, and operation of buildings and energy systems in districts considering the complexity and interactions of diverse building loads, weather impacts, distributed energy resources (e.g., PV, EV, electric and thermal energy storage), electric and thermal grid systems, and pricing signals. An overall workflow for developing these prototype district models is established. Stakeholders and potential users of the prototype district models provided technical feedback. The specifications of the selected high priority districts were defined and documented in a scorecard format. An example prototype district model was implemented with the URBANoptTM platform workflows. A case study was performed to demonstrate the model application.

district↗
DOE OSTI2024

AI/ML Expo Boosting Job Performance with AI: Innovative Approaches and Success Stories

Our technology leverages artificial intelligence (AI) to enhance the user experience in High Performance Computing (HPC) environments. By analyzing user behavior and providing personalized recommendations, our AI system helps HPC users optimize their workflows and improve productivity. Additionally, we offer an advanced image similarity search feature, which utilizes AI algorithms to identify and retrieve visually similar images, saving users valuable time and effort in their research and analysis.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Using Visual Systems Mapping to Improve Transparency and Comparability of Life Cycle Assessment Baseline Scenarios

Visual systems mapping is a systems engineering approach used to represent complex processes and interactions. This study evaluates its application for documenting assumptions in life cycle assessment (LCA) baseline scenarios. In LCA, the baseline or reference case represents the business as usual system against which changes in impacts (e.g., emissions) are assessed. These baseline assumptions are particularly influential in biomass LCAs, yet they often vary across studies due to regional context, system boundaries, and simplifying assumptions that are not consistently or transparently documented. As a result, key feedbacks, omitted processes, and boundary choices may remain unclear, limiting comparability across studies and weakening their usefulness for decision-making. This study examines whether visual systems mapping can improve the transparency and comparability of biomass LCA baseline scenarios. A case study of five published biomass-related LCAs were reviewed, and their baseline scenarios were translated into visual system maps to identify included processes, omitted components, and underlying assumptions. The analysis demonstrates that visual systems mapping can make baseline assumptions more explicit, highlight excluded dynamics, and improve documentation of system boundaries. Based on these findings, the study recommends the use of visual systems mapping alongside open data repositories and reproducible workflows to support greater transparency, reproducibility, and comparability in LCAs. These improvements can strengthen the role of LCAs in informing decisions related to sustainable biomass systems.

Davis, Maggie [ORNL] (ORCID:0000000181319328)↗
DOE OSTI2020

Automated and Distributed Monte Carlo Generation for GlueX

MCwrapper is a set of systems that manages the entire Monte Carlo production workflow for GlueX and provides standards for how that Monte Carlo is produced. MCwrapper was designed to be able to utilize a variety of batch systems in a way that is relatively transparent to the user, thus enabling users to quickly and easily produce valid simulated data at home institutions worldwide. Additionally, MCwrapper supports an autonomous system that takes user’s project submissions via a custom web application. The system then atomizes the project into individual jobs, matches these jobs to resources, and monitors the jobs status. The entire system is managed by a database which tracks almost all facets of the systems from user submissions to the individual jobs themselves. Users can interact with their submitted projects online via a dashboard or, in the case of testing failure, can modify their project requests from a link contained in an automated email. Beginning in 2018 the GlueX Collaboration began to utilize the Open Science Grid (OSG) to handle a bulk of simulation tasks; these tasks are currently being performed on the OSG automatically via MCwrapper. This talk will outline the entire system of MCwrapper, its use cases, and the unique challenges facing the system.

Britton, Thomas↗
DOE OSTI2020

Effectively Using Remote I/O For Work Composition in Distributed Workflows

Distributed scientific workflows are becoming more important with the interest in incorporating AI into their loops. A critical programming and performance question is how to compose workflow tasks when data is produced on one system but must be consumed on another. Since the dominant technique is composition with remote I/O, this paper explores its performance expectations. We describe BigFlowSim, a workflow I/O simulator that captures key implementation choices for remote I/O, including intensity, reuse, locality, access pattern, and data movement.With BigFlowSim, we generate a synthetic benchmark. We quantify the effects of each parameter with a performance sensitivity study. We explain trends in terms of data movement reduction and show that, under certain conditions, it is possible to establish a total order among most parameters. We apply these insights to a high energy physics workflow, Belle II Monte Carlo and simulate several I/O optimizations. Speedups range from 5% to 2×, without changing compute time.

Friese, Ryan D.↗