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DOE OSTI2014

F3D Image Processing and Analysis for Many - and Multi-core Platforms

F3D is written in OpenCL, so it achieve[sic] platform-portable parallelism on modern mutli-core CPUs and many-core GPUs. The interface and mechanims to access F3D core are written in Java as a plugin for Fiji/ImageJ to deliver several key image-processing algorithms necessary to remove artifacts from micro-tomography data. The algorithms consist of data parallel aware filters that can efficiently utilizes[sic] resources and can work on out of core datasets and scale efficiently across multiple accelerators. Optimizing for data parallel filters, streaming out of core datasets, and efficient resource and memory and data managements over complex execution sequence of filters greatly expedites any scientific workflow with image processing requirements. F3D performs several different types of 3D image processing operations, such as non-linear filtering using bilateral filtering and/or median filtering and/or morphological operators (MM). F3D gray-level MM operators are one-pass constant time methods that can perform morphological transformations with a line-structuring element oriented in discrete directions. Additionally, MM operators can be applied to gray-scale images, and consist of two parts: (a) a reference shape or structuring element, which is translated over the image, and (b) a mechanism, or operation, that defines the comparisons to be performed between the image and the structuring element. This tool provides a critical component within many complex pipelines such as those for performing automated segmentation of image stacks. F3D is also called a "descendent" of Quant-CT, another software we developed in the past. These two modules are to be integrated in a next version. Further details were reported in: D.M. Ushizima, T. Perciano, H. Krishnan, B. Loring, H. Bale, D. Parkinson, and J. Sethian. Structure recognition from high-resolution images of ceramic composites. IEEE International Conference on Big Data, October 2014.

Source record↗

DOE OSTI2017

Novel Serpentine Robot Combinations for Inspection in Hard-to-Reach Areas of Damaged or Decommissioned Structures - 17335

U.S. government agencies, particularly the Dept. of Energy, need robotic tools to handle and inspect high-consequence materials - materials for which mishandling would result in great risk to both application operators and the general public. The remediation and documentation of nuclear waste has become a problem of national significance and it cannot be addressed without robotic tools. The use of robots bypasses the danger and decreases the preparation time to handle harmful materials. In the 1980's and 90's, the US Dept. of Energy (DOE) invested heavily in robotic technologies as part of a comprehensive program of research and development aimed at enabling safer and more efficient cleanup of nuclear waste sites. For the next generation of harder, more complex clean-up problems, there is a clear need for more R and D. There is a clear need for flexible, quickly deployable robotic technologies that can be configured to respond to unforeseen situations -- or can be purpose-built for planned activities -- while keeping human workers safe, yet effective. Modularity and inter-operability are core capabilities for inspection tasks in contaminated facilities with a large variety of inspection problems. With any tool, familiarity breeds ingenuity, and ingenuity leads to new innovations in efficiency, effectiveness, safety, and performance. For example, the simple screwdriver can be held fourteen different ways by an experienced machinist who knows exactly what he or she wants to accomplish at any given moment. As a tool, robots are no different. To maximize the efficiency, effectiveness, safety and performance, redeployment and reuse of familiar tools is essential. Modularity can assist with re-deployment and modularity manifests itself in both hardware and software. In some applications of inspection of high-consequence materials, a common concern has been the simplicity of the tool -- often correlated with cost (which is not always accurate) -- in case it becomes unrecoverable. In the case of robots, which have complex user interfaces and complex dynamics of interaction, the lack of familiarity can lead to clumsy operations and reduced accumulation of knowledge. As a result, while incremental successes may be achieved with sequential uses of different robots, there often is little knowledge gain that is generalizable across multiple uses. Total cost often goes down when a more sophisticated, but more familiar tool, can be used more frequently.An example is the inspection of the H-Canyon Exhaust Air Tunnel at the Savannah River Site nuclear waste facility in the United States [1]. This is a particularly nasty environment, due to the acidic gases, large amounts of erosion and debris, and wet, mucky puddles. Over the past twelve years, six inspections have been attempted with five different robotic vehicles. In fact, these inspections have been successful with their limited, but progressively increasing objectives, although most robots have been abandoned in the tunnel. While each vehicle has cost less than $75,000 (the sacrificial part), the total costs of development have been much higher. The robots used to investigate changed much from iteration to iteration and it is not clear a strong body of generalizable expertise was built up across trials. The authors of this study demonstrated the combination of two types of modular, serpentine, re-configurable robots for locomotion over complex terrains and the inspection of hard-to-reach areas. The MOTHERSHIP is a tread/limb/serpentine hybrid robot design for locomotion over complex terrains, while the CMU snake robot provides highly articulated inspection of highly confined spaces. The demonstration at the Portsmouth Gaseous Diffusion Plant involves a combination of the two whereby gross locomotion is achieved with the MOTHERSHIP and the CMU Snake deploys a sensor package to gather data from behind and under obstacles. Although statically configured in its present state, the combined robot is built of individual modules for which reconfiguration has been demonstrated in both labs at Purdue and CMU. In future work, much higher degrees of reconfiguration and re-purposing of both software and hardware are easily achievable with these unique platforms. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

Floating Island International PHASE I FINAL TECHNICAL REPORT (Advancing the Development of Floating Solar-Powered Nanobubble Aeration Systems for Use with Floating Treatment Wetlands in Natural and Man-Made Waterbodies)

More and more freshwater lakes are suffering from algae blooms, which deprive water of oxygen and lead to widespread loss of aquatic life and production of dangerous toxins. When oxygen is lacking, methane is generated in the sediments as algae is decomposed; it has been calculated that more than half of global methane emissions come from nutrient-impaired freshwater systems (Beaulieu, 2021). Nutrients, mainly from agricultural run-off, feed algae blooms and are exacerbated by warming related to climate change. Artificial aeration is frequently used to restore oxygen to a waterbody. But diffuser aeration is inefficient and expensive, needs on-site grid power, and is failing to keep pace with the demands of water as it gets warmer with climate change. In the last five years, nanobubble aeration has been introduced into freshwater applications and shows promise for rapidly increasing dissolved oxygen effectively throughout the water column. Nanobubbles deliver oxygen in bubbles that have no buoyancy, so they stay in water longer and release their oxygen more fully, compared to large bubbles that rise and burst at the surface. The prospect of this new technology answering the deficiencies of diffuser aeration drove us to initiate the current project. During our Phase I period, we have successfully tested a nanobubble aeration system that can super-saturate oxygen levels. It is operated on solar power, and both the nanobubbler and solar array are mounted on a proprietary floating island platform, that also performs biological nutrient recycling. A rudimentary system was assembled and tested on a 6.5-acre research lake at our headquarters in Montana, where it was subjected to a summer drought that reduced water levels significantly, periods of severe cold in winter, and spring conditions that included heavy rainfall and violent thunderstorms. Our twice-weekly sampling throughout the project showed that dissolved oxygen levels rose rapidly and were maintained throughout winter and spring, well into June. The nanobubbler was able to run on solar power for long periods. Methane levels were tested periodically and found to decrease as oxygen increased. The nanobubbles appeared to have no adverse impact on fish or other aquatic life exposed to them. The stability of the installation survived the weather conditions. The many problems we encountered taught us what we need to improve in the next iteration. Towards the end of our Phase I, we were awarded a supplementary state grant that enabled us to acquire a new nanobubble system for testing that runs on DC, is extremely efficient and has few moving parts. We plan to team this with low-profile solar panels, lithium batteries and a controller that provides real-time data. The ultimate goal of this project is to commercialize an affordable and scalable lake management system that will oxygenate water from the surface down to the sludge. It will enliven fisheries, prevent toxic algae blooms and – most importantly for the fate of our planet - inhibit the production and release of methane from the sediments. We anticipate that as carbon credits expand to include methane, the cost of remediating waterbodies that produce methane will be offset. We firmly believe that this is a practical technology that works and will make a big difference when fully implemented.

14 SOLAR ENERGY↗

NASA NTRSDate not supplied

Real World Applications of AI/ML in Optimizing Airspace Operations

As National Airspace System (NAS) is going through the Digital Transformation journey, data science and analytics methods can significantly contribute to improving the traditional physics-based decision-making tools. The adoption of AI/ML methods will not only help accelerate Federal Aviation Administration (FAA)’s vision of Info-centric NAS but also contribute to the overall objective of sustainable aviation. AI/ML can improve the ground and airspace operations by enhancing the accuracy of current decision-making tools used by the airlines and the FAA to manage traffic on the ground and in the air. Huge amount of data that gets collected during a flight. AI/ML methods can extract information from this data and provide valuable insights to make better operational decisions. NASA has partnered with the FAA and commercial airlines such as American and Southwest Airlines on this effort and has successfully demonstrated the benefits of using ML in real world environment by reducing delays and optimizing ground operations at the US airports. In 2022 itself, NASA demonstrated real-world benefits (over 24K lbs. of fuel savings, over 76.6K lbs. CO2 emission savings, and several hours of delay savings) by deploying ML based prediction models to optimize ground operations at Dallas/Fort Worth International and Dallas Love Field Airports in Texas. These tools are being deployed on the cloud for broader deployment, adaptability, and scalability. NASA is developed a reference implementation of the cloud-based platform to significantly lower the bar to development and distribution of these digital services for aviation. In this talk, I will share information about the Digital Information Platform project, the novel AI/ML based approaches used for optimizing ground operations and the opportunities to partner with NASA on these demonstrations.

air traffic management↗

DOE OSTI2017

Simulation Data Management - Requirements and Design Specification

Simulation Data Management (SDM), the ability to securely organize, archive, and share analysis models and the artifacts used to create them, is a fundamental requirement for modern engineering analysis based on computational simulation. We have worked separately to provide secure, network SDM services to engineers and scientists at our respective laboratories for over a decade. We propose to leverage our experience and lessons learned to help develop and deploy a next-generation SDM service as part of a multi-laboratory team. This service will be portable across multiple sites and platforms, and will be accessible via a range of command-line tools and well-documented APIs. In this document, we’ll review our high-level and low-level requirements for such a system, review one existing system, and briefly discuss our proposed implementation.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2026

Livewire: A Model Platform for Data Quality Assessment and AI Readiness Across DOE Missions

High-quality, well-governed data is essential for accelerating discovery and achieving operational excellence across DOE and national laboratory missions. The Livewire Data Platform is a DOE-supported platform that offers automated assessments of data quality, standardization, provenance, and Artificial Intelligence (AI) readiness. It allows researchers and data practitioners to systematically and easily evaluate datasets against established governance criteria and prepare them for advanced analytics. Livewire addresses critical challenges in DOE's data ecosystem with integrated capabilities for metadata validation, provenance tracking, and schema alignment. This platform's automated workflows assist users in identifying data quality gaps, enhancing interoperability between datasets collected from various stakeholders, and ensuring compliance with DOE data standards, all while reducing manual curation efforts. Additionally, we will discuss its AI readiness framework, which is being developed to prepare datasets for training models, developing advanced analytic tools, and machine learning applications. Using some of the more than one hundred tabular datasets on Livewire, processed with this open-source methodology, we will demonstrate how Livewire can serve as a model for scalable, standards-driven data management. This approach provides a pathway to leverage existing and future datasets within the DOE, boosting innovation and efficiency across national laboratories.

33 - ADVANCED PROPULSION SYSTEMS↗

NASA NTRS2018

Model-Based Data Integration and Process Standardization Techniques for Fault Management: A Feasibility Study

This paper describes the theory and considerations in the application of model-based techniques to assimilate information from disjoint knowledge sources for performing NASA's Fault Management (FM)-related activities using the TEAMS® toolset. FM consists of the operational mitigation of existing and impending spacecraft failures. NASA's FM directives have both design-phase and operational-phase goals. This paper highlights recent studies by QSI and DST of the capabilities required in the TEAMS® toolset for conducting FM activities with the aim of reducing operating costs, increasing autonomy, and conforming to time schedules. These studies use and extend the analytic capabilities of QSI's TEAMS® toolset to conduct a range of FM activities within a centralized platform.

Haste, Deepak↗

DOE OSTI2023

From Count Rates to Quantifying Isotopic Activities – Field Analysis of Radiation Monitoring Data

The Nevada National Security Site (NNSS) provides a comprehensive bicoastal radiological and nuclear emergency response to United States Department of Energy/National Nuclear Security Administration. A major part of the support is to provide systematic radiological search for lost or stolen sources, Radiological search is a core competency of the NNSS with its origin dating back to nuclear weapons test era. Search operations from multiple platforms is the common thread among the various NNSS assets, which include Aerial Measuring System (AMS), Maritime Support Team (MST), National Capitol Response (NCR), National Search Team (NST) and Radiological Assistance Program (RAP). Information collected and analyzed during search operations add to the actionable intelligence for the law enforcement agencies and provide valuable guidance for the tactical resolution of a nuclear or radiological crisis. Search is an intelligence and situational awareness driven operation and most often called upon during a radiological emergency, however it can be brought into play to thwart a potential threat by providing monitoring and surveillance support. The Office of Nuclear Incident Response (NA-84) serves as the technical leader in responding to and resolving nuclear and radiological threats worldwide and integrates its efforts with other NNSA stakeholders (e.g., NNSA office of Defense Nuclear Non-proliferation NA-22). The response includes expertise in the areas of radiological search, render safe, and consequence management. This article will discuss the methodologies, tools, procedures, and techniques to extract maximum radiological characterization information (isotopic composition, activities for individual isotopes, threat assessment etc.) from field monitoring or Search operation data.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2021

National Virtual Biotechnology Laboratory: Report on Rapid R&D Solutions to the COVID-19 Crisis

With funding from the CARES Act, the U.S Department of Energy (DOE) established the National Virtual Biotechnology Laboratory (NVBL) in March 2020 to address key challenges associated with the COVID-19 crisis. NVBL brought together the broad scientific and technical expertise and resources of DOE’s 17 national laboratories to help tackle medical supply short ages, discover potential drugs to fight the virus, develop and validate COVID-19 testing methods, model disease spread and impact across the nation, and understand virus transport in buildings and the environment. National laboratory resources leveraged for this effort include a suite of world-leading user facilities broadly available to the research community, such as light and neutron sources, nanoscale science research centers, sequencing and biocharacterization facilities, and high-performance computing facilities. Within months, NVBL teams produced innovations in materials and advanced manufacturing that mitigated shortages in test kits and personal protective equipment (PPE), creating nearly 1,000 new jobs. They used DOE’s high-performance computers and light and neutron sources to identify promising candidates for antibodies and antivirals that universities and drug companies are now evaluating. NVBL researchers also developed new diagnostic targets and sample collection approaches, and supported U.S. Food and Drug Administration (FDA), Centers for Disease Control and Prevention (CDC), and U.S. Department of Defense (DoD) efforts to establish national guidelines used in administering millions of tests. Researchers used artificial intelligence and high-performance computing to produce near-real-time data analysis to forecast disease transmission, stress on public health infrastructure, and economic impact, which supported decision-makers at the local, state, and national levels. NVBL teams also studied how to control indoor virus movement to minimize uptake and protect human health. NVBL’s accomplishments demonstrate not only the powerful resource represented by DOE’s national laboratories working together to meet national needs, but also the effectiveness of the integrated NVBL framework for rapidly responding to emergencies with research and development (R&D) solutions. As the fight against COVID continues, sustained efforts are needed to confront this pandemic as well as future threats. Examples include: 1) Establishing “supply chains on demand” to meet emergency production needs by leveraging the materials and manufacturing expertise of DOE national laboratories and developing advances in electronics, sensing, robotics, and automation capabilities; 2) Improving the speed and robustness of drug discovery by integrating experimental platforms with DOE’s computational and experimental user facilities, which provide unique resources to support the discovery of high-potential therapeutic agents; 3) Protecting public, environmental, and animal health by developing new testing protocols and instrumentation adaptable to diverse sample types (both physiological and environmental) to quickly detect a wide range of pathogens and monitor other biorisks; 4) Supporting near-real-time data needs of decision-makers at the local, regional, state, and national levels by advancing data curation, analysis, and modeling using artificial intelligence and new data science tools for managing and evaluating large diverse datasets; 5) Harnessing DOE’s expertise in environmental modeling to design rooms and air handling for offices, classrooms, restaurants, and other structures to minimize biorisk transmissions. Going forward, NVBL is poised to apply the unique capabilities and expertise of the national laboratory complex to future national and international emergencies, both natural and engineered. Through this framework, the Office of Science will continue to be an integral component of agency wide efforts to prepare for and respond to biorisks and other crises.

42 ENGINEERING↗

NASA NTRS2009

Benchmarking Diagnostic Algorithms on an Electrical Power System Testbed

Diagnostic algorithms (DAs) are key to enabling automated health management. These algorithms are designed to detect and isolate anomalies of either a component or the whole system based on observations received from sensors. In recent years a wide range of algorithms, both model-based and data-driven, have been developed to increase autonomy and improve system reliability and affordability. However, the lack of support to perform systematic benchmarking of these algorithms continues to create barriers for effective development and deployment of diagnostic technologies. In this paper, we present our efforts to benchmark a set of DAs on a common platform using a framework that was developed to evaluate and compare various performance metrics for diagnostic technologies. The diagnosed system is an electrical power system, namely the Advanced Diagnostics and Prognostics Testbed (ADAPT) developed and located at the NASA Ames Research Center. The paper presents the fundamentals of the benchmarking framework, the ADAPT system, description of faults and data sets, the metrics used for evaluation, and an in-depth analysis of benchmarking results obtained from testing ten diagnostic algorithms on the ADAPT electrical power system testbed.

Kurtoglu, Tolga↗

NASA NTRS2023

NASA’s Digital Information Platform to Accelerate the Transformation of the National Airspace System

In order to accelerate the digital transformation of airspace operations, a foundational framework and infrastructure for providing sustainable, data-driven, and cohesive decision-making digital services for both traditional and emergent air vehicles is being developed. The reference implementation of Digital Information Platform builds an ecosystem for the aviation community by providing access to a secure and trusted source of aviation data and services. Several key features and services have been implemented to enable secure data sharing, communication, and service registration on the Platform. The technical approach used to implement these features is presented here. NASA-developed integrated aviation data and machine learning based prediction services to optimize airspace operations are available on the Platform. These services are being evaluated in an operational environment by flight operators and the real-world benefits are being captured. The Platform fosters collaboration among industry and researchers to develop complex aviation services and the aim is to make it publicly accessible for consumption by the aviation community.

Digital Transformation↗

NASA NTRSDate not supplied

NASA’s Digital Information Platform to Accelerate the Transformation of the National Airspace System

Digital Transformation↗

NASA NTRS2005

Testing the Archivas Cluster (Arc) for Ozone Monitoring Instrument (OMI) Scientific Data Storage

The Ozone Monitoring Instrument (OMI) launched on NASA's Aura Spacecraft, the third of the major platforms of the EOS program on July 15,2004. In addition to the long term archive and distribution of the data from OM1 through the Goddard Earth Science Distributed Active Archive Center (GESDAAC), we are evaluating other archive mechanisms that can archive the data in a more immediately available method where it can be used for futher data production and analysis. In 2004, Archivas, Inc. was selected by NASA s Small Business Innovative Research (SBIR) program for the development of their Archivas Cluster (ArC) product. Arc is an online disk based system utilizing self-management and automation on a Linux cluster. Its goal is to produce a low cost solution coupled with the ease of management. The OM1 project is an application partner of the SBIR program, and has deployed a small cluster (5TB) based on the beta Archwas software. We performed extensive testing of the unit using production OM1 data since launch. In 2005, Archivas, Inc. was funded in SBIR Phase II for further development, which will include testing scalability with the deployment of a larger (35TB) cluster at Goddard. We plan to include Arc in the OM1 Team Leader Computing Facility (TLCF) hosting OM1 data for direct access and analysis by the OMI Science Team. This presentation will include a brief technical description of the Archivas Cluster, a summary of the SBIR Phase I beta testing results, and an overview of the OMI ground data processing architecture including its interaction with the Phase II Archivas Cluster and hosting of OMI data for the scientists.

Tilmes, Curt↗

DOE OSTI2019

Is it Worth Relaxing Fault Tolerance to Speed Up Decommission in Distributed Storage Systems?

Efficient resource utilization is a major concern for large-scale computer platforms. One method used to lower energy consumption and operational cost is to reduce the amount of idle resources. This can be achieved by using malleability, namely, the possibility for resource managers to dynamically increase or decrease the amount of resources of jobs while they are running. Decommissioning (i.e., removing from the cluster) the idle nodes as soon as possible allows the resource manager to quickly reallocate those nodes to other jobs. Challenges appear when such nodes host part of a distributed storage system. Such storage systems may need to transfer large amounts of data before releasing the nodes, in order to ensure data availability and a certain level of fault tolerance. In this paper, we model and evaluate the performance of the decommission operation when relaxing the level of fault tolerance (i.e., the number of replicas) during this operation. Intuitively, this is expected to reduce the amount of data transfers needed before nodes are released, and thus allow nodes to be returned to the resource manager faster. We quantify theoretically how much time and resources are saved by such a fast decommission strategy compared with a standard decommission that does not temporarily reduce the fault-tolerance level. We establish lower bounds for the duration of the different phases of a fast decommission. We use the lower bounds to estimate when fast decommission would be useful to reduce the usage of core-hours and when not. We implement a prototype for fast decommission and experimentally validate the lower bounds on the duration of the operation and confirm in practice our theoretical findings.

Decommission↗

DOE OSTI2018

CANDLE/Supervisor: a workflow framework for machine learning applied to cancer research

BackgroundCurrent multi-petaflop supercomputers are powerful systems, but present challenges when faced with problems requiring large machine learning workflows. Complex algorithms running at system scale, often with different patterns that require disparate software packages and complex data flows cause difficulties in assembling and managing large experiments on these machines.ResultsThis paper presents a workflow system that makes progress on scaling machine learning ensembles, specifically in this first release, ensembles of deep neural networks that address problems in cancer research across the atomistic, molecular and population scales. The initial release of the application framework that we call CANDLE/Supervisor addresses the problem of hyper-parameter exploration of deep neural networks.ConclusionsInitial results demonstrating CANDLE on DOE systems at ORNL, ANL and NERSC (Titan, Theta and Cori, respectively) demonstrate both scaling and multi-platform execution.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

Marine Corps Base Camp Lejeune New River Estuary (NRE) - Autonomous Vertical Profiler (AVP) Stone's Bay Data 2008-2013

Critical military training and testing on lands along the nation’s coastal and estuarine shorelines are increasingly placed at risk because of encroachment pressures in surrounding areas, impairments due to other anthropogenic disturbances, and changes in climate and sea level. The U.S. Department of Defense (DoD) intends to enhance and sustain its training and testing assets and also optimize its stewardship of natural resources through the development and application of an ecosystem-based management approach on DoD installations. To accomplish this goal, particularly for installations in estuarine/coastal environments, the Strategic Environmental Research and Development Program (SERDP) launched the Defense Coastal/Estuarine Research Program (DCERP) as a 10-year effort at Marine Corps Base Camp Lejeune (MCBCL) in North Carolina. The results of the second 5 years of the program (DCERP2) are presented in the DCERP2 Final Report.There were four overarching objectives of DCERP2. The first objective was to understand the effects of climate change impacts, including warming temperatures, variability in the hydrological cycle, storm events, and sea level rise on the coastal ecosystems at MCBCL from observations and measurements made over the 10-year program. The second objective was to understand the carbon cycle of the coastal and terrestrial ecosystems at MCBCL through a highly integrated sampling program. The third objective was to develop models, tools, and indicators to evaluate current and projected future ecosystem state changes and translate scientific findings into actionable information for installation managers. The last objective was to recommend adaptive management strategies to sustain ecosystem natural resources within the context of an active military installation.This data package includes a zipfile containing the results of continuous (30 minute interval) water quality samples and wind speed and direction anemometer (MET) data from Stone's Bay at Station 3 (AVP1). Description from Monitoring Report: Autonomous vertical profilers (AVPs) were deployed from June 2008 through October 2016 in central Stones Bay at Station 3, and in Morgan Bay upstream of Station 5 and were used to continuously monitor key water quality parameters. The AVPs consist of a moored floating platform housing a computer controlled winch mechanism that lowers and raises a YSI Model 6600 multi-parameter water quality datasonde at programmable time intervals (Reynolds-Fleming et al., 2002). Winch speed and sampling frequency of the AVP system and datasonde were configured to produce full water column profiles of temperature, salinity, DO concentration, chl concentration (by fluorescence), and turbidity every 30 minutes and achieve vertical depth resolution of approximately 10 cm. An anemometer (RM Young) affixed to the mast of the AVP measured six minute averages of wind speed and direction every 30 minutes. Data were stored locally on the AVP computers, and telemetered back to the laboratory each night. Datasondes were exchanged with freshly cleaned and calibrated datasondes as necessary but at least on a monthly basis.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1973

Monitoring of streamflow in the Verde River by ERTS-1 Data Collection System (DCS)

The Verde River watershed in central Arizona furnishes municipal, industrial, and agricultural water to the Salt River Valley --an area that contains more than half of Arizona's population and about one-fourth of the State's irrigated land. Water-management decisions related to the operation of large multiple-use reservoirs require accurate and continuous monitoring of moisture conditions over large remote areas. The U.S. Geological Survey in cooperation with the Salt River Valley Water Users' Association installed a specially designed gaging station on the Verde River near the town of Camp Verde to evaluate near-real time streamflow data furnished by the ERTS-1 Data Collection System (DCS). On Nov. 3, 1972, the installation was equipped with a Stevens digital water-level recorder, modified for telemetry, and an ERTS-1 data collection platform operating in the digital-parallel mode.

Schumann, H. H.↗

DOE OSTI2022

Co-scheduling Ensembles of In Situ Workflows

Molecular dynamics (MD) simulations are widely used to study large-scale molecular systems. HPC systems are ideal platforms to run these studies, however, reaching the necessary simulation timescale to detect rare processes is challenging, even with modern supercomputers. To overcome the timescale limitation, the simulation of a long MD trajectory is replaced by multiple short-range simulations that are executed simultaneously in an ensemble of simulations. Analyses are usually co-scheduled with these simulations to efficiently process large volumes of data generated by the simulations at runtime, thanks to in situ techniques. Executing a workflow ensemble of simulations and their in situ analyses requires efficient co- scheduling strategies and sophisticated management of computational resources so that they are not slowing down each other. In this paper, we propose an efficient method to co-schedule simulations and in situ analyses such that the makespan of the workflow ensemble is minimized. We present a novel approach to allocate resources for a workflow ensemble under resource constraints by using a theoretical framework modeling the workflow ensemble’s execution. We evaluate the proposed approach using an accurate simulator based on the WRENCH simulation framework on various workflow ensemble configurations. Results demonstrate the significance of co-scheduling simulations and in situ analyses that couple data together to benefit from data locality, in which inefficient scheduling decisions can lead to slowdown in makespan up to a factor of 30.

Do, Tu Mai Anh↗