Search NASA⌕ Search

SEARCH · Search NASA

Results for “collaborative teams”

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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

A Path to Successful Energy Retrofits: Early Collaboration through Integrated Project Delivery Teams

This document guides you through a process for the early design phases of retrofit projects to help you mitigate frustrations commonly experienced by building owners and designers. It outlines the value of forming an integrated project delivery team and developing a communication and information-sharing infrastructure that fosters collaboration. This guide does not present a complete process for designing an energy retrofit for a building. Instead, it focuses on the early design phase tasks related to developing and selecting energy efficiency measures (EEMs) that benefit from collaboration, and highlights the resulting advantages.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2020

NASA GPU Hackathon Yields Significant Code Improvements

The NASA GPU Hackathon 2020 brought together application developers and computer experts to help get important NASA applications running effectively on graphics processing unit (GPU) nodes. Nine teams of application developers participated in this virtual event, a major impetus for teams to modernize codes of interest for NASA missions to CPU nodes containing GPU accelerators, with a focus on hands-on problem solving. The photo in Figure1 shows 30 of the more than50 participants. The HECC project and NVIDIA jointly organized the event, and HECC provided five Pleiades nodes each with 4 V100 GPUs for teams to use. The virtual event, which took place over four days from September 28–October 7, 2020, used Microsoft Teams and Slack as collaboration tools. Each team consisted of three to six members from NASA Centers and supporting organizations. The teams were paired with one to two mentors from industry, government, and academia. The experience levels of the teams ranged from being GPU novices to advanced CUDA programming experts. OpenACC and the emerging Kokkos API were used in addition to CUDA for GPU programming. During the event, which focused on accelerating AeroSciences and CFD applications, most teams achieved considerable performance improvements on both GPUs and CPUs. For example, a team with no GPU experience completed a first port of a time-critical loop to a GPU. Another team of expert CUDA programmers were able to restructure their algorithm, yielding a factor of five speed-up. And another team sped up some of their CUDA kernels by a factor of 20, which directly translated into their production code. This article highlights some of the many successes resulting from the event.

HECC↗
DOE OSTI2025

Modernizing the Nuclear Industry and New Ways of Working

Nuclear energy is recognized as the most feasible energy source towards achieving nation-wide net-zero goals (COP 208, MIT). Additionally, the demand for reliable, consistent energy supply is soaring with the construction of energy hungry data centers and AI tools. This means the construction of new nuclear and the continuation of current nuclear are a high national priority. However, this leaves the nuclear industry with a workforce challenge. The rising demand for nuclear power is threatened by an underpopulated workforce. There exist a few contributors to the decreased workforce such as skewed workforce demographics resulting in large-scale workforce retirement. Also, some plants have observed difficulty hiring and retaining skill sets that typically comprise the nuclear power workforce – specifically skilled craftsman. Those who are graduating with desirable skills are seeking employment in sectors that are more modern and culturally more aligned with younger generational values. Lastly, within the nuclear sector, skilled employees will be a competitive commodity as advanced plants come online offering work environments that incorporate modern technologies, skill sets, and opportunities for career advancement. These factors emphasize the need for legacy plants to modernize with a focus on the workplace environment and culture that meets younger generations’ skills, cultural expectations, and desire for advancement opportunities. Nuclear plants are actively engaged in deploying advancements that improve the management of systems, structures, and components as well as process improvements and technologies that can reduce the costs of operation and maintenance. However, the advancements must also be analyzed, reviewed and deployed in a manner that considers the impact on how people within the organization collaborate, communicate, make decisions, and solve problems. Neglecting to consider the cultural impact of modernization risks poor adoption, unrealized opportunities, or insignificant change toward helping attract new employees. The traditional way of working is not necessarily the way new generations want to engage with their employer. For instance, in a survey performed by North American Young Generation in Nuclear the top three reasons for younger nuclear employees to seek other job opportunities was seeking better work-life balance, a lack of advancement opportunities, and work culture and leadership style differences (Smyth et al. 2022). Integrated Operations for Nuclear (ION) is an approach for the nuclear industry to create long-term strategic modernization plans and analyze each advancement for the impact on people and processes and measure how those impacts flow up to support high-level, long-term plant goals and requirements. One principle of ION is to replace the current labor-centric operation style in legacy nuclear plants with data-centric collaborative operation styles. This paper will explain how transitioning operations to leverage centralized skills and responsibility, multi-skilled teams, and collaborative decision making can flatten the hierarchical structure in plants, enable greater individual efficacy while also offering more experiences and advancement opportunities to staff. Adopting the ION way of working shifts the industry mind-set towards more networked, collaborative, and modern way of working that will attract skilled, next-generation workers to operate the legacy nuclear fleet.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2014

Near Real-time Data Analysis of Core-collapse Supernova Simulations with Bellerophon

We present an overview of a software system, Bellerophon, built to support a production-level HPC application called CHIMERA, which simulates core-collapse supernova events at the petascale. Developed over the last four years, Bellerophon enables CHIMERA's geographically dispersed team of collaborators to perform data analysis in near real-time. Its n-tier architecture provides an encapsulated, end-to-end software solution that enables the CHIMERA team to quickly and easily access highly customizable animated and static views of results from anywhere in the world via a web- deliverable, cross-platform desktop application. In addition, Bellerophon addresses software engineering tasks for the CHIMERA team by providing an automated mechanism for performing regression testing on a variety of supercomputing platforms. Elements of the team's workflow management needs are met with software tools that dynamically generate code repository statistics, access important online resources, and monitor the current status of several supercomputing resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Near Real-time Data Analysis of Core-Collapse Supernova Simulations With Bellerophon

We present an overview of a software system, Bellerophon, built to support a production-level HPC application called CHIMERA, which simulates core-collapse supernova events at the petascale. Developed over the last four years, Bellerophon enables CHIMERA s geographically dispersed team of collaborators to perform data analysis in near real-time. Its n-tier architecture provides an encapsulated, end-to-end software solution that enables the CHIMERA team to quickly and easily access highly customizable animated and static views of results from anywhere in the world via a web-deliverable, cross-platform desktop application. In addition, Bellerophon addresses software engineering tasks for the CHIMERA team by providing an automated mechanism for performing regression testing on a variety of supercomputing platforms. Elements of the team s workflow management needs are met with software tools that dynamically generate code repository statistics, access important online resources, and monitor the current status of several supercomputing resources.

Lingerfelt, Eric J↗
DOE OSTI2017

Bellerophon

The Bellerophon software system supports CHIMERA, a production-level HPC application that simulates the evolution of core-collapse supernovae. Bellerophon enables CHIMERA's geographically dispersed team of collaborators to perform job monitoring and real-time data analysis from multiple supercomputing resources, including platforms at OLCF, NERSC, and NICS. Its multi-tier architecture provides an encapsulated, end-to-end software solution that enables the CHIMERA team to quickly and easily access highly customizable animated and static views of results from anywhere in the world via a cross-platform desktop application.

Lingerfelt, Eric J↗
DOE OSTI2022

Abstract for CRADA among National Energy Technology Laboratory, Research Triangle Institute, Lawrence Livermore National Laboratory, and Los Alamos National Laboratory (AGMT-0921)

Through the Carbon Capture Simulation for Industry Impact (CCSI 2 ) project, the National Energy Technology Laboratory (NETL), Lawrence Livermore National Laboratory (LLNL), and Los Alamos National Laboratory (LANL) (herein collectively “the CCSI 2 team”) will collaborate through a CRADA with the Research Triangle Institute (RTI). This collaboration will develop a modeling framework of RTI’s Non-Aqueous Solvent (NAS) process using physical and thermodynamic data and models previously developed by RTI. RTI will provide existing models and data on its NAS system to inform the model construction effort by the CCSI 2 team.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2020

Strategic Deconfliction Performance: Results and Analysis from the NASA UTM Technical Capability Level 4 Demonstration

Unmanned Aircraft System (UAS) Traffic Management (UTM) refers to the service-based, cooperative approach to the management of small UAS in the National Airspace System that is safe, scalable, and fair. UTM provides the means to manage the airspace in a complementary manner that does not burden the current air traffic control workforce or infrastructure but allows the Air Navigation Service Provider to maintain its regulatory and operational authority of the airspace. A key feature of UTM is the ability to provide operators the means to strategically deconflict operations from others in the airspace through the digital exchange of information via supporting services. Through this approach, the four-dimensional operation volumes that encompass the intent of operators in a given area are discoverable and can be used for airspace awareness as well as planning conflict free operations that account for and avoid other operations. In certain cases, it is also possible to negotiate volume intersections for shared airspace use without the need to re-plan. In the NASA UTM concept, strategic deconfliction is the first layer of three in the overall conflict management model. The three layers of the conflict management model, which follow the International Civil Aviation Organization’s scheme [ICAO 2005] are: strategic conflict management, separate provision, and collision avoidance. In UTM, the strategic layer mostly occurs prior to departure, but is applicable to en route operations with sufficient planning horizon. The initial requirements for a strategic deconfliction capability within UTM are defined in a NASA publication [Rios 2018]. Within the concept and implementation of service-provided strategic deconfliction is the notion of priority. It is understood that there are instances in which an operation requires a priority designation within the UTM system and special handling accordingly to provide situation awareness and facilitate appropriate responses from other airspace users. Examples of situations requiring priority designation include: when an operator declares an emergency due to problems with the vehicle or its immediate surroundings; operations that are in support of certain organizations (e.g., public safety and first responders); or special missions that also require priority use of airspace (e.g., emergency medical deliveries). UAS Volume Reservations (UVRs) also relate to the topic of priority in the sense that the airspace that the volume encompasses has a different status or classification in which unassociated operations must vacate if inside, or avoid if outside, through strategic deconfliction with the volume. Operations that are specially permitted to access the UVR area are typically assigned priority status given the nature of their mission and their associated credentials. The ability to perform strategic deconfliction, handle certain operations with a priority distinction, and establish UVRs that are communicated throughout the UTM system, is predicated on an architecture that has been established through an evolutionary process in response to close collaboration with stakeholders from government and industry. Another important and influential aspect of these capabilities and architecture is the live, distributed flight tests that have been conducted across the Technical Capability Levels (TCLs) that culminated with a set of complex tests performed as part of TCL4 [Rios 2020]. The TCL4 flight test involved two FAA-designated UAS test sites building teams to collaborate with NASA’s UTM Project on the execution of several detailed, small UAS scenarios in urban environments.

conflict management↗
DOE OSTI2017

Carbon Capture Multidisciplinary Simulation Center Trilab Support Team (TST) Fall Meeting 2016 Report

The theme of this year’s meeting was “Predictivity: Now and in the Future”. After welcoming remarks, Erik Draeger gave a talk on the NNSA Labs’ history of predictive simulation and the new challenges faced by upcoming architecture changes. He described an example where the volume of analysis data produced by a set of inertial confinement fusion (ICF) simulations on the Trinity machine was too large to store or transfer, and the steps needed to reduce it to a manageable size. He also described the software re-engineering plan for LLNL’s suite of multiphysics codes and physics packages with a new push toward common components, making collaboration with teams like the CCMSC who already have experience trying to architect complex multiphysics code infrastructure on next-generation architectures all the more important. Phil Smith then gave an overview outlining the goals of the project, namely to accelerate development of new technology in the form of high efficiency carbon capture pulverized coal power generation as well as further optimize existing state of the art designs. He then presented a summary of the Center’s top-down uncertainty quantification approach, in which ultimate target predictivity informs uncertainty targets for lower-level components, and gave data on how close all the different components currently are to their targets. Most components still need an approximately two-fold reduction in uncertainty to hit the ultimate predictivity target, but the current accuracy is already rather impressive.

01 COAL, LIGNITE, AND PEAT↗
NASA NTRS2018

Modeling Spacecraft Safe Mode Events

Spacecraft enter a ‘safe mode’ to protect the vehicle when a potentially harmful anomaly occurs. This minimally functioning state isolates faults, establishes contact with Earth, and orients the vehicle into a power positive attitude until operators intervene. Though ‘safings’ are inherently unpredictable, mission teams build in time margin during operations to determine root causes and restore functionality. Planning and managing this margin is both critical and enabling on mission architectures dependent on near-continuous operability – such as a low-thrust electric propulsion mission. To better quantify the occurrences and severity of safe mode anomalies, the Jet Propulsion Laboratory (JPL) has assembled a database of safings from past and active missions. Currently nearly 240 records are captured from 21 beyond-Earth missions, stemming from a collaboration between teams at JPL, Ames Research Center, Goddard Space Flight Center, and the Johns Hopkins University Applied Physics Laboratory. This paper discusses the event database, explores a statistical approach in modeling the occurrences and severity of safing events, presents a simulation technique, and details recommendations and future work to benefit future concepts.

Nicholas, Austin↗
DOE OSTI2012

Alternative D and D Planning Tool - 12466

On August 1, 2011, URS - CH2M Oak Ridge LLC (UCOR) began cleanup of the East Tennessee Technology Park (ETTP). UCOR's $2.2 billion contract has an initial five-year term and a four-year option period for completing the cleanup of ETTP and performing surveillance and maintenance and waste management operations at both the Oak Ridge National Laboratory and the Y-12 National Security Complex. ETTP D and D work includes disposition of large, complex, contaminated, Manhattan Project-era facilities such as the K-25 and K-27 uranium enrichment facilities. At ETTP, UCOR views the D and D process as a 'Waste Factory' with waste production lines from the point-of-generation to the point-of-disposal. Safely transforming vertically-standing buildings into horizontally-lying waste in a disposal facility is the primary cleanup objective. Whereas a factory produces widgets, D and D produces waste-lots of waste. In support of the Waste Factory view, UCOR is developing a systems planning tool to help better plan how to effect cleanup by improving waste planning, uniting waste generator with waste dis-positioner, and represent the 'waste factory' in a computer model that allows the D and D and waste management teams to better understand available disposal paths, waste uncertainties and potential consequences, driving variables, and sensitivity to changes. Any model of reality represents a compromise. Part of the Waste Factory Model's value may be in providing standardization and relative direction for assisting decision making as opposed to absolute cost or schedule answers. From that relative direction, management can commission detailed planning and estimating. Also, the model's output credibility is tied directly to its input quality. That is why, as discussed above, the Waste Factory Model's key informational component will be the standardized waste streams (e.g., Structure/Debris disposed at EMWMF) and associated standardized unit costs. The model development process generally, and the development team's collaboration, specifically, is most important. Building-in this integrity up-front, transparently, will help ensure that the model outputs are known, understood, and credible (no mysterious 'black-box' components). The Waste Factory Model is envisioned to help improve understanding of a given project's waste implications; help reveal and suggest the important few variables out of the trivial many; reveal likely process bottlenecks, and help focus risk mitigation and assumption management efforts to better manage the projected total cost. In the long term, the UCOR team's goal is to incorporate an information feedback loop to help improve the accuracy of key model attributes, such as the standardized cost information. This way, by design, the model output should improve over time and thus help the model mature into both a credible scenario tool and a standardized project planning resource. The initial model capabilities may be narrow, but with time and effort it may prove to be a timely and cost-effective tool. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2020

CloudSat - From the A-Train to the C-Train

The CloudSat satellite was launched from Vandenberg Air Force Base, in California, on April 28, 2006. It was launched with the CALIPSO satellite, a joint mission between the National Aeronautics and Space Administration (NASA) and the Centre National D'Etudes Spatiales (CNES). CloudSat and CALIPSO were designed to fly in formation with each other, to combine their complimentary scientific measurements. The two missions were maneuvered into the Earth Science Afternoon Constellation (A-Train) shortly after launch and have flown in formation together since then. Originally designed to fly for 22 months, the CloudSat satellite has far exceeded expectations. Inflight anomalies on CloudSat necessitated changes to both the daily concept of operations and formation flying operations with CALIPSO. In 2011, a severe battery anomaly restricted CloudSat science data collection and maneuverability to the sunlit portions of the orbit. In 2017, CloudSat suffered a reaction wheel failure, triggering the criteria established to exit the A-Train. CloudSat successfully exited the A-Train in early 2018, suspending formation flying and joint science observations with CALIPSO. The joint CloudSat/CALIPSO science team continued collaboration with discussions focused on resuming joint observations. As both satellites were aging and CALIPSO was nearing the exhaustion of satellite propellant, the decision was made to lower CloudSat to the CALIPSO graveyard orbit, to be joined by CALIPSO as soon as feasible. With NASA’s approval, CloudSat was maneuvered to the lower orbit over the course of several months in mid-2018. Once CloudSat was in place, and with approval from CNES, CALIPSO began the delicate dance of lowering their orbit and re-aligning the satellite ground tracks. CloudSat and CALIPSO resumed formation flying in the newly dubbed C-Train (CloudSat and CALIPSO) orbit in the Fall of 2018. The ingenuity of the CloudSat and CALIPSO teams made the concept of the C-Train a reality. This paper will discuss the decision processes and the operational challenges the CloudSat flight team faced while designing and implementing the C-Train.

Witkowski, M.M.↗
NASA NTRS2018

Considerations in Assuring Safety of Increasingly Autonomous Systems

Recent technological advances have accelerated the development and application of increasingly autonomous (IA) systems in civil and military aviation. IA systems can provide automation of complex mission tasks-ranging across reduced crew operations, air-traffic management, and unmanned, autonomous aircraft-with most applications calling for collaboration and teaming among humans and IA agents. IA systems are expected to provide benefits in terms of safety, reliability, efficiency, affordability, and previously unattainable mission capability. There is also a potential for improving safety by removal of human errors. There are, however, several challenges in the safety assurance of these systems due to the highly adaptive and non-deterministic behavior of these systems, and vulnerabilities due to potential divergence of airplane state awareness between the IA system and humans. These systems must deal with external sensors and actuators, and they must respond in time commensurate with the activities of the system in its environment. One of the main challenges is that safety assurance, currently relying upon authority transfer from an autonomous function to a human to mitigate safety concerns, will need to address their mitigation by automation in a collaborative dynamic context. These challenges have a fundamental, multidimensional impact on the safety assurance methods, system architecture, and V&V capabilities to be employed. The goal of this report is to identify relevant issues to be addressed in these areas, the potential gaps in the current safety assurance techniques, and critical questions that would need to be answered to assure safety of IA systems. We focus on a scenario of reduced crew operation when an IA system is employed which reduces, changes or eliminates a human's role in transition from two-pilot operations.

Alves, Erin E.↗
DOE OSTI2026

“Frameworks, Algorithms and Scalable Technologies for Mathematics (FASTMath) SciDAC Institute” (Final Technical Report)

SMU personnel formed a portion of the overall “Time Integration” team within the FASTMath SciDAC-5 Institute, and we interacted very closely with team members from collaborating institutions. The major goals of our team within the FASTMath institute may be categorized into two groups: the development of advanced and application-aware time integration methods and software, and close interactions with DOE application scientists to facilitate their use of these new methods and software. The two goals are intimately linked, since our research and development of novel tools is informed by the needs of our application partners, who in turn benefit from subsequent mathematical and software advances. While the SMU personnel collaborated with the rest of the Time Integration FASTMath team on most of our shared deliverables within the larger FASTMath institute, SMU personnel primarily contributed to the following subset of those goals: ● expanding capabilities for higher-order and solve-decoupled multirate methods, ● enhancing support for temporal adaptivity within multirate methods, and ● adding structure-aware time integration methods. Each of these goals focused both on intellectual contributions through journal articles or research presentations, and on enhancements to the open-source SUNDIALS library of time integrators and nonlinear solvers, of which the SMU PI Reynolds is a core developer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

BigData Express: Toward Schedulable, Predictable, and High-Performance Data Transfer

Big Data has emerged as a driving force for scientific discoveries. Large scientific instruments (e.g., colliders, and telescopes) generate exponentially increasing volumes of data. To enable scientific discovery, science data must be collected, indexed, archived, shared, and analyzed, typically in a widely distributed, highly collaborative manner. Data transfer is now an essential function for science discoveries, particularly within big data environments. Although significant improvements have been made in the area of bulk data transfer, the currently available data transfer tools and services can not successfully address the high-performance and time-constraint challenges of data transfer required by extreme-scale science applications for the following reasons: disjoint end-to-end data transfer loops, cross-interference between data transfers, and existing data transfer tools and services are oblivious to user requirements (deadline and QoS requirements). Fermilab has been working on the BigData Express project to address these problems. BigData Express seeks to provide a schedulable, predictable, and high-performance data transfer service for big data science. The BigData Express software is being deployed and evaluated at multiple research institutions, which include UMD, StarLight, FNAL, KISTI, KSTAR, SURFnet, Ciena, and other sites. Meanwhile, the BigData Express research team is collaborating with the StarLight International/National Communications Exchange Facility to deploy BigData Express at various research platforms, including Pacific Research Platform, National Research Platform, and Global Research Platform. It is envisioned that we are working toward building a high-performance data transfer federation for big data science.

Lu, Qiming↗
NASA NTRS2005

Enhancing the Design Process for Complex Space Systems through Early Integration of Risk and Variable-Fidelity Modeling

An important enabler of the new national Vision for Space Exploration is the ability to rapidly and efficiently develop optimized concepts for the manifold future space missions that this effort calls for. The design of such complex systems requires a tight integration of all the engineering disciplines involved, in an environment that fosters interaction and collaboration. The research performed under this grant explored areas where the space systems design process can be enhanced: by integrating risk models into the early stages of the design process, and by including rapid-turnaround variable-fidelity tools for key disciplines. Enabling early assessment of mission risk will allow designers to perform trades between risk and design performance during the initial design space exploration. Entry into planetary atmospheres will require an increased emphasis of the critical disciplines of aero- and thermodynamics. This necessitates the pulling forward of EDL disciplinary expertise into the early stage of the design process. Radiation can have a large potential impact on overall mission designs, in particular for the planned nuclear-powered robotic missions under Project Prometheus and for long-duration manned missions to the Moon, Mars and beyond under Project Constellation. This requires that radiation and associated risk and hazards be assessed and mitigated at the earliest stages of the design process. Hence, RPS is another discipline needed to enhance the engineering competencies of conceptual design teams. Researchers collaborated closely with NASA experts in those disciplines, and in overall space systems design, at Langley Research Center and at the Jet Propulsion Laboratory. This report documents the results of this initial effort.

Mavris, Dimitri↗
DOE OSTI2012

Argonne Leadership Computing Facility 2011 annual report: Shaping future supercomputing

The ALCF's Early Science Program aims to prepare key applications for the architecture and scale of Mira and to solidify libraries and infrastructure that will pave the way for other future production applications. Two billion core-hours have been allocated to 16 Early Science projects on Mira. The projects, in addition to promising delivery of exciting new science, are all based on state-of-the-art, petascale, parallel applications. The project teams, in collaboration with ALCF staff and IBM, have undertaken intensive efforts to adapt their software to take advantage of Mira's Blue Gene/Q architecture, which, in a number of ways, is a precursor to future high-performance-computing architecture. The Argonne Leadership Computing Facility (ALCF) enables transformative science that solves some of the most difficult challenges in biology, chemistry, energy, climate, materials, physics, and other scientific realms. Users partnering with ALCF staff have reached research milestones previously unattainable, due to the ALCF's world-class supercomputing resources and expertise in computation science. In 2011, the ALCF's commitment to providing outstanding science and leadership-class resources was honored with several prestigious awards. Research on multiscale brain blood flow simulations was named a Gordon Bell Prize finalist. Intrepid, the ALCF's BG/P system, ranked No. 1 on the Graph 500 list for the second consecutive year. The next-generation BG/Q prototype again topped the Green500 list. Skilled experts at the ALCF enable researchers to conduct breakthrough science on the Blue Gene system in key ways. The Catalyst Team matches project PIs with experienced computational scientists to maximize and accelerate research in their specific scientific domains. The Performance Engineering Team facilitates the effective use of applications on the Blue Gene system by assessing and improving the algorithms used by applications and the techniques used to implement those algorithms. The Data Analytics and Visualization Team lends expertise in tools and methods for high-performance, post-processing of large datasets, interactive data exploration, batch visualization, and production visualization. The Operations Team ensures that system hardware and software work reliably and optimally; system tools are matched to the unique system architectures and scale of ALCF resources; the entire system software stack works smoothly together; and I/O performance issues, bug fixes, and requests for system software are addressed. The User Services and Outreach Team offers frontline services and support to existing and potential ALCF users. The team also provides marketing and outreach to users, DOE, and the broader community.

99 GENERAL AND MISCELLANEOUS↗