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At least 559 records · Page 31

What Multilevel Parallel Programs do when you are not Watching: A Performance Analysis Case Study Comparing MPI/OpenMP, MLP, and Nested OpenMP

With the current trend in parallel computer architectures towards clusters of shared memory symmetric multi-processors, parallel programming techniques have evolved that support parallelism beyond a single level. When comparing the performance of applications based on different programming paradigms, it is important to differentiate between the influence of the programming model itself and other factors, such as implementation specific behavior of the operating system (OS) or architectural issues. Rewriting-a large scientific application in order to employ a new programming paradigms is usually a time consuming and error prone task. Before embarking on such an endeavor it is important to determine that there is really a gain that would not be possible with the current implementation. A detailed performance analysis is crucial to clarify these issues. The multilevel programming paradigms considered in this study are hybrid MPI/OpenMP, MLP, and nested OpenMP. The hybrid MPI/OpenMP approach is based on using MPI [7] for the coarse grained parallelization and OpenMP [9] for fine grained loop level parallelism. The MPI programming paradigm assumes a private address space for each process. Data is transferred by explicitly exchanging messages via calls to the MPI library. This model was originally designed for distributed memory architectures but is also suitable for shared memory systems. The second paradigm under consideration is MLP which was developed by Taft. The approach is similar to MPi/OpenMP, using a mix of coarse grain process level parallelization and loop level OpenMP parallelization. As it is the case with MPI, a private address space is assumed for each process. The MLP approach was developed for ccNUMA architectures and explicitly takes advantage of the availability of shared memory. A shared memory arena which is accessible by all processes is required. Communication is done by reading from and writing to the shared memory.

Jost, Gabriele↗

Structural Health Management for Future Aerospace Vehicles

Structural Health Management (SHM) will be of critical importance to provide the safety, reliability and affordability necessary for the future long duration space missions described in America's Vision for Space Exploration. Long duration missions to the Moon, Mars and beyond cannot be accomplished with the current paradigm of periodic, ground based structural integrity inspections. As evidenced by the Columbia tragedy, this approach is also inadequate for the current Shuttle fleet, thus leading to its initial implementation of on-board SHM sensing for impact detection as part of the return to flight effort. However, future space systems, to include both vehicles as well as structures such as habitation modules, will require an integrated array of onboard in-situ sensing systems. In addition, advanced data systems architectures will be necessary to communicate, store and process massive amounts of SHM data from large numbers of diverse sensors. Further, improved structural analysis and design algorithms will be necessary to incorporate SHM sensing into the design and construction of aerospace structures, as well as to fully utilize these sensing systems to provide both diagnosis and prognosis of structural integrity. Ultimately, structural integrity information will feed into an Integrated Vehicle Health Management (IVHM) system that will provide real-time knowledge of structural, propulsion, thermal protection and other critical systems for optimal vehicle management and mission control. This paper will provide an overview of NASA research and development in the area of SHM as well as to highlight areas of technology improvement necessary to meet these future mission requirements.

Prosser, W. H.↗

Effects of a Scanning Reflectarray Antenna on Modulated Data Modeled

The ferroelectric reflectarray is a new type of scanning antenna under development for space and ground communications and radar applications at the NASA Glenn Research Center. The reflectarray offers tremendous cost, gain, and efficiency advantages in comparison to the state-of-the-art phased arrays, but there is an inherent intersymbol interference (ISI) problem with digital communication data streams processed through the array because of the way the reflectarray is operated. This interference stems from the fact that the antenna beam is formed by the superposition of reflected waves from the array elements. The delays from the elements to the observation point of each wave differ. Ferroelectric phase shifters are designed to compensate for the modulo-2p phase differences so that the waves will constructively interfere at the sinusoidal carrier level; however, the delays of the integer-number multiple of the carrier period are not compensated for. This lack of compensation causes ISI in digital modulated signals. ISI has not been a problem with analog modulations and low-rate digital modulations since the distortion (or ISI) introduced is negligible. The problem becomes acute when the data rate gets higher, and the electrical size of the array gets bigger. Moreover, there are intrinsic transient effects associated with the phase shifters and the controller that executes the algorithm to direct the beam. That is, phase shifter updates to redirect the beam may be initiated simultaneously, but nonlinearity will cause different settling times. The ISI problem and other effects ultimately limit the size (gain) and data rate of the reflectarray. An ISI analysis was required to prove or disprove the suitability of the reflectarray antenna for high-rate digitally modulated signals and to give guidance for determining antenna parameters. In this research, the nature of the ISI of the reflectarray has been mathematically modeled.

Romanofsky, Robert R.↗

An Integrated Approach To Payload System Simulation

This paper describes a payload system simulation implemented at JPL as part of a comprehensive mission simulation facility. The flight software function includes communication with other process modules, instrument control, and data management. The payload system simulation software consists of: a camera subsystem, a virtual world, and a mission visualization toolset.

dynamics model photometric model geological model ↗

An Integrated Fault Tolerant Robotic Controller System for High Reliability and Safety

This paper describes the concepts and features of a fault-tolerant intelligent robotic control system being developed for applications that require high dependability (reliability, availability, and safety). The system consists of two major elements: a fault-tolerant controller and an operator workstation. The fault-tolerant controller uses a strategy which allows for detection and recovery of hardware, operating system, and application software failures.The fault-tolerant controller can be used by itself in a wide variety of applications in industry, process control, and communications. The controller in combination with the operator workstation can be applied to robotic applications such as spaceborne extravehicular activities, hazardous materials handling, inspection and maintenance of high value items (e.g., space vehicles, reactor internals, or aircraft), medicine, and other tasks where a robot system failure poses a significant risk to life or property.

robotics fault-tolerant control systems↗

REE radiation fault model: a tool for organizing and communication radiation test data and construction COTS based spacebourne computing systems

The growth in data rates of instruments on future NASA spacecraft continues to outstrip the improvement in communications bandwidth and processing capabilities of radiation-hardened computers. Sophisticated autonomous operations strategies will further increase the processing workload. Given the reductions in spacecraft size and available power, standard radiation hardened computing systems alone will not be able to address the requirements of future missions. The REE project was intended to overcome this obstacle by developing a COTS- based supercomputer suitable for use as a science and autonomy data processor in most space environments. This development required a detailed knowledge of system behavior in the presence of Single Event Effect (SEE) induced faults so that mitigation strategies could be designed to recover system level reliability while maintaining the COTS throughput advantage. The REE project has developed a suite of tools and a methodology for predicting SEU induced transient fault rates in a range of natural space environments from ground-based radiation testing of component parts. In this paper we provide an overview of this methodology and tool set with a concentration on the radiation fault model and its use in the REE system development methodology. Using test data reported elsewhere in this and other conferences, we predict upset rates for a particular COTS single board computer configuration in several space environments.

Radiation Effects Modeling COTS computers REE SEU ↗

Research to Operations: The Critical Transition

Space Life Sciences Directorate (SLSD) specializes in transitioning technology and knowledge to medical operations. This activity encompasses funding a spectrum of research and technology efforts, such as understanding fundamental biological mechanisms altered by microgravity and executing technology watches for state of the art diagnostic imaging equipment. This broad spectrum approach to fulfilling the need to protect crewmember health and performance during long and short duration missions to the International Space Station, moon and Mars is made possible by having a line of site between research and operations. Currently, SLSD's line of site is articulated in a transition to medical practice (TMP) process. This process is designed to shepherd information and knowledge gained through fundamental and mechanistic research toward the development of an operational solution such as a pre-flight selection criteria; an in-flight countermeasure, monitoring capability or treatment; or a post-flight reconditioning program. The TMP process is also designed to assist with the customization of mature hardware or technology for NASA specific use. The benefits of this process are that the concept of operational usability is interjected early in the research, design, or acquisition phase, and stakeholders are involved early to identify requirements and also periodically asked to assess requirements compliance of research or technology development project. Currently a device known as the actiwatch is being assessed for the final transition to operational use. Specific examples of research to operations transition success help to illustrate the process and bolster communication between the research and medical operations communities.

Fogarty, Jennifer A.↗

Global Environmental Micro Sensors Test Operations in the Natural Environment

ENSCO, Inc. is developing an innovative atmospheric observing system known as Global Environmental Micro Sensors (GEMS). The GEMS concept features an integrated system of miniaturized in situ, airborne probes measuring temperature, relative humidity, pressure, and vector wind velocity. In order for the probes to remain airborne for long periods of time, their design is based on a helium-filled super-pressure balloon. The GEMS probes are neutrally buoyant and carried passively by the wind at predetermined levels. Each probe contains onboard satellite communication, power generation, processing, and geolocation capabilities. ENSCO has partnered with the National Aeronautics and Space Administration's Kennedy Space Center (KSC) for a project called GEMS Test Operations in the Natural Environment (GEMSTONE) that will culminate with limited prototype flights of the system in spring 2007. By leveraging current advances in micro and nanotechnology, the probe mass, size, cost, and complexity can be reduced substantially so that large numbers of probes could be deployed routinely to support ground, launch, and landing operations at KSC and other locations. A full-scale system will improve the data density for the local initialization of high-resolution numerical weather prediction systems by at least an order of magnitude and provide a significantly expanded in situ data base to evaluate launch commit criteria and flight rules. When applied to launch or landing sites, this capability will reduce both weather hazards and weather-related scrubs, thus enhancing both safety and cost-avoidance for vehicles processed by the Shuttle, Launch Services Program, and Constellation Directorates. The GEMSTONE project will conclude with a field experiment in which 10 to 15 probes are released over KSC in east central Florida. The probes will be neutrally buoyant at different altitudes from 500 to 3000 meters and will report their position, speed, heading, temperature, humidity, and pressure via satellite. The GEMS data will be validated against reference observations provided by current weather instrumentation located at KSC. This paper will report on the results of the GEMSTONE project and discuss the challenges encountered in developing an airborne sensor system.

Adams, Mark L.↗

International Space Station Columbus Payload SoLACES Degradation Assessment

SOLAR is a European Space Agency (ESA) payload deployed on the International Space Station (ISS) and located on the Columbus Laboratory. It is located on the Columbus External Payload Facility in a zenith location. The objective of the SOLAR payload is to study the Sun. The SOLAR payload consists of three instruments that allow for measurement of virtually the entire electromagnetic spectrum (17 nm to 100 um). The three payload instruments are SOVIM (SOlar Variable and Irradiance Monitor), SOLSPEC (SOLar SPECctral Irradiance measurements), and SolACES (SOLar Auto‐Calibrating Extreme UV/UV Spectrophotometers). The SolACES payload includes a set of 4 spectrometers that measure the solar EUV flux from 17 nm to 220 nm. One of these 4 spectrometers failed early on (before deployment). EUV data is important in understanding the solar dynamo. Also, EUV flux is the source of most of the ionization that produces the ionosphere plasma. Plasma production is important in understanding the ionosphere environment. The ionosphere conditions affect many subjects including spacecraft charging, dynamo processes, instabilities, and communications. The 3 remaining spectrometers have collected valuable data during the historically low solar cycle 24. Some of this data will be presented. A significant trend in degradation of the remaining SolACES spectrometers was observed towards the end of CY2010 (GMT 310) through mid CY 2011 (GMT 132). The Principle Investigators of SolACES initiated a Mission Evaluation Room (MER) Chit to request an investigation of the degradation in CY 2011 (GMT 230). The Boeing Space Environments team was asked to respond to the ESA initiated MER Chit request to investigate the cause of the degradation. This paper will discuss the findings of that investigation.

Harman, William↗

On-Orbit Validation of a Framework for Spacecraft-Initiated Communication Service Requests with NASA's SCaN Testbed

We design, analyze, and experimentally validate a framework for demand-based allocation of high-performance space communication service in which the user spacecraft itself initiates a request for service. Leveraging machine-to-machine communications, the automated process has potential to improve the responsiveness and efficiency of space network operations. We propose an augmented ground station architecture in which a hemispherical-pattern antenna allows for reception of service requests sent from any user spacecraft within view. A suite of ground-based automation software acts upon these direct-to-Earth requests and allocates access to high-performance service through a ground station or relay satellite in response to immediate user demand. A software-defined radio transceiver, optimized for reception of weak signals from the helical antenna, is presented. Design and testing of signal processing equipment and a software framework to handle service requests is discussed. Preliminary results from on-orbit demonstrations with a testbed onboard the International Space Station are presented to verify feasibility of the concept.

Adam M Gannon↗

Data Science and Urban Air Mobility: Challenges and Opportunities

Aviation is broadly a combination of aircraft, airspace and airports. The data science life cycle comprises of five steps - capture, maintain, process, analyze and communicate. The presentation introduces the legacy of conventional aviation research in the context of the data science life cycle to motivate the challenges with Urban Air Mobility, a field that is quite nascent. A summary of recent research will be presented to highlight the innovative ways to address the challenges. Examples provided will include the generation of synthetic data, encounter models from simulations, and leveraging novel and diverse data sets from traditional transportation and non-aviation sources, to analyze problems of operation in urban airspace. Finally, opportunities will be identified for further exploration, niche development and filling the gaps in the field of data science for UAM.

Urban Air Mobility↗

Data Science and Urban Air Mobility: Challenges and Opportunities

Aviation is broadly a combination of aircraft, airspace and airports. The data science life cycle comprises of five steps - capture, maintain, process, analyze and communicate. The presentation introduces the legacy of conventional aviation research in the context of the data science life cycle to motivate the challenges with Urban Air Mobility, a field that is quite nascent. A summary of recent research will be presented to highlight the innovative ways to address the challenges. Examples provided will include the generation of synthetic data, encounter models from simulations, and leveraging novel and diverse data sets from traditional transportation and non-aviation sources, to analyze problems of operation in urban airspace. Finally, opportunities will be identified for further exploration, niche development and filling the gaps in the field of data science for UAM.

Urban Air Mobility↗

Data Science Challenges for Urban Air Mobility

Aviation is a combination of aircraft, airspace and airports. The data science life cycle comprises of five steps - capture, maintain, process, analyze and communicate. The presentation introduces the legacy of conventional aviation research in the context of the data science life cycle to motivate the challenges with Urban Air Mobility, a field that is quite nascent. A summary of recent research will be presented to highlight the innovative ways to address the challenges. Examples provided will include the generation of synthetic data, encounter models from simulations, and leveraging novel and diverse data sets from traditional transportation and non-aviation sources, to analyze problems of operation in urban airspace. Finally, opportunities will be identified for further exploration, niche development and filling the gaps in the field of data science for UAM.

Data Science↗

An Agile-Like Approach to Hardware Development: The Ejectable Data Recorder (EDR) for Orion's Ascent Abort 2 (AA-2) Test Flight

On July 2, 2019, the Ascent Abort 2 (AA-2) Flight Test Vehicle was launched from Cape Canaveral, with the goal of demonstrating the performance of Orion’s Launch Abort System (LAS) and collecting data from hundreds of sensors throughout the vehicle. The data collected during this test flight is of paramount importance, as it will be used to certify the Orion vehicle for human spaceflight. Originally, the data was to be downlinked via a single string network of antennas on the LAS, with the associated risk of potential data dropouts, as well as loss of data once the LAS was jettisoned. Thus, additional antennas were added onto the crew module (CM) to support data downlink post-LAS jettison, a buffer rebroadcast capability was added to fill in any gaps in data downlink transmissions, and an ejectable data recorder (EDR) subsystem was added to the CM as a redundant measure to collect all the instrumentation data. The EDR subsystem was added to the project about one year after the project commenced, which significantly reduced the available development time when compared with the other subsystems of the AA-2 Test Flight. The project was further accelerated by six months, around the critical design review gate. Due to the schedule compression challenge and the fact that the EDR subsystem was a backup system and not flight critical, the EDR subsystem was further challenged to find a new and more efficient way to develop hardware. Thus, the EDR subsystem experimented with different management and systems engineering processes, team sizes, communication methods, and tools. Some examples are novel uses of SharePoint as a Data-centric Project Management & Systems Engineering environment, a continuous testing approach through the lifecycle, and a Skunkworks approach to managing the team. The EDR subsystem blended Commercial Off The Shelf (COTS) hardware with in-house developed hardware and software to create a novel data retrieval capability. The capability evolved rapidly through a hardware in the loop simulation environment that enabled incremental component updates for not only the EDR subsystem but across the entire Crew Module. This paper will present an overview of how the EDR subsystem was managed and compare it to an Agile approach to managing projects. The paper will further provide a recommended approach to future Agile-like hardware development that incorporates lessons learned from the EDR experience.

Agile↗

Data Science Challenges for Urban Air Mobility

Aviation is a combination of aircraft, airspace and airports. The data science life cycle comprises of five steps - capture, maintain, process, analyze and communicate. The presentation introduces the legacy of conventional aviation research in the context of the data science life cycle to motivate the challenges with Urban Air Mobility, a field that is quite nascent. A summary of recent research will be presented to highlight the innovative ways to address the challenges. Examples provided will include the generation of synthetic data, encounter models from simulations, and leveraging novel and diverse data sets from traditional transportation and non-aviation sources, to analyze problems of operation in urban airspace. Finally, opportunities will be identified for further exploration, niche development and filling the gaps in the field of data science for UAM.

Data Science↗

Mission Operations and Command Assurance: Flight Operations Quality Improvements

Flight mission operations systems place human operators in demanding, high risk environments. The objective of Mission Operations and Command Assurance (MO&CA) is to help improve the operational reliability of flight projects by reducing error -- Pareto Analysis shows that the human is the largest error category. MO&CA is a system level task reporting to project flight management and JPL Systems Assurance Division. MO&CA provides direct transfer of knowledge, continuous process improvement, and communication facilitation.

mission↗

Increasing the Uptake of Ecological Model Results in Policy Decisions to Improve Biodiversity Outcomes

Models help decision-makers anticipate the consequences of policies for ecosystems and people; for instance, improving our ability to represent interactions between human activities and ecological systems is essential to identify pathways to meet the 2030 Sustainable Development Goals. However, use of modeling outputs in decision-making remains uncommon. We share insights from a multidisciplinary National Socio-Environmental Synthesis Center working group on technical, communication, and process-related factors that facilitate or hamper uptake of model results. We emphasize that it is not simply technical model improvements, but active and iterative stakeholder involvement that can lead to more impactful outcomes. In particular, trust- and relationship-building with decision-makers are key for knowledge-based decision making. In this respect, nurturing knowledge exchange on the interpersonal (e.g., through participatory processes), and institutional level (e.g., through science-policy interfaces across scales), represent promising approaches. To this end, we offer a generalized approach for linking modeling and decision-making.

Biodiversity-ecosystem function relationships↗

NASA's Small Spacecraft and Distributed Systems: Development and Demonstration of Technologies Enabling Swarms and New Spacecraft Platforms with AI and Edge Computing

NASA’s Small Spacecraft & Distributed Systems (SSDS) within the Research and Technology Mission Directorate (RTMD) expands U.S. capability to execute unique missions through targeted investment, rapid development, and flight demonstration of small spacecraft technologies applicable to exploration, science and the commercial space sector. SSDS strategically invests in technology development and on-orbit demonstrations executed across NASA, other government agencies, industry, and academia. The program’s University SmallSat Technology Partnerships initiative awards academic researchers with the opportunity to collaborate with NASA to mature innovative technology. Capabilities aligned with RTMD’s technology shortfalls and interests - power, processing, propulsion, sensors, communications, autonomous navigation, architectures, and advanced applications like artificial intelligence (AI), machine learning, and edge computing - are prioritized in SSDS investments. These investments enable distributed, autonomous, and cooperative small spacecraft systems that support swarm missions extending beyond low Earth orbit into cislunar and deep space. This paper highlights representative SSDS flight demonstrations that mature these capabilities to enable a future operational infrastructure needed to support sustained exploration of the Moon and beyond. SSDS’s investment strategy emphasizes rapid development and on-orbit demonstration to validate spacecraft technologies required for swarms and distributed mission architectures. The Starling swarm technology demonstration mission exemplifies this approach by advancing distributed spacecraft autonomy, cooperative operations, and space situational awareness. Extended flight testing and ongoing studies of next generation swarm configurations and on-orbit space traffic monitoring and management continue to inform future swarm designs. DiskSat’s four-spacecraft demonstration mission represents SSDS’s strategic vision to expand the design space for future small spacecraft through its commitment to advance novel platform concepts that can impact how science is performed on orbit. Continuing to invest in future platforms, the notional PY12 concept is a 12-spacecraft swarm hosting neuromorphic processors and is envisioned as an on-orbit testbed for AI, edge computing, and positioning, navigation and timing technologies. SSDS also invests in single-spacecraft technology demonstrations that underpin the success of future swarm missions and accelerate the availability of validated technologies across the small spacecraft ecosystem. Examples of such demonstrations include Pathfinder Technology Demonstrator-3 (PTD-3), which performed high-rate optical communications; PTD-R, which demonstrated a camera capable of simultaneous ultraviolet and short-wave infrared optical sensing; and CAPSTONE, the Cislunar Autonomous Positioning System Technology and Operations Navigation Experiment, which validated autonomous navigation in cislunar space. Collectively, SSDS-funded demonstrations advance capabilities across swarms and illustrate a coordinated investment strategy to mature high-impact technologies required for autonomous, distributed, and cooperative small spacecraft systems for low Earth orbit, cislunar, and deep space applications. Technology demonstrations strengthen SSDS partnerships with industry, academia, and other government agencies, and promote small spacecraft community adoption of capabilities required to close technical gaps for swarm missions.

Jan Stupl↗