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High Performance Space Computing with System-on-Chip Instrument Avionics for Space-based Next Generation Imaging Spectrometers (NGIS).
The emergent technology of system-on-chip (SoC) devices promises lighter, smaller, cheaper, and more capable and reliable space electronic systems that could help to unveil some of the most treasured secrets in our universe. This technology is an improvement over the technology that is currently used in space applications, which lags behind stateof-the-art commercial-off-the-shelf (COTS) equipment by several generations. SoC technology integrates all computational power required by next-generation space exploration science instruments onto a single chip. This presentation will describe a Xilinx Zynq-based data acquisition, cloud-screening and compression computing system that has been developed at the Jet Propulsion Laboratory (JPL) for JPL’s Next Generation Imaging Spectrometers (NGIS). The Xilinx Zynq-based Alpha Data hardware assembly fits into a 120mm by 190m by 40mm assembly and uses 9 watts at peak performance. The computing element is a Xilinx Zynq Z7045Q which includes a Kintex-7 FPGA (equivalent to 3 RAD Virtex5 FPGAs in terms of logic cell resources) and dual-core ARM Cortex-A9 Processors (equivalent to 10 RAD750 Power PCs in term of processing capability).
High Performance Space Computing with System-on-Chip Instrument Avionics for Space-based Next Generation Imaging Spectrometers (NGIS).
The emergent technology of system-on-chip (SoC) devices promises lighter, smaller, cheaper, and more capable and reliable space electronic systems that could help to unveil some of the most treasured secrets in our universe. This technology is an improvement over the technology that is currently used in space applications, which lags behind state-of-the-art commercial-off-the-shelf (COTS) equipment by several generations. SoC technology integrates all computational power required by next-generation space exploration science instruments onto a single chip. This presentation will describe a Xilinx Zynq-based data acquisition, cloud-screening and compression computing system that has been developed at the Jet Propulsion Laboratory (JPL) for JPL’s Next Generation Imaging Spectrometers (NGIS). The Xilinx Zynq-based Alpha Data hardware assembly fits into a 120mm by 190m by 40mm assembly and uses 9 watts at peak performance. The computing element is a Xilinx Zynq Z7045Q which includes a Kintex-7 FPGA (equivalent to 3 RAD Virtex5 FPGAs in terms of logic cell resources) and dual-core ARM Cortex-A9 Processors (equivalent to 10 RAD750 Power PCs in term of processing capability).
High-Performance Spaceflight Computing (HPSC) Middleware Release 4 Update
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High performance space computing with system-on-chip instrument avionics for space-based Next Generation Imaging Spectrometers (NGIS)
The emergent technology of system-on-chip (SoC) devices promises lighter, smaller, cheaper, and more capable and reliable space electronic systems that could help to unveil some of the most treasured secrets in our universe. This technology is an improvement over the technology that is currently used in space applications, which lags behind state-of-the-art commercial-off-the-shelf (COTS) equipment by several generations. SoC technology integrates all computational power required by next-generation space exploration science instruments onto a single chip. This presentation will describe a Xilinx Zynq-based data acquisition, cloud-screening and compression computing system that has been developed at the Jet Propulsion Laboratory (JPL) for JPL’s Next Generation Imaging Spectrometers (NGIS). The Xilinx Zynq-based Alpha Data hardware assembly fits into a 120mm by 190m by 40mm assembly and uses 9 watts at peak performance. The computing element is a Xilinx Zynq Z7045Q which includes a Kintex-7 FPGA (equivalent to 3 RAD Virtex5 FPGAs in terms of logic cell resources) and dual-core ARM Cortex-A9 Processors (equivalent to 10 RAD750 Power PCs in term of processing capability).
Space Technology Mission Directorate Game Changing Development Program High Performance Spaceflight Computing
No abstract provided
Medical Resource Set Bulky Item Trade Space Analysis for Spaceflight Medical Risk
The NASA engineering community utilizes event-driven and fault-tree probabilistic techniques to classify risks in the space environment by taking advantage of the inherent knowledge of complex spaceflight system design and testing to quantify failure risk. In harmonizing the risk of human space flight, answering the question of ‘How do we balance health, performance and resource risks with other engineering risks on long duration space missions?’ remains a deeply challenging and largely qualitative practice. The Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) is one aspect of the efforts by NASA’s Human Research Program (HRP) to quantitatively assess the impact of health and performance risk. One of MEDPRAT’s key features is its high degree of computational efficiency. Coupled with the HRP High Performance Compute cluster located at NASA’s Glenn Research Center, MEDPRAT runs millions of simulated missions in a matter of minutes. This degree of computational efficiency provides the novel opportunity to explore the relationship between medical set mass, volume, and medical resource size. Of particular interest for future human spaceflight missions are ‘bulky’ items, medical resources like devices, which occupy a large portion of the small, allocated mass and volume for the medical set leaving less room for other resources. This talk will present results showing the quantitative impact of forced inclusion of several bulky items across a variety of medical kit constraints, and the effect that a potential research investment into reducing the bulky item mass and volume may have on risk.
High Performance Spaceflight Computing (HPSC) for Earth Science Applications
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NASA High Performance Spaceflight Computing (HPSC) Overview
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NASA High Performance Spaceflight Computing (HPSC) Overview
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Integrated edge-to-exascale workflow for real-time steering in neutron scattering experiments
We introduce a computational framework that integrates artificial intelligence (AI), machine learning, and high-performance computing to enable real-time steering of neutron scattering experiments using an edge-to-exascale workflow. Focusing on time-of-flight neutron event data at the Spallation Neutron Source, our approach combines temporal processing of four-dimensional neutron event data with predictive modeling for multidimensional crystallography. At the core of this workflow is the Temporal Fusion Transformer model, which provides voxel-level precision in predicting 3D neutron scattering patterns. The system incorporates edge computing for rapid data preprocessing and exascale computing via the Frontier supercomputer for large-scale AI model training, enabling adaptive, data-driven decisions during experiments. This framework optimizes neutron beam time, improves experimental accuracy, and lays the foundation for automation in neutron scattering. Although real-time experiment steering is still in the proof-of-concept stage, the demonstrated potential of this system offers a substantial reduction in data processing time from hours to minutes via distributed training, and significant improvements in model accuracy, setting the stage for widespread adoption across neutron scattering facilities and more efficient exploration of complex material systems.
What Does a 100,000 Processor Computer Look Like? (and where is the "ON" button?)
Eric Whiting will be giving his "What Does a 100,000 Processor Computer Look Like? (and where is the "ON" button?)" presentation on INL's High Performance Computing to the BYU-Idaho CS/EE/CIT Department's Forum on Thursday, December 3, 2020 at 11:30 a.m. remotely via ZOOM.
CESDIS
CESDIS, the Center of Excellence in Space Data and Information Sciences was developed jointly by NASA, Universities Space Research Association (USRA), and the University of Maryland in 1988 to focus on the design of advanced computing techniques and data systems to support NASA Earth and space science research programs. CESDIS is operated by USRA under contract to NASA. The Director, Associate Director, Staff Scientists, and administrative staff are located on-site at NASA's Goddard Space Flight Center in Greenbelt, Maryland. The primary CESDIS mission is to increase the connection between computer science and engineering research programs at colleges and universities and NASA groups working with computer applications in Earth and space science. Research areas of primary interest at CESDIS include: 1) High performance computing, especially software design and performance evaluation for massively parallel machines; 2) Parallel input/output and data storage systems for high performance parallel computers; 3) Data base and intelligent data management systems for parallel computers; 4) Image processing; 5) Digital libraries; and 6) Data compression. CESDIS funds multiyear projects at U. S. universities and colleges. Proposals are accepted in response to calls for proposals and are selected on the basis of peer reviews. Funds are provided to support faculty and graduate students working at their home institutions. Project personnel visit Goddard during academic recess periods to attend workshops, present seminars, and collaborate with NASA scientists on research projects. Additionally, CESDIS takes on specific research tasks of shorter duration for computer science research requested by NASA Goddard scientists.
Model Assisted Probability of Detection for NASA Space Missions
Model assisted probability of detection (MAPOD) uses data from simulations to improve a traditional probability of detection (POD) study. This could include extending the parameter space to reduce uncertainty or substituting experimental data with simulated data to reduce the time and cost of a POD study. In the past MAPOD was difficult due to limited computational resources, but recent innovations in simulation tools and high-performance computing have made this type of high-degree-of-freedom modeling possible, and complex structures have made it necessary. This presentation will summarize the work done by the computational nondestructive evaluation (CNDE) specialists within the Nondestructive Evaluation Sciences branch at NASA Langley Research Center (LaRC) to complete a MAPOD study for phased array ultrasound testing (PAUT) of a friction stir welding (FSW) method to be used on Space Launch System (SLS) structures. The three critical needs for a MAPOD study are a validated and verified model of the inspection technique for the structure being inspected, some experimental POD data, and an uncertainty model for both the model and the experimental data. PAUT was simulated using Extende CIVA’s UT module. The model was validated using laboratory inspection data from NASA Marshall Space Flight Center (MSFC) for a Hit/Miss POD for FSW in 2219-T87 aluminum panels representative of those used in the SLS. This model was then used to simulate flaw sizes that were originally omitted from the original POD study. The results of this new MAPOD study will be presented along with a discussion of the methods and processes used to analyze the original data, selected simulation parameters, and development of the uncertainty model used for the statistical analysis. The goal of this effort is not just to improve the POD study but to demonstrate the value of MAPOD and provide a roadmap for application of MAPOD on future projects.
Model Assisted Probability of Detection for NASA Space Missions
Model assisted probability of detection (MAPOD) uses data from simulations to improve a traditional probability of detection (POD) study. This could include extending the parameter space to reduce uncertainty or substituting experimental data with simulated data to reduce the time and cost of a POD study. In the past MAPOD was difficult due to limited computational resources, but recent innovations in simulation tools and high-performance computing have made this type of high-degree-of-freedom modelling possible, and complex structures have made it necessary. This presentation will summarize the work done by the computational nondestructive evaluation (CNDE) group at NASA Langley Research Center (LaRC) to complete a MAPOD study for phased array ultrasound testing (PAUT) of a friction stir welding (FSW) method to be used on Space Launch System (SLS) structures. The three critical needs for a MAPOD study are a validated and verified model of the inspection technique for the structure being inspected, some experimental POD data, and an uncertainty model for both the model and the experimental data. PAUT was simulated using Extende CIVA’s UT module. The model was validated using laboratory inspection data from NASA Marshall Space Flight Center (MSFC) for a Hit/Miss POD for FSW in 2219-T87 aluminum panels representative of those used in the SLS. This model was then used to simulate flaw sizes that were originally omitted from the original POD study. The results of this new MAPOD study will be presented along with a discussion of the methods and processes used to analyze the original data, selected simulation parameters, and development of the uncertainty model used for the statistical analysis. The goal of this effort is not just to improve the POD study but to demonstrate the value of MAPOD and provide a roadmap for application of MAPOD on future projects.
The NASA Computational Aerosciences Program - Toward teraFLOPS computing
The Computational Aerosciences (CAS) project of the High Performance Computing and Communications program is presented and discussed. The main emphasis of this presentation is on the applications portion of the CAS program, which includes a High-Speed Civil Transport element, a High Performance Aircraft element, a NASP-Derived Vehicle element, and an Aerobraking element. Two major thrusts of this program are the enhancement of simulation capabilities using multidiscipline formulations and the improvement in processing efficiency via massive parallel computer hardware. Current activities in these two areas at Ames, Langley and Lewis, are presented and discussed.
BeeSwarm: Enabling Parallel Scaling Performance Measurement in Continuous Integration for HPC Applications
Testing is one of the most important steps in software development–it ensures the quality of software. Continuous Integration (CI) is a widely used testing standard that can report software quality to the developer in a timely manner during development progress. Performance, especially scalability, is another key factor for High Performance Computing (HPC) applications. There are many existing profiling and performance tools for HPC applications, but none of these are integrated into CI tools. In this work, we propose BeeSwarm, an HPC container based parallel scaling performance system that can be easily applied to the current CI test environments. BeeSwarm is mainly designed for HPC application developers who need to monitor how their applications can scale on different compute resources. We demonstrate BeeSwarm using a multi-physics HPC application with Travis CI, GitLab CI and GitHub Actions while using ChameleonCloud and Google Compute Engine as the compute backends. Finally, our results show that BeeSwarm can be used for scalability and performance testing of HPC applications.
Compilation of Abstracts for SC12 Conference Proceedings
1 A Breakthrough in Rotorcraft Prediction Accuracy Using Detached Eddy Simulation; 2 Adjoint-Based Design for Complex Aerospace Configurations; 3 Simulating Hypersonic Turbulent Combustion for Future Aircraft; 4 From a Roar to a Whisper: Making Modern Aircraft Quieter; 5 Modeling of Extended Formation Flight on High-Performance Computers; 6 Supersonic Retropropulsion for Mars Entry; 7 Validating Water Spray Simulation Models for the SLS Launch Environment; 8 Simulating Moving Valves for Space Launch System Liquid Engines; 9 Innovative Simulations for Modeling the SLS Solid Rocket Booster Ignition; 10 Solid Rocket Booster Ignition Overpressure Simulations for the Space Launch System; 11 CFD Simulations to Support the Next Generation of Launch Pads; 12 Modeling and Simulation Support for NASA's Next-Generation Space Launch System; 13 Simulating Planetary Entry Environments for Space Exploration Vehicles; 14 NASA Center for Climate Simulation Highlights; 15 Ultrascale Climate Data Visualization and Analysis; 16 NASA Climate Simulations and Observations for the IPCC and Beyond; 17 Next-Generation Climate Data Services: MERRA Analytics; 18 Recent Advances in High-Resolution Global Atmospheric Modeling; 19 Causes and Consequences of Turbulence in the Earths Protective Shield; 20 NASA Earth Exchange (NEX): A Collaborative Supercomputing Platform; 21 Powering Deep Space Missions: Thermoelectric Properties of Complex Materials; 22 Meeting NASA's High-End Computing Goals Through Innovation; 23 Continuous Enhancements to the Pleiades Supercomputer for Maximum Uptime; 24 Live Demonstrations of 100-Gbps File Transfers Across LANs and WANs; 25 Untangling the Computing Landscape for Climate Simulations; 26 Simulating Galaxies and the Universe; 27 The Mysterious Origin of Stellar Masses; 28 Hot-Plasma Geysers on the Sun; 29 Turbulent Life of Kepler Stars; 30 Modeling Weather on the Sun; 31 Weather on Mars: The Meteorology of Gale Crater; 32 Enhancing Performance of NASAs High-End Computing Applications; 33 Designing Curiosity's Perfect Landing on Mars; 34 The Search Continues: Kepler's Quest for Habitable Earth-Sized Planets.