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NASA NTRS2017

Data Production on Past and Future NASA Missions

Data return is a metric that is commonly publicized for all space science missions. In the early days of the Space Program, this figure was small, and could be described in bits or maybe even megabits. But now, missions are capable of returning data volumes two or three orders of magnitude larger. For example, Voyager 1 and 2 combined produced a little over 5 Terabits of data in 39 years of operation. In contrast, the Cassini mission, launched two decades after Voyager, produced about one and a half times those data volumes in half the time. NISAR, an Earth Science Mission currently in implementation, plans to produce over 28 Petabits of raw data in just 3 years. This means that NISAR will produce about as many data in 30 days as the combined data production of nearly all planetary missions to date. These increases in capability are a result of technology enhancements in two main areas: telecommunications architecture (both space and ground segments) and data storage technology. This paper describes the progression of these two technologies over the course of more than three decades of space missions and provides additional insight into the design of the end-to-end NISAR Data System Architecture. Trends in the data are briefly explored and compared to Moore’s Law which provides only a qualitative model for memory growth but not for data production. In summary, early missions are found to be driven by unrefined processes while later missions, having utilized earlier lessons learned, focus more on improvements to flight and ground capabilities. Data return seems to fall into three categories. First, deep space missions are driven by the large distances that limit data return to the Earth. Next, the orbiter infrastructure around Mars helps these missions generate more data than other deep space spacecraft. Finally, near-Earth missions have the greatest capabilities for the studied metrics due to their close proximity to Earth and the ground network availability.

Xaypraseuth, Peter↗

NASA NTRS1992

Architectures for mission control at the Jet Propulsion Laboratory

JPL is currently converting to an innovative control center data system which is a distributed, open architecture for telemetry delivery and which is enabling advancement towards improved automation and operability, as well as new technology, in mission operations at JPL. The scope of mission control within mission operations is examined. The concepts of a mission control center and how operability can affect the design of a control center data system are discussed. Examples of JPL's mission control architecture, data system development, and prototype efforts at the JPL Operations Engineering Laboratory are provided. Strategies for the future of mission control architectures are outlined.

Davidson, Reger A.↗

NASA NTRS1987

Computer architecture for efficient algorithmic executions in real-time systems: New technology for avionics systems and advanced space vehicles

Improvements and advances in the development of computer architecture now provide innovative technology for the recasting of traditional sequential solutions into high-performance, low-cost, parallel system to increase system performance. Research conducted in development of specialized computer architecture for the algorithmic execution of an avionics system, guidance and control problem in real time is described. A comprehensive treatment of both the hardware and software structures of a customized computer which performs real-time computation of guidance commands with updated estimates of target motion and time-to-go is presented. An optimal, real-time allocation algorithm was developed which maps the algorithmic tasks onto the processing elements. This allocation is based on the critical path analysis. The final stage is the design and development of the hardware structures suitable for the efficient execution of the allocated task graph. The processing element is designed for rapid execution of the allocated tasks. Fault tolerance is a key feature of the overall architecture. Parallel numerical integration techniques, tasks definitions, and allocation algorithms are discussed. The parallel implementation is analytically verified and the experimental results are presented. The design of the data-driven computer architecture, customized for the execution of the particular algorithm, is discussed.

Carroll, Chester C.↗

NASA NTRS1999

Strategies and Technologies for In Situ Mineralogical Investigations on Mars

Surface landers on Mars (Viking and Pathfinder) have not revealed satisfying answers to the mineralogy and lithology of the planet's surface. In part, this results from their prime directives: Viking focused on exobiology, Pathfinder focused on technology demonstration. The analytical instruments on board the landers made admirable attempts to extract the mineralogy and geology of Mars, as did countless modeling efforts after the missions. Here we suggest a framework for elucidating martian, or any other planetary geology, through an approach that defines (a) type of information required, (b) explorational strategy harmonious with acquisition of these data, (c) interpretation approach to the data, (d) compatible mission architecture, (e) instrumentation for interrogating rocks and soil. (a) Data required: The composition of a planet is ordered at scales ranging from molecules to minerals to rocks, and from geological units to provinces to planetary-scale systems. The largest ordering that in situ compositional instruments can attempt to interrogate is rock type "aggregate" information. This is what the geologist attempts to identify first. From this, mineralogy can be either directly seen or inferred. From mineralogy can be determined elemental abundances and perhaps the state of the compounds as being crystalline or amorphous. Knowledge of rock type and mineralogy is critical for elucidating geologic process. Mars landers acquired extremely valuable elemental data, but attempted to move from elements to aggregates, but this can only be done by making many assumptions and sometimes giant leaps of faith. Data we believe essential are elements, minerals, degree of ordering of compounds, and the aggregate or rock type that these materials compose. (b) Explorational strategy: A lander should function as a surrogate geologist. Of the total landscape, a geologist sees much, but gives detailed attention to an infinitesimally small amount of what is seen. To acquire samples worth detailed scrutiny, as many samples as possible need examining at a cursory or reconnaissance level. A representative, statistically-meaningful sample number cannot be overemphasized. This maxim still applies to geological exploration of our own planet of which we have abundant knowledge. Analysis of many samples mandates low-power consumption per sample. (c) Data interpretation: No single instrument can analyze the full spectrum of the x-axis. An instrument is optimized for detecting certain material characteristics and must therefore affix itself to some point on the x-axis. Any conclusions drawn about data to the left or right of the instrument's position on this axis must necessarily be derived by inference. Hence, it seems logical to include on a mission, instruments that are not closely spaced in their x-axis-position, and if only two analytical methods are used, as shown, they should start at opposite ends of the axis and work towards the center. As examples, we depict a high-resolution camera to evaluate rock type ("aggregate" state) and mineralogy, and an x-ray diffractometer-fluorescence spectrometer (XRD-XRF) to determine elements, minerals, and the degree of order of materials. (d) Mission architecture: No instrument or suite of instruments can be relied upon to always give truly unequivocal analyses. The suite of instruments should therefore permit conclusions of one instrument to be checked against those of another through closed analytical loops. These "loops" can be structured by a combination of orbital imagery, descent imagery, broad-band site viewing/analysis, and data that cover both x and y axes. For example, the detection of a basaltic-looking rock with a microscope should be checked against the elements detected, the appearance of the rock as a lava flow from descent imagery, and so forth. (e) Instrumentation: To satisfy the above criteria, it is necessary to: (i) See the rock or soil with high resolution + magnification, (ii) Examine many samples, (iii) Consume little power per analysis, (iv) Determine elemental species, (v) Determine mineralogy directly (not inferentially) and the degree of ordering of compounds, (vi) Start analyzing from both ends of the x-axis. Every geologist wants to see the hand sample first, and apply a hand lens to its surface. This has not been the starting point for missions to Mars. Thus, our technology satisfies all these criteria . This XRD-XRF-Optical instrument currently being developed, analyses rock or soil surfaces without the need for sample acquisition or preparation; this satisfies the power criterion, and enables many analyses. The device acquires direct mineralogy and determines elemental species. The embedded endoscopic camera satisfies the critical criterion of close inspection of samples; the fiber optic cable can also be used for IR, LTV, or laser sample analysis. Additional information is contained in the original (Figures).

Marshall, J. R.↗

DOE OSTI2013

IMG/M 4 version of the integrated metagenome comparative analysis system

IMG/M (http://img.jgi.doe.gov/m) provides support for comparative analysis of microbial community aggregate genomes (metagenomes) in the context of a comprehensive set of reference genomes from all three domains of life, as well as plasmids, viruses and genome fragments. IMG/M’s data content and analytical tools have expanded continuously since its first version was released in 2007. Since the last report published in the 2012 NAR Database Issue, IMG/M’s database architecture, annotation and data integration pipelines and analysis tools have been extended to cope with the rapid growth in the number and size of metagenome data sets handled by the system. IMG/M data marts provide support for the analysis of publicly available genomes, expert review of metagenome annotations (IMG/M ER: http://img.jgi.doe.gov/mer) and Human Microbiome Project (HMP)-specific metagenome samples (IMG/M HMP: http://img.jgi.doe.gov/ imgm_hmp).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Tiling Framework for Heterogeneous Computing of Matrix based Tiled Algorithms

Tiling matrix operations can improve the load balancing and performance of applications on heterogeneous computing resources. Writing a tile-based algorithm for each operation with a traditional, hand-tuned tiling approach that uses for loops in C/C++ is cumbersome and error prone. Moreover, it must enable and support the heterogeneous memory management of data objects and also explore architecture-supported, native, tiled-data transfer APIs instead of copying the tiled data to continuous memory before the data transfer. The tiling framework provides a tiled data structure for heterogeneous memory mapping and parameterization to a heterogeneous task specification API. We have integrated our tiled framework into MatRIS (Math kernels library using IRIS). IRIS is a heterogeneous run-time framework with a heterogeneous programming model, memory model, and task execution model. Experiments reveal that the tiled framework for BLAS operations has improved the programmability of tiled BLAS and improved performance by ~20% when compared against the traditional method that copies the data to continuous memory locations for heterogeneous computing.

Miniskar, Narasinga Rao↗

DOE OSTI2020

Mapped Moments to a Cartesian Grid (MMCG) Value-Added Product Report

Objective analysis (OA) is a method of mapping unstructured data to a structured grid. In the context of scanning radar data, OA is used to interpolate data in antenna coordinates (range, azimuth, and elevation) onto a regularly spaced Cartesian grid (Trapp and Doswell 2000). The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s Mapped Moments to a Cartesian Grid (MMCG) Value-Added Product (VAP) uses the Python ARM Radar Toolkit (Py-ART), a data model-driven interactive architecture for working with weather radar data, to map the data to a Cartesian grid (Helmus and Collis 2016). MMCG, with Py-ART built in, has the ability to take radar data in antenna coordinates and map the gates to a Cartesian grid using inverse distance weight functions such as Cressman (square) and Barnes (exponential), but also can filter the data during the interpolation. MMCG also allows arbitrary formulations for the radius of influence, which are matched to particular radar scanning strategies. This creates a complex parameter space for optimizing the retention of storm structure detail while minimizing artifacts. MMCG takes data processed with ARM’s Corrected Precipitation Radar Moments in Antenna Coordinates (CMAC) VAP and maps it to a Cartesian grid as the output product. A variety of fields that have been mapped to the Cartesian grid are then saved to plots to complement each grid file.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Fixing Amdahl's Law within the Limits of Accelerated Systems: FALLACY

Closeout report for FALLACY project. The performance of Data Model Convergence Initiative (DMC) applications on parallel machines is far below the limit set by Amdahl’s law. Whether the machine is based on many-core, GPUs, FPGAs, or a heterogeneous combination, usually the most significant bottleneck is accessing data from the memory system. Aligning with DMC’s HW/architecture thrust, this project developed a set of memory-centric tools called ‘MemGaze’ that inform the HW/SW stack about an application’s memory behavior, including data access latency and diagnosing poor data layout and data composition. Our approach uses architectural modeling and analysis of workload data accesses.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

My vehicle is a data mine

In this talk we explore how analysis of vehicle data provides information of individual vehicle behaviors, information of other vehicles in the flow of traffic, and insights into the behavior of drivers. Over the last two decades traditional passenger vehicles have been transformed from integrated two-port electrical nodes to cyber-physical systems of communicating computational nodes whose individual state and control variables are shared on a standard controller area network (CAN) bus. As driver assistance systems have crept into vehicles as safety features, driver behaviors can be observed through analysis of the data streams on the CAN bus as these new nodes communicate with one another. The properties of these data streams, as well as architectures and approaches to gather the data, are important to consider when drawing conclusions on the relevance of the data in making decisions at varying levels of the information hierarchy. We will demonstrate several technical challenges associated with these data collection processes, as well as preliminary results that demonstrate application relevance of the data to behavior, traffic, and systems domains.

42 ENGINEERING↗

DOE OSTI2015

Gregarious Data Re-structuring in a Many Core Architecture

this paper, we have developed a new methodology that takes in consideration the access patterns from a single parallel actor (e.g. a thread), as well as, the access patterns of “grouped” parallel actors that share a resource (e.g. a distributed Level 3 cache). We start with a hierarchical tile code for our target machine and apply a series of transformations at the tile level to improve data residence in a given memory hierarchy level. The contribution of this paper includes (a) collaborative data restructuring for group reuse and (b) low overhead transformation technique to improve access pattern and bring closely connected data elements together. Preliminary results in a many core architecture, Tilera TileGX, shows promising improvements over optimized OpenMP code (up to 31% increase in GFLOPS) and over our own previous work on fine grained runtimes (up to 16%) for selected kernels

data restructuring↗

NASA NTRS1979

ATLAS, an integrated structural analysis and design system. Volume 2: System design document

ATLAS is a structural analysis and design system, operational on the Control Data Corporation 6600/CYBER computers. The overall system design, the design of the individual program modules, and the routines in the ATLAS system library are described. The overall design is discussed in terms of system architecture, executive function, data base structure, user program interfaces and operational procedures. The program module sections include detailed code description, common block usage and random access file usage. The description of the ATLAS program library includes all information needed to use these general purpose routines.

Erickson, W. J.↗

DOE OSTI2021

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗

NASA NTRS2001

Telemetry Tracking & Control (TT&C) - First TDRSS, then Commercial GEO & Big LEO and Now Through LEO

The advent of low earth orbit (LEO) commercial communication satellites provides an opportunity to dramatically reduce Telemetry, Tracking and Control (TT&C) costs of launch vehicles, Unpiloted Aerial Vehicles (UAVs), Research Balloons and spacecraft by reducing or eliminating ground infrastructure. Personnel from the Goddard Space Flight Center's Wallops Flight Facility (GSFC\WFF) have successfully used commercial Geostationary Earth Orbit (GEO) and Big LEO communications satellites for Long Duration Balloon Flight TT&C. The Flight Modem is a GSFC\WFF Advanced Range Technology initiative (ARTI) designed to streamline TT&C capability in the user community of these scientific data gathering platforms at low cost. Making use of existing LEO satellites and adapting and ruggedized commercially available components; two-way, over the horizon communications may be established with these vehicles at great savings due to reduced infrastructure. Initially planned as a means for permitting GPS data for tracking and recovery of sounding rocket and balloon payloads, expectations are that the bandwidth can soon be expanded to allow more comprehensive data transfer. The system architecture which integrates antennas, GPS receiver, commercial satellite packet data modem and a single board computer with custom software is described and technical challenges are discussed along with the plan for their resolution. A three-phase testing and development plan is outlined and the current results are reported. Results and status of ongoing flight tests on aircraft and sounding rockets are reported. Future applications on these platforms and the potential for satellite support are discussed along with an analysis of cost effectiveness of this method vs. other tracking and data transmission schemes.

Morgan, Dwayne R.↗

NASA NTRS2012

Exploration Medical System Demonstration (EMSD) Project

The Exploration Medical System Demonstration (EMSD) is a project under the Exploration Medical Capability (ExMC) element managed by the Human Research Program (HRP). The vision for the EMSD is to utilize ISS as a test bed to show that several medical technologies needed for an exploration mission and medical informatics tools for managing evidence and decision making can be integrated into a single system and used by the on-orbit crew in an efficient and meaningful manner. Objectives: a) Reduce and even possibly eliminate the time required for on-orbit crew and ground personnel (which include Surgeon, Biomedical Engineer (BME) Flight Controller, and Medical Operations Data Specialist) to access and move medical data from one application to another. b) Demonstrate that the on-orbit crew has the ability to access medical data/information using an intuitive and crew-friendly software solution to assist/aid in the treatment of a medical condition. c) Develop a common data management framework and architecture that can be ubiquitously used to automate repetitive data collection, management, and communications tasks for all crew health and life sciences activities.

Chin, Duane↗

NASA NTRS1989

The Environment-Power System Analysis Tool development program

The Environment Power System Analysis Tool (EPSAT) is being developed to provide engineers with the ability to assess the effects of a broad range of environmental interactions on space power systems. A unique user-interface-data-dictionary code architecture oversees a collection of existing and future environmental modeling codes (e.g., neutral density) and physical interaction models (e.g., sheath ionization). The user-interface presents the engineer with tables, graphs, and plots which, under supervision of the data dictionary, are automatically updated in response to parameter change. EPSAT thus provides the engineer with a comprehensive and responsive environmental assessment tool and the scientist with a framework into which new environmental or physical models can be easily incorporated.

Jongeward, Gary A.↗

NASA NTRS1992

VETA x ray data acquisition and control system

We describe the X-ray Data Acquisition and Control System (XDACS) used together with the X-ray Detection System (XDS) to characterize the X-ray image during testing of the AXAF P1/H1 mirror pair at the MSFC X-ray Calibration Facility. A variety of X-ray data were acquired, analyzed and archived during the testing including: mirror alignment, encircled energy, effective area, point spread function, system housekeeping and proportional counter window uniformity data. The system architecture is presented with emphasis placed on key features that include a layered UNIX tool approach, dedicated subsystem controllers, real-time X-window displays, flexibility in combining tools, network connectivity and system extensibility. The VETA test data archive is also described.

Brissenden, Roger J. V.↗

NASA NTRS1993

VETA X-ray Data Acquisition and Control System

Brissenden, Roger J. V.↗

NASA NTRS2009

NASA's Earth Observing Data and Information System

NASA's Earth Observing System Data and Information System (EOSDIS) has been a central component of NASA Earth observation program for over 10 years. It is one of the largest civilian science information system in the US, performing ingest, archive and distribution of over 3 terabytes of data per day much of which is from NASA s flagship missions Terra, Aqua and Aura. The system supports a variety of science disciplines including polar processes, land cover change, radiation budget, and most especially global climate change. The EOSDIS data centers, collocated with centers of science discipline expertise, archive and distribute standard data products produced by science investigator-led processing systems. Key to the success of EOSDIS is the concept of core versus community requirements. EOSDIS supports a core set of services to meet specific NASA needs and relies on community-developed services to meet specific user needs. EOSDIS offers a metadata registry, ECHO (Earth Observing System Clearinghouse), through which the scientific community can easily discover and exchange NASA s Earth science data and services. Users can search, manage, and access the contents of ECHO s registries (data and services) through user-developed and community-tailored interfaces or clients. The ECHO framework has become the primary access point for cross-Data Center search-and-order of EOSDIS and other Earth Science data holdings archived at the EOSDIS data centers. ECHO s Warehouse Inventory Search Tool (WIST) is the primary web-based client for discovering and ordering cross-discipline data from the EOSDIS data centers. The architecture of the EOSDIS provides a platform for the publication, discovery, understanding and access to NASA s Earth Observation resources and allows for easy integration of new datasets. The EOSDIS also has developed several methods for incorporating socioeconomic data into its data collection. Over the years, we have developed several methods for determining needs of the user community including use of the American Customer Satisfaction Index and a broad metrics program.

Mitchell, Andrew E.↗