NASA's Contributions to Information Technology - Intelligent Systems ...

NASA's Contributions to Information Technology: A View From Ames Research Center Michael G. Shafto and David J. Korsmeyer Intelligent Systems Division, NASA-Ames Research Center

Abstract Building on its expertise in wind-tunnels and other facilities for experimental aerodynamics, NASA-Ames Research Center established a leadership position in supercomputing and computational fluid dynamics during the 1970s. During the 1980s, Ames pioneered networking and telepresence research. In the late 1990s, Ames became the Center of Excellence for Information Technology within NASA. Today Ames is a leader in human-computer interaction, information management, intelligent systems, supercomputing, and computational solutions to problems in aerospace engineering and science. This article touches on some historical highlights, but focuses primarily on recent contributions in supercomputing, modeling, and simulation; nextgeneration air-traffic management; intelligent systems; and complex data analysis. KEYWORDS: C.1 Processor Architectures: C.1.4 Parallel Architectures; H.2.8.d Data mining; J.2 Physical Sciences and Engineering: J.2.a Aerospace; J.6 Computer-Aided Engineering; J.7.a Command and control

Ames Research Center was named after Joseph S. Ames, chair of the National Advisory Committee for Aeronautics (NACA) when the center was first proposed. Vannevar Bush, who succeeded Joseph Ames as NACA Chair, approved the current site for the new center. Bush later wrote one of the most visionary papers ever published on the future of Information Technology (IT), As We May Think.1 Bush’s paper had a profound influence on a young engineer working at Ames in the late 1940s, Douglas Engelbart, who went on to win the Turing Award for his invention of the mouse, multiple on-screen windows, groupware, linked hypermedia, and on-line publishing.

Early Information Technology at Ames When NASA was created in 1958, computers consisted of card-readers, tape-drives, line-printers, and large boxes of electronics. They were expensive and unreliable, requiring continual attention from technicians. NASA’s needs were (and are) on the forefront of computer technology regarding space-qualified performance, robustness, reliability, and miniaturization. Although NASA had computing requirements beyond the commercial sector, the Agency's flight and ground computing systems often lagged behind the state-of-the-art. Flight programs did not aim systematically to improve the technology of computing. Rather, NASA's contributions to the development of IT have been side-effects of its specific requirements for deterministic real-time performance, ultra-high reliability, distributed processing, repeatable software engineering processes, and high-fidelity modeling and simulation.2 For example, Computational Fluid Dynamics (CFD) was well-established at Ames by the 1970s, due to the pioneering work of Lomax, Ballhaus, and others.3 Ames became a leader in high-end computing for CFD when Hans Mark, as Ames Center Director, brought the ILLIAC IV to the Center in the early 1970s. The ILLIAC was experimental and far ahead of its time. It had 64-bit registers, 64 processors, and was designed to perform at 1 GFLOPS. Then as now, the actual performance of the parallel architecture depended upon the structure of the problem. If the problem could not be parallelized in appropriate fashion, the ILLIAC would be slower than contemporary CDC machines.

New parallelized versions of FORTRAN were designed to load arrays across processors and to unwind loops automatically into array operations.4 On some problems the ILLIAC out-performed the CDC 7600, though by less than an order of magnitude. Its performance was tuned for programs running the same operation on large, detailed datasets, which is what CFD is all about. Experience has shown the ILLIAC’s massively parallel approach to be a good one for almost all scientific computing, although that was far from apparent in the 1970s.5 By the 1980s, loosely coupled networks of mini-computers supported spaceflight operations, flight-computing was a well-established discipline, and NASA had a strong presence on the emerging Internet. Ames played a central role in advanced networking,6 as well as in telepresence.7 In 1983, Peter Denning founded the Research Institute for Advanced Computer Science (RIACS) at Ames, and many projects since then have been collaborations between Ames and RIACS.

Recent Contributions of Ames to Information Technology Starting in the late 1990s, Ames became the Center of Excellence for Information Technology within NASA. Today Ames is a leader in human-computer interaction, information management, intelligent systems, supercomputing, and computational solutions to many problems in aerospace engineering and science. Starting with its expertise in experimental aerodynamics, including sophisticated wind tunnels and research aircraft, Ames has pioneered physical modeling, high-fidelity simulation, artificial intelligence, and air-traffic management research. The most recent contributions of Ames to IT fall into four categories:  supercomputing, modeling, and simulation for science and engineering applications  automation of next-generation air-traffic management  intelligent systems  complex data analysis We present several representative examples of recent work at Ames in each of these categories. A number of publications over the past decade have presented summaries of work by Ames and its collaborators from different perspectives and using various sets of examples.8-10 Supercomputing, Modeling, and Simulation Supercomputing. The rapidly increasing number of processors in today’s computers is driven by data-processing challenges approaching the petascale and exascale regimes. Ames is now operating one of the ten fastest computers in the world, targeted towards NASA’s modeling and simulation requirements in science and engineering. The heterogeneous nature of the most advanced supercomputers, including a complex range of options for distributed memory, shared memory, and non-uniform memory access within each node, poses programmability and portability challenges to software developers. Ames conducts research on supercomputer performance, using complex design, engineering, and science applications, as well as standard benchmarks from the NASA Advanced Supercomputing Parallel Benchmarks. A number of multi-core and many-core systems have been designed and analyzed. Extensions to advanced programming languages have been developed to improve programmability and portability, and to match the memory structures of emerging many-core architectures.11 Cart3D. Cart3D is a high-fidelity geometry-processing and flow-analysis package that supports the early phases of aerodynamic design. Its approach to space discretization has had a significant impact on complex simulation in aerospace, astrophysics, computer science, and electromagnetics. It has been licensed for use in aerospace, electronics/electromagnetics, automotive, turbomachinery, and industrial process applications. Recently developed tools make it possible to compute thousands of Euler and Navier-Stokes solutions for new aerospace vehicles in one week. Automation and advanced user-interface design can reduce user workload, and rapid turn-around enables more extensive validation with experimental data. Flow visualization and three-dimensional surface plots enable better characterization of computed flow fields.12 DPLR. The Data-Parallel Line Relaxation (DPLR) Code is used to understand the extreme environments experienced by spacecraft during entry, descent, and landing in planetary atmospheres, including, as a major

example, the Earth’s atmosphere. Credible simulated environments are required because thermal protection systems cannot be tested under realistic conditions on Earth. The physical modeling innovations of the DPLR software have the potential to enhance model-based engineering of future systems. DPLR has been transferred to government, industry, and university researchers and has been used for projects of the Department of Defense, the Department of Energy, and civil aerospace. DPLR-derived tools are being developed within universities and industry. Other uses of DPLR include analysis of the breakup of de-orbiting debris and of missile plumesignatures. The generalized chemical kinetics and transport property packages in DPLR make it potentially valuable for the simulation of combustion flows for a wide variety of applications.13

Next-Generation Air Traffic Management CTAS. The Center Terminal Radar Approach Control Automation System (CTAS) is a set of three software tools for managing air traffic control systems at major airports: Traffic Management Advisor, Final Approach Spacing Tool, and Conflict Predictor Trial Planner. It was designed to optimize flight operations in civil aviation. It has been operationally tested at Dallas/Ft. Worth International Airport, integrated into the existing radar system with displays in the control room and supplementing the manual air traffic control system. CTAS uses four-dimensional trajectory synthesis algorithms for aircraft path prediction, providing time-based arrival traffic flow visualization, strategic planning based on aircraft separation, and tactical en-route controller advisories for precision metering. Numerous other tools and techniques for next-generation air traffic management have been inspired by or derived from CTAS.14 FACET. The Future Air Traffic Management Concepts Evaluation Tool (FACET) is a simulation environment for exploration, development, and evaluation of advanced air traffic management concepts. Examples include aircraft self-separation for Free Flight, modeling and prediction of air traffic controller workload, a decision support tool for direct routing, integration of commercial space launch vehicle operations into the U.S. National Airspace System, and advanced traffic flow management techniques using rerouting, metering, and ground delay. FACET can model system-wide airspace operations over the contiguous U.S. Airspace. FACET’s component models include center/sector boundaries, airways, and locations of navigation aids and airports. Weather models include winds, temperature, and bad weather cells. Simulated aircraft can be flown along either flight plan routes or direct routes as they climb, cruise, and descend according to their individual aircraft-type performance models. The FAA is considering the use of this technology for optimally rerouting aircraft during system upsets.15

Intelligent Systems Remote Agent. The Remote Agent software system operated NASA's Deep Space 1 spacecraft and its first-in-class ion engine during two experiments that started on Monday, May 17, 1999. For two days Remote Agent ran on the on-board computer of Deep Space 1, more than 60,000,000 miles (96,560,640 km) from Earth. The tests were a step toward robotic explorers of the 21st century that are increasingly affordable, capable, and independent from ground control. Remote Agent demonstrated numerous autonomy concepts including high-level goal-oriented commanding, on-board planning, robust plan execution, and model-based fault protection. It was the first on-board autonomous operation of a spacecraft using artificial intelligence software, and it set a standard by which subsequent autonomous systems in space are measured.16 Europa/SPIFe. The Extensible Universal Remote Operations Planning Architecture and the Scheduling and Planning Interface for Exploration have been used for a variety of different mission planning and scheduling applications, as well as for advanced technology demonstrations in mission planning and control of robots and spacecraft. The integrated package can efficiently manage scheduled and unscheduled activities, replanning under numerous constraints, ground rules, and flight rules. It covers both crew activities and the coordination of activities between crew and flight controllers on the ground. Unanticipated changes can range from trivial (move activities) to moderate (add constraints, insert activities, delete activities) to sophisticated (change the resources an activity uses). This technology was used on the Mars Exploration Rovers mission, the Mars Phoenix Lander mission, and will be used on the Mars Science Laboratory mission.17

Brahms/OCAMS. The Orbital Communications Adapter Monitoring System (OCAMS) has been used 24x7 by the OCA Flight Control Team in the International Space Station (ISS) Mission Control Center since July 8, 2008. The ISS OCA Officer is responsible for uplinking and downlinking all files to and from the ISS, and for documenting all such file transfers. These files include schedules, procedures, commands, email, photographs, health data, newspapers, etc. OCAMS was the first application of intelligent multi-agent system technology in NASA’s human spaceflight mission control operations. It was developed using the Brahms multi-agent software design and development tools. Brahms enables a “from simulation to implementation” software engineering methodology, in which a multi-agent simulation of people’s work practice is turned into a multi-agent workflow system that automates part of the process and integrates seamlessly with existing work practices. Using the Brahms virtual machine, OCAMS agents manage the workflow on multiple computers and servers using secure communications. The tool also automatically writes large parts of the OCA handover log, thereby greatly reducing the amount of effort required from the flight controllers.18 Complex Data Analysis Aviation Safety. Researchers at Ames have developed advanced algorithms for extracting information from large heterogeneous and distributed databases and integrating information from domain experts to provide reliable and operationally significant information. The Aviation Safety program’s data mining project works closely with the FAA and airlines to transition technology to the field. The project develops advanced data-mining technologies for larger and more complex data streams from millions of flights to identify precursors to aviation incidents or accidents, potentially benefiting airline passengers world-wide.19 IMS. Model-based diagnostic reasoning uses the history of system parameter values as input to a simulation and determines if a particular set of observed values is consistent with that simulation. When the values are not consistent, a conflict occurs, indicating that the system operation is off-nominal. Building models for model-based reasoning is a difficult and time-consuming process. The Inductive Monitoring System (IMS) provides a method that can monitor the health of a system without the need to build a model manually. IMS automatically defines groups of consistent system parameter data by generalizing from examples of nominal system data. With a sufficiently broad training data set, the knowledge base produced by IMS will contain most of the consistent parameter value combinations that characterize nominal performance. After learning how the system behaves when operating correctly, IMS can identify off-nominal behavior and send appropriate alert messages to system operators.20 World Wind. World Wind (WW), is an open source 3D interactive viewer that was created by Ames and released in mid-2004. It allows any user to zoom from outer space to any place on Earth, using satellite imagery and elevation data to experience terrain in visually rich 3D. WW is maintained by NASA staff and open-source community developers. Government and international agencies have adopted WW because it is open source and extensible at the source code level. The modular architecture improves the ability to advance the overall technology, due to the increased ability to optimize individual components. WW uses the Open Geospatial Consortium Web Mapping Service, to which NASA is a signatory, along with the Department of Homeland Security, National Oceanic and Atmospheric Administration, National Geospatial-Intelligence Agency, U.S. Environmental Protection Agency, U.S. Geological Survey, and others.21 Kepler Science Pipeline. The Kepler mission is the first NASA mission capable of discovering hundreds of Earthlike planets around a wide variety of stars. Technology based on transit photometry can find Earth-like planets that are several hundred times less massive than Jupiter-like planets. The Kepler Science Operations Center is responsible for several aspects of the mission, including managing targets, generating onboard data compression tables, monitoring photometer health and status, processing science data, and exporting Science Processing Pipeline products to the Multi-mission Archive at Space Telescope. The pipeline framework software is used to achieve these goals, including development of pipeline configurations for processing science data, performance of other support roles, and creation of custom unit-of-work generators for controlling how data are partitioned and distributed across the computing cluster. It includes an interface between the Java software that manages data retrieval and storage for a given unit of work and the MATLAB algorithms that process the data. The data for each unit of work are packaged into a single file that contains everything needed by the science algorithms, allowing the files to be used to debug and evolve the algorithms offline.22

Conclusion NASA-Ames Research Center has long been at the forefront of advanced Information Technology. Though NASA is most generally known for its launch vehicles, its experimental aircraft, and its space science discoveries, IT and software systems are at the core of enabling all NASA missions. NASA is pushing the envelope for state-of-the-art IT systems: computing the fundamental physics of fluids to improve our understanding of flight; understanding and modeling the national airspace to enable safer, more efficient air travel that is critical for the U.S. economy; creating intelligent systems to enhance robotic and human performance to enable more capable and affordable space exploration; and finally, providing new tools and technologies to mine numeric and textual data, and to tease out Earth-like planets from massive, noisy data sets. Each of these efforts is a uniquely “NASA” mission, and the spin-offs from those tools and technologies are continuing to engage the best and brightest minds in IT and software research today, just as they have in the past.

Acknowledgment The authors thank Eugene Miya for valuable insights, references, and corrections regarding historical information.

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Authors’ bios David J. Korsmeyer is the Division Chief for Intelligent Systems at NASA Ames Research Center. He has held prior positions including Chief Architect for Science and Engineering Infrastructure, Senior Project Scientist in the Computational Sciences Division, Manager for Analytical Tools and Design Environments in NASA’s Information Technology Base Program, and development lead for the DARWIN distributed design environment. He received his B.S. from the Pennsylvania State University, and his M.S. and Ph.D. from the University of Texas at Austin. He is a member of AIAA, IEEE, AAAI, and IAF.

Michael G. Shafto is the Deputy Chief Technologist at NASA Ames Research Center and the road-mapping lead for Modeling, Simulation, Information Technology and Processing in NASA’s Office of the Chief Technologist. His prior positions included Technical Area Lead for Collaborative and Assistant Systems in the Intelligent Systems Division, Project Manager for Human-Automation Integration in the Aviation Safety Research Program, and Manager for Software, Intelligent Systems, and Modeling in the Exploration Systems R&T Program. He received his A.B. from Carleton College, and his M.A. and Ph.D. from Princeton University. He is a member of ACM and the Cognitive Science Society.

Author contact information: Dr. David J. Korsmeyer Mail Stop 269-1 NASA-Ames Research Center Moffett Field, CA 94035-0001 (650) 604-3114 (voice) (650) 604-6009 (fax) [email protected] Dr. Michael G. Shafto Mail Stop 269-4 NASA-Ames Research Center Moffett Field, CA 94035-0001 (650) 604-6170 (voice) (650) 604-6009 (fax) [email protected]