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NASA NTRS2019

Electra: A Modular-Based Expansion of NASA's Supercomputing Capability

NASA has increasingly relied on high-performance computing (HPC) re- sources for computational modeling, simulation, and data analysis to meet the science and engineering goals of its missions in space exploration, aeronautics, and Earth and space science. The NASA Advanced Supercomputing (NAS) Division at Ames Research Center in Silicon Valley, Calif., hosts NASA’s premier supercomputing resources, integral to achieving and enhancing the success of the agency’s missions. NAS provides a balanced environment, funded under the High-End Computing Capability (HECC) project, comprised of world-class supercomputers, including its flagship distributed-memory cluster, Pleiades; high-speed networking; and massive data storage facilities, along with multi-disciplinary support teams for user support, code porting and optimization, and large-scale data analysis and scientific visualization. However, as scientists have increased the fidelity of their simulations and engineers are conducting larger parameter-space studies, the requirements for supercomputing resources have been growing by leaps and bounds. With the facility housing the HECC systems reaching its power and cooling capacity, NAS undertook a prototype project to investigate an alternative approach for housing supercomputers. Modular supercomputing, or container-based computing, is an innovative concept for expanding NASA’s HPC capabilities. With modular supercomputing, additional containers—similar to portable storage pods—can be connected together as needed to accommodate the agency’s ever-increasing demand for computing resources. In addition, taking advantage of the local weather permits the use of cooling technologies that would additionally save energy and reduce annual water usage. The first stage of NASA’s Modular Supercomputing Facility (MSF) prototype, which resulted in a 1,000 square-foot module on a concrete pad with room for 16 compute racks, was completed in Fall 2016 and an SGI (now HPE) computer system, named Electra, was deployed there in early 2017. Cooling is performed via an evaporative system built into the module, and preliminary experience shows a Power Usage Effectiveness (PUE) measurement of 1.03. Electra achieved over a petaflop on the LINPACK benchmark, sufficient to rank number 96 on the November 2016 TOP500 list [14]. The system consists of 1,152 InfiniBand-connected Intel Xeon Broadwell-based nodes. Its users access their files on a facility-wide file system shared by all HECC compute assets via Mellanox MetroX InfiniBand extenders, which connect the Electra fabric to Lustre routers in the primary facility over fiber-optic links about 900 feet long. The MSF prototype has exceeded expectations and is serving as a blueprint for future expansions. In the remainder of this chapter, we detail how modular data center technology can be used to expand an existing compute resource. We begin by describing NASA’s requirements for supercomputing and how resources were provided prior to the integration of the Electra module-based system.

Biswas, Rupak↗
NASA NTRS1993

Use of high performance networks and supercomputers for real-time flight simulation

In order to meet the stringent time-critical requirements for real-time man-in-the-loop flight simulation, computer processing operations must be consistent in processing time and be completed in as short a time as possible. These operations include simulation mathematical model computation and data input/output to the simulators. In 1986, in response to increased demands for flight simulation performance, NASA's Langley Research Center (LaRC), working with the contractor, developed extensions to the Computer Automated Measurement and Control (CAMAC) technology which resulted in a factor of ten increase in the effective bandwidth and reduced latency of modules necessary for simulator communication. This technology extension is being used by more than 80 leading technological developers in the United States, Canada, and Europe. Included among the commercial applications are nuclear process control, power grid analysis, process monitoring, real-time simulation, and radar data acquisition. Personnel at LaRC are completing the development of the use of supercomputers for mathematical model computation to support real-time flight simulation. This includes the development of a real-time operating system and development of specialized software and hardware for the simulator network. This paper describes the data acquisition technology and the development of supercomputing for flight simulation.

Cleveland, Jeff I., II↗
NASA NTRS2017

Chasing Exoplanets

NASA's Kepler Mission was launched in March 2009 as NASA's first mission capable of finding Earth-size planets orbiting in the habitable zone of Sun-like stars, that range of distances for which liquid water would pool on the surface of a rocky planet. Kepler has discovered over 1000 planets and over 4600 candidates, many of them as small as the Earth. Today, Kepler's amazing success seems to be a fait accompli to those unfamiliar with her history. But twenty years ago, there were no planets known outside our solar system, and few people believed it was possible to detect tiny Earth-size planets orbiting other stars. Motivating NASA to select Kepler for launch required a confluence of the right detector technology, advances in signal processing and algorithms, and the power of supercomputing.

Kepler Mission↗
NASA NTRS2017

Prospecting for Habitable Worlds

NASAs Kepler Mission was launched in March 2009 as NASAs first mission capable of finding Earth-size planets orbiting in the habitable zone of Sun-like stars, that range of distances for which liquid water would pool on the surface of a rocky planet. Kepler has discovered over 2200 planets and over 2200 candidate planets, many of them as small as the Earth. Forty nine of these are less than twice the size of Earth and orbit in the habitable zone of their stars, all of which are cooler and significantly smaller than the Sun.Today, Keplers amazing success seems to be a fait accompli to those unfamiliar with her history. But twenty years ago, there were no planets known outside our solar system, and few people believed it was possible to detect tiny Earth-size planets orbiting other stars. Indeed, demonstrating that the science was feasible took four proposals to NASAs Discovery Program and extensive research and laboratory demonstrations. Motivating NASA to select Kepler for launch required a confluence of the right detector technology, advances in signal processing and algorithms, and the power of supercomputing. On August 23 2015 we reported the discovery of Kepler-452b, the first small, possibly rocky planet in the habitable zone of a G2 star very similar to our own. Kepler-452b orbits its star once every 385 days in an orbit just 5 larger than that of Earth. This discovery represents an important step towards finding and characterizing small habitable worlds orbiting Sun-like stars.

Jenkins, Jon M.↗
NASA NTRS2018

Prospecting for Habitable Planets

NASA's Kepler Mission was launched in March 2009 as NASA's first mission capable of finding Earth-size planets orbiting in the habitable zone of Sun-like stars, that range of distances for which liquid water would pool on the surface of a rocky planet. Kepler has discovered over 1000 planets and over 4600 candidates, many of them as small as the Earth. Today, Kepler's amazing success seems to be a fait accompli to those unfamiliar with her history. But twenty years ago, there were no planets known outside our solar system, and few people believed it was possible to detect tiny Earth-size planets orbiting other stars. Motivating NASA to select Kepler for launch required a confluence of the right detector technology, advances in signal processing and algorithms, and the power of supercomputing.

Kepler Mission↗
NASA NTRS1998

Software-implemented Fault Tolerance for Supercomputing in Space

The NASA Jet Propulsion Laboratory Remote Exploration and Experimentaion (REE) Project is a large multi-year technology demonstration project which will develop low-power, scalable, fault-tolerant, high- performance computing for use in space and will demonstrate that significant onboard processing capability enables a new class of science missions.

software fault tolerance supercomputing space remo↗
NASA NTRS2003

High End Computing Technologies for Earth Science Applications: Trends, Challenges, and Innovations

Earth science applications of the future will stress the capabilities of even the highest performance supercomputers in the areas of raw compute power, mass storage management, and software environments. These NASA mission critical problems demand usable multi-petaflops and exabyte-scale systems to fully realize their science goals. With an exciting vision of the technologies needed, NASA has established a comprehensive program of advanced research in computer architecture, software tools, and device technology to ensure that, in partnership with US industry, it can meet these demanding requirements with reliable, cost effective, and usable ultra-scale systems. NASA will exploit, explore, and influence emerging high end computing architectures and technologies to accelerate the next generation of engineering, operations, and discovery processes for NASA Enterprises. This article captures this vision and describes the concepts, accomplishments, and the potential payoff of the key thrusts that will help meet the computational challenges in Earth science applications.

Parks, John↗
NASA NTRS1992

High performance flight simulation at NASA Langley

The use of real-time simulation at the NASA facility is reviewed specifically with regard to hardware, software, and the use of a fiberoptic-based digital simulation network. The network hardware includes supercomputers that support 32- and 64-bit scalar, vector, and parallel processing technologies. The software include drivers, real-time supervisors, and routines for site-configuration management and scheduling. Performance specifications include: (1) benchmark solution at 165 sec for a single CPU; (2) a transfer rate of 24 million bits/s; and (3) time-critical system responsiveness of less than 35 msec. Simulation applications include the Differential Maneuvering Simulator, Transport Systems Research Vehicle simulations, and the Visual Motion Simulator. NASA is shown to be in the final stages of developing a high-performance computing system for the real-time simulation of complex high-performance aircraft.

Cleveland, Jeff I., II↗
NASA NTRS1998

NASA's Participation in the National Computational Grid

Over the last several years it has become evident that the character of NASA's supercomputing needs has changed. One of the major missions of the agency is to support the design and manufacture of aero- and space-vehicles with technologies that will significantly reduce their cost. It is becoming clear that improvements in the process of aerospace design and manufacturing will require a high performance information infrastructure that allows geographically dispersed teams to draw upon resources that are broader than traditional supercomputing. A computational grid draws together our information resources into one system. We can foresee the time when a Grid will allow engineers and scientists to use the tools of supercomputers, databases and on line experimental devices in a virtual environment to collaborate with distant colleagues. The concept of a computational grid has been spoken of for many years, but several events in recent times are conspiring to allow us to actually build one. In late 1997 the National Science Foundation initiated the Partnerships for Advanced Computational Infrastructure (PACI) which is built around the idea of distributed high performance computing. The Alliance lead, by the National Computational Science Alliance (NCSA), and the National Partnership for Advanced Computational Infrastructure (NPACI), lead by the San Diego Supercomputing Center, have been instrumental in drawing together the "Grid Community" to identify the technology bottlenecks and propose a research agenda to address them. During the same period NASA has begun to reformulate parts of two major high performance computing research programs to concentrate on distributed high performance computing and has banded together with the PACI centers to address the research agenda in common.

Feiereisen, William J.↗
NASA NTRS1986

Mars landing mission: A structural approach

A Mars landing mission in 2000 presents a structural challenge. Earlier studies have indicated that a Mars landing was then feasible using current structural techniques. Since these earlier studies, technology advances were made to enhance the capability. Lighter and stronger materials, large structures programs, and supercomputers now exist and even greater advances are expected. The feasibility of a Mars landing does not depend on the structure. If the space travelers can withstand the trip, the necessary structures can be provided to deliver them. If artificial gravity is required the structure can also provide for it. The structural challenge is to provide structural designs that are lightweight with high reliability. In order to do this advanced technology must be utilized to the fullest on all structural elements.

Fuller, Stan↗
NASA NTRS1989

Efficient utilization of graphics technology for space animation

Efficient utilization of computer graphics technology has become a major investment in the work of aerospace engineers and mission designers. These new tools are having a significant impact in the development and analysis of complex tasks and procedures which must be prepared prior to actual space flight. Design and implementation of useful methods in applying these tools has evolved into a complex interaction of hardware, software, network, video and various user interfaces. Because few people can understand every aspect of this broad mix of technology, many specialists are required to build, train, maintain and adapt these tools to changing user needs. Researchers have set out to create systems where an engineering designer can easily work to achieve goals with a minimum of technological distraction. This was accomplished with high-performance flight simulation visual systems and supercomputer computational horsepower. Control throughout the creative process is judiciously applied while maintaining generality and ease of use to accommodate a wide variety of engineering needs.

Panos, Gregory Peter↗
NASA NTRS2009

Coupling Advanced Modeling and Visualization to Improve High-Impact Tropical Weather Prediction

To meet the goals of extreme weather event warning, this approach couples a modeling and visualization system that integrates existing NASA technologies and improves the modeling system's parallel scalability to take advantage of petascale supercomputers. It also streamlines the data flow for fast processing and 3D visualizations, and develops visualization modules to fuse NASA satellite data.

Shen, Bo-Wen↗
NASA NTRS2017

NeMO-Net The Neural Multi-Modal Observation Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets.We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Remote Sensin↗
NASA NTRS2017

NeMO-Net: The Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 percent error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets. We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

NeMO-Net↗
NASA NTRS2015

Supercomputing in the Age of Discovering Superearths, Earths and Exoplanet Systems

With the Kepler Mission, exoplanet science has entered the era of Big Data and has benefited tremendously from the availability of the NAS (NASA Advanced Supercomputing Division) Pleiades supercomputer. This presentation provides an overview of the history of exoplanet discoveries and highlights the key enabling technologies for Kepler. I provide an overview of the Kepler science pipeline and the algorithms used to analyze the data and detect transiting planets, all of which run routinely on the NAS Pleiades system. The presentation also provides a description of the Blender analysis program used to validate Kepler planet discoveries statistically, and the effort to discover exomoons lurking in Kepler data, both of which also run on Pleiades. Finally, I describe NASA's TESS (Transiting Exoplanet Survey Satellite) Mission, which leverages Kepler heritage to provide a science pipeline similar to that for Kepler and that will also run routinely on the NAS Pleiades supercomputer.

big data↗
NASA NTRS1993

A Novel Method for Characterization of Superconductors: Physical Measurements and Modeling of Thin Films

A method for characterization of granular superconducting thin films has been developed which encompasses both the morphological state of the sample and its fabrication process parameters. The broad scope of this technique is due to the synergism between experimental measurements and their interpretation using numerical simulation. Two novel technologies form the substance of this system: the magnetically modulated resistance method for characterizing superconductors; and a powerful new computer peripheral, the Parallel Information Processor card, which provides enhanced computing capability for PC computers. This enhancement allows PC computers to operate at speeds approaching that of supercomputers. This makes atomic scale simulations possible on low cost machines. The present development of this system involves the integration of these two technologies using mesoscale simulations of thin film growth. A future stage of development will incorporate atomic scale modeling.

Kim, B. F.↗
NASA NTRS1987

Applications of computational modeling in ballistics

The development of the technology of ballistics as applied to gun launched Army weapon systems is the main objective of research at the U.S. Army Ballistic Research Laboratory (BRL). The primary research programs at the BRL consist of three major ballistic disciplines: exterior, interior, and terminal. The work done at the BRL in these areas was traditionally highly dependent on experimental testing. A considerable emphasis was placed on the development of computational modeling to augment the experimental testing in the development cycle; however, the impact of the computational modeling to this date is modest. With the availability of supercomputer computational resources recently installed at the BRL, a new emphasis on the application of computational modeling to ballistics technology is taking place. The major application areas are outlined which are receiving considerable attention at the BRL at present and to indicate the modeling approaches involved. An attempt was made to give some information as to the degree of success achieved and indicate the areas of greatest need.

Sturek, Walter B.↗