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NASA NTRS1990

Sparse distributed memory overview

The Sparse Distributed Memory (SDM) project is investigating the theory and applications of massively parallel computing architecture, called sparse distributed memory, that will support the storage and retrieval of sensory and motor patterns characteristic of autonomous systems. The immediate objectives of the project are centered in studies of the memory itself and in the use of the memory to solve problems in speech, vision, and robotics. Investigation of methods for encoding sensory data is an important part of the research. Examples of NASA missions that may benefit from this work are Space Station, planetary rovers, and solar exploration. Sparse distributed memory offers promising technology for systems that must learn through experience and be capable of adapting to new circumstances, and for operating any large complex system requiring automatic monitoring and control. Sparse distributed memory is a massively parallel architecture motivated by efforts to understand how the human brain works. Sparse distributed memory is an associative memory, able to retrieve information from cues that only partially match patterns stored in the memory. It is able to store long temporal sequences derived from the behavior of a complex system, such as progressive records of the system's sensory data and correlated records of the system's motor controls.

Raugh, Mike↗

NASA NTRS1995

A rotating arm using shape-memory alloy

NASA's Mars Pathfinder mission, to be launched in 1996, reflects a new philosophy of exploiting new technologies to reduce mission cost and accelerate the pace of space exploration. One of the experiments on board Pathfinder will demonstrate the first use in space of a multi-cycle, electrically-activated, shape-memory alloy (SMA) actuator. SMA's are metal alloys which, when heated, undergo a crystalline phase change. This change in phase alters the alloy lattice-constant, resulting in a change of dimension. Upon cooling, the alloy returns to its original lattice formation. Wire drawn from an SMA contracts in length when heated. The reversible change in length is 3 percent to 5 percent. The wire used in this actuator is a nickel-titanium alloy known as nitinol.

Jenkins, Phillip P.↗

NASA NTRS2012

Shape Memory Composite Hybrid Hinge

There are two conventional types of hinges for in-space deployment applications. The first type is mechanically deploying hinges. A typical mechanically deploying hinge is usually composed of several tens of components. It is complicated, heavy, and bulky. More components imply higher deployment failure probability. Due to the existence of relatively moving components among a mechanically deploying hinge, it unavoidably has microdynamic problems. The second type of conventional hinge relies on strain energy for deployment. A tape-spring hinge is a typical strain energy hinge. A fundamental problem of a strain energy hinge is that its deployment dynamic is uncontrollable. Usually, its deployment is associated with a large impact, which is unacceptable for many space applications. Some damping technologies have been experimented with to reduce the impact, but they increased the risks of an unsuccessful deployment. Coalescing strain energy components with shape memory composite (SMC) components to form a hybrid hinge is the solution. SMCs are well suited for deployable structures. A SMC is created from a high-performance fiber and a shape memory polymer resin. When the resin is heated to above its glass transition temperature, the composite becomes flexible and can be folded or packed. Once cooled to below the glass transition temperature, the composite remains in the packed state. When the structure is ready to be deployed, the SMC component is reheated to above the glass transition temperature, and it returns to its as-fabricated shape. A hybrid hinge is composed of two strain energy flanges (also called tape-springs) and one SMC tube. Two folding lines are placed on the SMC tube to avoid excessive strain on the SMC during folding. Two adapters are used to connect the hybrid hinge to its adjacent structural components. While the SMC tube is heated to above its glass transition temperature, a hybrid hinge can be folded and stays at folded status after the temperature is reduced to below its glass transition temperature. After the deployable structure is launched in space, the SMC tube is reheated and the hinge is unfolded to deploy the structure. Based on test results, the hybrid hinge can achieve higher than 99.999% shape recovery. The hybrid hinge inherits all of the good characteristics of a tape-spring hinge such as simplicity, light weight, high deployment reliability, and high deployment precision. Conversely, it eliminates the deployment impact that has significantly limited the applications of a tape-spring hinge. The deployment dynamics of a hybrid hinge are in a slow and controllable fashion. The SMC tube of a hybrid hinge is a multifunctional component. It serves as a deployment mechanism during the deployment process, and also serves as a structural component after the hinge is fully deployed, which makes a hybrid hinge much stronger and stiffer than a tape-spring hinge. Unlike a mechanically deploying hinge that uses relatively moving components, a hybrid hinge depends on material deformation for its packing and deployment. It naturally eliminates the microdynamic phenomenon.

Fang, Houfei↗

NASA NTRSDate not supplied

Designing Non-Pneumatic, Structural Tires for Space: Challenges and Solutions for a Difficult Application

Design and development of Shape Memory Alloy (SMA) Tires is a difficult and challenging problem. A “build and test” approach isn’t practical due to both the expense and time associated with taking such an approach. The multiple iterations that would be required to close requirements space for a space flight hardware program makes this approach infeasible. Hence, details regarding GRC’s SMA modeling approach to solving this challenging problem will be highlighted and used to showcase the power associated with leveraging an SMA modeling framework during design.

Shape Memory Alloys (SMA)↗

NASA NTRS2014

Radiation Effects of Commercial Resistive Random Access Memories

We present results for the single-event effect response of commercial production-level resistive random access memories. We found that the resistive memory arrays are immune to heavy ion-induced upsets. However, the devices were susceptible to single-event functional interrupts, due to upsets from the control circuits. The intrinsic radiation tolerant nature of resistive memory makes the technology an attractive consideration for future space applications.

Random Access Memory (RAM)↗

DOE OSTI2025

Numerically exact configuration interaction at quadrillion-determinant scale

The combinatorial growth of configuration interaction (CI) has long limited this formally exact quantum chemistry method to only the smallest molecules. Here, we report a numerically exact CI calculation exceeding one quadrillion (10 15 ) determinants, made possible by a lossless categorical compression strategy within the small-tensor-product distributed active space (STP-DAS) framework. This approach overcomes the traditional memory bottlenecks of CI by a numerically exact compression of the wavefunction representation and reformulating the most computationally demanding matrix–vector operations. Using this method, we performed a fully relativistic CI calculation of the ground state of HBrTe with over 10 15 complex-valued determinants in just 34.5 h on 1000 computing nodes—the largest CI calculation ever reported. We further achieved fast computation for systems with hundreds of billions of determinants on only a few compute nodes. Extensive benchmarks confirm that the method retains full numerical exactness while cutting memory and computational cost by orders of magnitude. Compared to previous state-of-the-art CI calculations, this work achieves a 1000 times increase in CI space, a 10 6 -fold increase in floating-point operations performed, and a 10 6 -fold improvement in computational speed.

Computational chemistry↗

NASA NTRS2011

Experiment in Onboard Synthetic Aperture Radar Data Processing

Single event upsets (SEUs) are a threat to any computing system running on hardware that has not been physically radiation hardened. In addition to mandating the use of performance-limited, hardened heritage equipment, prior techniques for dealing with the SEU problem often involved hardware-based error detection and correction (EDAC). With limited computing resources, software- based EDAC, or any more elaborate recovery methods, were often not feasible. Synthetic aperture radars (SARs), when operated in the space environment, are interesting due to their relevance to NASAs objectives, but problematic in the sense of producing prodigious amounts of raw data. Prior implementations of the SAR data processing algorithm have been too slow, too computationally intensive, and require too much application memory for onboard execution to be a realistic option when using the type of heritage processing technology described above. This standard C-language implementation of SAR data processing is distributed over many cores of a Tilera Multicore Processor, and employs novel Radiation Hardening by Software (RHBS) techniques designed to protect the component processes (one per core) and their shared application memory from the sort of SEUs expected in the space environment. The source code includes calls to Tilera APIs, and a specialized Tilera compiler is required to produce a Tilera executable. The compiled application reads input data describing the position and orientation of a radar platform, as well as its radar-burst data, over time and writes out processed data in a form that is useful for analysis of the radar observations.

Holland, Matthew↗

NASA NTRS2017

Behavioral Assessment of Spaceflight Effects on Neurocognitive Performance: Extent and Longevity

Exposure to the microgravity environment during spaceflight missions impacts crewmembers' sensorimotor function. Bock et al. [1] studied the cognitive demands of human sensorimotor performance and dual tasking during long duration missions and concluded that both stress and scarcity of cognitive resources required for sensorimotor adaptation may be responsible for these deficits during spaceflight. Therefore, in consideration of the health and performance of crewmembers in- and post-flight, we are conducting this study to investigate the effects of spaceflight on the extent, longevity and neural bases of sensorimotor, cognitive, and neural changes. The data presented will focus on the behavioral measures that were collected pre-, in- and post-flight including spatial cognition, processing speed, bimanual coordination, functional mobility, computerized dynamic posturography (CDP), and vibrotactile induced vestibular evoked myogenic potential (VEMP). To date, data were collected over the course of two pre-flight sessions and four post-flight sessions on five crewmembers (n=13) using the protocol described in Koppelmans et al. [2]. Balance control was assessed using CDP, with eyes closed and a sway-referenced base of support (Sensory Organization Test 5), with and without head movements in the pitch plane. Spatial working memory was assessed using Thurston's Card Rotation Test and a Mental Rotation Test. The Rod and Frame Test was performed to test visual dependence. The Digit Symbol Substitution Test was performed to evaluate processing speed, and the Purdue Pegboard Task was performed to test bimanual coordination. Vestibular function was assessed by eliciting ocular VEMP via a hand held striker on the side of the head as subjects lay supine on a gurney. Subjects also performed the Functional Mobility Test of walking through an obstacle course to assess rate of early motor learning. Data were also collected on the same crewmembers during three in-flight sessions on the International Space Station (ISS). In-flight, spatial working memory was assessed using the Mental Rotation Test, adaptation to visuo-motor transformation in manual control was assessed using the Sensorimotor Adaptation Test, and multi-tasking ability was assessed using the Dual Task Test. These three tests were performed in a strapped-in configuration mimicking a seated position - waist bungees pulled the crewmember toward the "floor" with feet secured in foot loops. The Mental Rotation Test was also performed in a free-floating configuration while the crewmember floated while holding on to the gamepad controller used to provide input that was secured to the equipment rack on the ISS. Preliminary findings from data collected to date, will be included in the presentation. Eventual comparison to results from supporting bed rest and longitudinal studies will enable the parsing out of the multiple mechanisms contributing to any observed spaceflight-induced sensorimotor and cognitive behavioral changes.

De Dios, Y. E.↗

DOE OSTI2020

Traversing large graphs on GPUs with unified memory

Due to the limited capacity of GPU memory, the majority of prior work on graph applications on GPUs has been restricted to graphs of modest sizes that fit in memory. Recent hardware and software advances make it possible to address much larger host memory transparently as a part of a feature known as unified virtual memory. While accessing host memory over an interconnect is understandably slower, the problem space has not been sufficiently explored in the context of a challenging workload with low computational intensity and an irregular data access pattern such as graph traversal. We analyse the performance of breadth first search (BFS) for several large graphs in the context of unified memory and identify the key factors that contribute to slowdowns. Next, we propose a lightweight offline graph reordering algorithm, HALO (Harmonic Locality Ordering), that can be used as a pre-processing step for static graphs. HALO yields speedups of 1.5x-1.9x over baseline in subsequent traversals. Our method specifically aims to cover large directed real world graphs in addition to undirected graphs whereas prior methods only account for the latter. Additionally, we demonstrate ties between the locality ordering problem and graph compression and show that prior methods from graph compression such as recursive graph bisection can be suitably adapted to this problem.

Gera, Prasun↗

NASA NTRS2008

Intelligent Memory Module Overcomes Harsh Environments

Solar cells, integrated circuits, and sensors are essential to manned and unmanned space flight and exploration, but such systems are highly susceptible to damage from radiation. Especially problematic, the Van Allen radiation belts encircle Earth in concentric radioactive tori at distances from about 6,300 to 38,000 km, though the inner radiation belt can dip as low as 700 km, posing a severe hazard to craft and humans leaving Earth s atmosphere. To avoid this radiation, the International Space Station and space shuttles orbit at altitudes between 275 and 460 km, below the belts range, and Apollo astronauts skirted the edge of the belts to minimize exposure, passing swiftly through thinner sections of the belts and thereby avoiding significant side effects. This radiation can, however, prove detrimental to improperly protected electronics on satellites that spend the majority of their service life in the harsh environment of the belts. Compact, high-performance electronics that can withstand extreme environmental and radiation stress are thus critical to future space missions. Increasing miniaturization of electronics addresses the need for lighter weight in launch payloads, as launch costs put weight at a premium. Likewise, improved memory technologies have reduced size, cost, mass, power demand, and system complexity, and improved high-bandwidth communication to meet the data volume needs of the next-generation high-resolution sensors. This very miniaturization, however, has exacerbated system susceptibility to radiation, as the charge of ions may meet or exceed that of circuitry, overwhelming the circuit and disrupting operation of a satellite. The Hubble Space Telescope, for example, must turn off its sensors when passing through intense radiation to maintain reliable operation. To address the need for improved data quality, additional capacity for raw and processed data, ever-increasing resolution, and radiation tolerance, NASA spurred the development of the Radiation Tolerant Intelligent Memory Stack (RTIMS).

Source record↗

DOE OSTI2025

DIF3D-VARIANT 12.0: Updates and New Features

The DIF3D code has been a workhorse of fast reactor analysis work at Argonne National Laboratory for over 40 years. In 1995, a transport option called VARIANT was added to DIF3D to improve the flux solutions for fast reactor problems which we term DIF3D-VARIANT today. DIF3D-VARIANT performs nodal neutron transport calculations using P N or SP N theory in Cartesian and hexagonal two- and three-dimensional geometries. The limited computing capabilities of the time restricted DIF3D-VARIANT to use at most a 6 th order spatial approximation combined with a P3 flux approximation and P1 scattering kernel for a 33 group structure on most studied reactor problems. Computer capabilities have increased steadily since 1995 and today much larger space-angle-energy approximations are possible. This manuscript serves as an update to the theory section of the original DIF3D-VARIANT manual and details more than twenty years of changes made to DIF3D to make version 12 which was released on November 1 st , 2024. The primary focus of the initial work was to extend the space-angle approximations available in DIF3D-VARIANT such that the error due to transport approximations could be better understood. This work was started and completed in 2002 and marked the official version 10. Unfortunately, those higher order approximations could not be used at that time due to the memory constraints of the BPOINTER part of DIF3D (limited to 2 GB). In version 11, completed in 2012, BPOINTER was circumvented in DIF3D-VARIANT for the largest arrays by introducing a Fortran 90 module called LMA (Large Memory Array). This seamlessly replaces all of the functionality of the BPOINTER concept, but it allows 64 bit addressing for every array such that they can be larger than 2 GB. It is now common for DIF3D-VARIANT jobs to consume 50 GB of memory on modern workstations when using high order space-angle approximations and a large number of groups. Many improvements were made to version 11 from 2012 to 2022 when work to create version 12 started. For version 12, several parts of DIF3D were updated to improve performance and thread parallelism was introduced to further reduce the runtime. Numerous minor bugs were discovered in DIF3D-VARIANT as part of the process of creating the perturbation and sensitivity code PERSENT. All of these algorithmic problems were identified in the transition from version 10 to version 11 which prevented DIF3D-VARIANT from running efficiently and reliably. Firstly, the coarse mesh rebalance scheme would routinely diverge and a study detailed in this report demonstrates how it was also typically not effective. This is not a failure of the coarse mesh rebalance methodology, but a failure of its implementation in DIF3D-VARIANT for hexagonal geometries. The fission source extrapolation algorithm was also found to be unreliable on larger group structure problems, leading to divergence in some cases and a negligible improvement in performance overall. Finally, the “Omega” acceleration applied to the partial current solver routine of DIF3D-VARIANT was found to cause DIF3D-VARIANT to converge to the wrong answer. To resolve these issues, both the coarse mesh rebalance and fission source extrapolation were permanently disabled in version 11. The Tchebychev acceleration was put in as a temporary reliable alternative but it is generally inferior to coarse mesh rebalance or coarse mesh finite difference. For the Omega acceleration, the factor was restricted to guarantee that it would not cause follow-on errors in PERSENT. Due to limited funding to support maintenance and development of DIF3D in the last 10 years, no effort was spent since to resolve the outer iteration acceleration. Except for the threading work, all of the changes discussed in this manuscript refer to changes made between version 10 and version 11. Performance comparisons are done to demonstrate the improvements from version 9 to version 12. As will be demonstrated, the updated versi

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

UPC++ v1.0 Programmer’s Guide, Revision 2020.10.0

UPC++ is a C++11 library that provides Partitioned Global Address Space (PGAS) programming. It is designed for writing parallel programs that run efficiently and scale well on distributed-memory parallel computers. The PGAS model is single program, multiple-data (SPMD), with each separate constituent process having access to local memory as it would in C++. However, PGAS also provides access to a global address space, which is allocated in shared segments that are distributed over the processes. UPC++ provides numerous methods for accessing and using global memory. In UPC++, all operations that access remote memory are explicit, which encourages programmers to be aware of the cost of communication and data movement. Moreover, all remote-memory access operations are by default asynchronous, to enable programmers to write code that scales well even on hundreds of thousands of cores.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

UPC++ v1.0 Programmer’s Guide, Revision 2020.3.0

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1972

Design, processing, and testing of lsi arrays for space station

The design of a MOS 256-bit Random Access Memory (RAM) is discussed. Technological achievements comprise computer simulations that accurately predict performance; aluminum-gate COS/MOS devices including a 256-bit RAM with current sensing; and a silicon-gate process that is being used in the construction of a 256-bit RAM with voltage sensing. The Si-gate process increases speed by reducing the overlap capacitance between gate and source-drain, thus reducing the crossover capacitance and allowing shorter interconnections. The design of a Si-gate RAM, which is pin-for-pin compatible with an RCA bulk silicon COS/MOS memory (type TA 5974), is discussed in full. The Integrated Circuit Tester (ICT) is limited to dc evaluation, but the diagnostics and data collecting are under computer control. The Silicon-on-Sapphire Memory Evaluator (SOS-ME, previously called SOS Memory Exerciser) measures power supply drain and performs a minimum number of tests to establish operation of the memory devices. The Macrodata MD-100 is a microprogrammable tester which has capabilities of extensive testing at speeds up to 5 MHz. Beam-lead technology was successfully integrated with SOS technology to make a simple device with beam leads. This device and the scribing are discussed.

Lile, W. R.↗

NASA NTRS2005

Case Study of the Space Shuttle Cockpit Avionics Upgrade Software

The purpose of the Space Shuttle Cockpit Avionics Upgrade project was to reduce crew workload and improve situational awareness. The upgrade was to augment the Shuttle avionics system with new hardware and software. An early version of this system was used to gather human factor statistics in the Space Shuttle Motion Simulator of the Johnson Space Center for one month by multiple teams of astronauts. The results were compiled by NASA Ames Research Center and it was was determined that the system provided a better than expected increase in situational awareness and reduction in crew workload. Even with all of the benefits nf the system, NASA cancelled the project towards the end of the development cycle. A major success of this project was the validation of the hardware architecture and software design. This was significant because the project incorporated new technology and approaches for the development of human rated space software. This paper serves as a case study to document knowledge gained and techniques that can be applied for future space avionics development efforts. The major technological advances were the use of reflective memory concepts for data acquisition and the incorporation of Commercial off the Shelf (COTS) products in a human rated space avionics system. The infused COTS products included a real time operating system, a resident linker and loader, a display generation tool set, and a network data manager. Some of the successful design concepts were the engineering of identical outputs in multiple avionics boxes using an event driven approach and inter-computer communication, a reconfigurable data acquisition engine, the use of a dynamic bus bandwidth allocation algorithm. Other significant experiences captured were the use of prototyping to reduce risk, and the correct balance between Object Oriented and Functional based programming.

Ferguson, Roscoe C.↗

NASA NTRS2009

Memory Technologies and Data Recorder Design

Missions, both near Earth and deep space, are under consideration that will require data recorder capacities of such magnitude as to be unthinkable just a few years ago. Concepts requiring well over 16,000 GB of storage are being studied. To achieve this capacity via "normal means" was considered incredible as recently as 2004. This paper is presented in two parts. Part I describes the analysis of data recorder capacities for missions as far back as 35 years and provides a projection of data capacities required 20 years from now based upon missions either nearing launch, or in the planning stage. The paper presents a similar projection of memory device capacities as baselined in the ITRS - the International Technology Roadmap for Semiconductors. Using known Total Ionizing Dose tolerance going back as far as a decade, a projection of total dose tolerance is made for two prime technologies out to the year 2028.

Strauss, Karl F↗

DOE OSTI2022

Generative memory for lifelong machine learning

Techniques are disclosed for training machine learning systems. An input device receives training data comprising pairs of training inputs and training labels. A generative memory assigns training inputs to each archetype task of a plurality of archetype tasks, each archetype task representative of a cluster of related tasks within a task space and assigns a skill to each archetype task. The generative memory generates, from each archetype task, auxiliary data comprising pairs of auxiliary inputs and auxiliary labels. A machine learning system trains a machine learning model to apply a skill assigned to an archetype task to training and auxiliary inputs assigned to the archetype task to obtain output labels corresponding to the training and auxiliary labels associated with the training and auxiliary inputs assigned to the archetype task to enable scalable learning to obtain labels for new tasks for which the machine learning model has not previously been trained.

Nadamuni Raghavan, Aswin↗

DOE OSTI2021

In Situ Machine Learning for Intelligent Data Capture on Exascale Platforms. Final Report

In many dynamic systems, interesting events occur locally in time and space. Examples of such systems include ignition events in combustion simulations, material fractures in mechanics simulations, and extreme weather events in climate simulations. Due to memory constraints and data I/O costs, current simulation workflows save data at regularly spaced time-steps, at a fixed rate determined before the start of the simulation. Often this mode of operation results in missed events of interest, necessitating a simulation restart from before an event occurred with more frequent data saves. This data saving workflow is grossly inefficient and is already a bottleneck in the computing process. We propose to develop machine learning algorithms that can detect when interesting dynamical events are occurring, triggering data saves. These machine learning algorithms will perform in situ anomaly detection to flag regions with different dynamical properties than those previously recorded. The adaptive data saves would be local in time and space to match the event of interest, thereby enabling a much more efficient workflow that will reduce data I/O costs and data storage memory requirements. The algorithms will be tested on two applications: auto-ignition simulations and climate simulations. A critical component of this project will be developing machine learning algorithms that can be deployed efficiently in situ on HPC platforms with out-of-the-box functionality. The development of in situ machine learning methods to detect anomalous events would enable a more efficient and effective workflow, in which all the relevant data are saved in a single simulation run, without re-starts or scientist intervention.

42 ENGINEERING↗