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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2014

Software Engineering for Human Spaceflight

The Spacecraft Software Engineering Branch of NASA Johnson Space Center (JSC) provides world‐class products, leadership, and technical expertise in software engineering, processes, technology, and systems management for human spaceflight. The branch contributes to major NASA programs (e.g. ISS, MPCV/Orion) with in‐house software development and prime contractor oversight, and maintains the JSC Engineering Directorate CMMI rating for flight software development. Software engineering teams work with hardware developers, mission planners, and system operators to integrate flight vehicles, habitats, robotics, and other spacecraft elements. They seek to infuse automation and autonomy into missions, and apply new technologies to flight processor and computational architectures. This presentation will provide an overview of key software‐related projects, software methodologies and tools, and technology pursuits of interest to the JSC Spacecraft Software Engineering Branch.

Fredrickson, Steven E.↗

NASA NTRS2004

Rapid Chemometric Filtering of Spectral Data

A method of rapid, programmable filtering of spectral transmittance, reflectance, or fluorescence data to measure the concentrations of chemical species has been proposed. By programmable is meant that a variety of spectral analyses can readily be performed and modified in software, firmware, and/or electronic hardware, without need to change optical filters or other optical hardware of the associated spectrometers. The method is intended to enable real-time identification of single or multiple target chemical species in applications that involve high-throughput screening of multiple samples. Examples of such applications include (but are not limited to) combinatorial chemistry, flow cytometry, bead assays, testing drugs, remote sensing, and identification of targets. The basic concept of the proposed method is to perform real-time crosscorrelations of a measured spectrum with one or more analytical function(s) of wavelength that could be, for example, the known spectra of target species. Assuming that measured spectral intensities are proportional to concentrations of target species plus background spectral intensities, then after subtraction of background levels, it should be possible to determine target species concentrations from cross-correlation values. Of course, the problem of determining the concentrations is more complex when spectra of different species overlap, but the problem can be solved by use of multiple analytical functions in combination with computational techniques that have been developed previously for analyses of this type. The method is applicable to the design and operation of a spectrometer in which spectrally dispersed light is measured by means of an active-pixel sensor (APS) array. The row or column dimension of such an array is generally chosen to be aligned along the spectral-dispersion dimension, so that each pixel intercepts light in a narrow spectral band centered on a wavelength that is a known function of the pixel position. The proposed method admits of two hardware implementations for computing cross-correlations in real time.

Beaman, Gregory↗

DOE OSTI2022

Bridging Python to Silicon: The SODA Toolchain

Systems performing scientific computing, data analysis, and machine learning tasks have a growing demand for application-specific accelerators that can provide high computational performance while meeting strict size and power requirements. However, the algorithms and applications that need to be accelerated are evolving at a rate that is incompatible with manual design processes based on hardware description languages. Agile hardware design tools based on compiler techniques can help by quickly producing an application-specific integrated circuit (ASIC) accelerator starting from a high-level algorithmic description. Here, we present the software-defined accelerator (SODA) synthesizer, a modular and open-source hardware compiler that provides automated end-to-end synthesis from high-level software frameworks to ASIC implementation, relying on multilevel representations to progressively lower and optimize the input code. Our approach does not require the application developer to write any register-transfer level code, and it is able to reach up to 364 giga floating point operations per second (GFLOPS)/W efficiency (32-bit precision) on typical convolutional neural network operators.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Hardware-Conscious Optimization of the Quantum Toffoli Gate

While quantum computing holds great potential in combinatorial optimization, electronic structure calculation, and number theory, the current era of quantum computing is limited by noisy hardware. Many quantum compilation approaches can mitigate the effects of imperfect hardware by optimizing quantum circuits for objectives such as critical path length. Few approaches consider quantum circuits in terms of the set of vendor-calibrated operations (i.e., native gates) available on target hardware. This manuscript expands the analytical and numerical approaches for optimizing quantum circuits at this abstraction level. We present a procedure for combining the strengths of analytical native gate-level optimization with numerical optimization. Although we focus on optimizing Toffoli gates on the IBMQ native gate set, the methods presented are generalizable to any gate and superconducting qubit architecture. Our optimized Toffoli gate implementation demonstrates an 18% reduction in infidelity compared with the canonical implementation as benchmarked on IBM Jakarta with quantum process tomography. Assuming the inclusion of multi-qubit cross-resonance (MCR) gates in the IBMQ native gate set, we produce Toffoli implementations with only six multi-qubit gates, a 25% reduction from the canonical eight multi-qubit implementations for linearly connected qubits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Opportunities for retrieval and tool augmented large language models in scientific facilities

Upgrades to advanced scientific user facilities such as next-generation x-ray light sources, nanoscience centers, and neutron facilities are revolutionizing our understanding of materials across the spectrum of the physical sciences, from life sciences to microelectronics. However, these facility and instrument upgrades come with a significant increase in complexity. Driven by more exacting scientific needs, instruments and experiments become more intricate each year. This increased operational complexity makes it ever more challenging for domain scientists to design experiments that effectively leverage the capabilities of and operate on these advanced instruments. Large language models (LLMs) can perform complex information retrieval, assist in knowledge-intensive tasks across applications, and provide guidance on tool usage. Using x-ray light sources, leadership computing, and nanoscience centers as representative examples, we describe preliminary experiments with a Context-Aware Language Model for Science (CALMS) to assist scientists with instrument operations and complex experimentation. With the ability to retrieve relevant information from facility documentation, CALMS can answer simple questions on scientific capabilities and other operational procedures. With the ability to interface with software tools and experimental hardware, CALMS can conversationally operate scientific instruments. By making information more accessible and acting on user needs, LLMs could expand and diversify scientific facilities’ users and accelerate scientific output.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Editorial: Themed issue on data-intensive water systems management and operation

The water sector increasingly looks at intensified instrumentation, data collection and automation as tools for daily use. Still, a massive opportunity remains to fully embrace emerging methods and technologies, such as artificial intelligence, data analytics, low-cost sensor hardware, and cloud computing. Indeed, sensing and automation technology has already infiltrated many facets of society. As such, the time is ripe to evaluate the role of novel technologies for systems monitoring, diagnostics, and automation of aquatic processes and large-scale water systems. When leveraged, the water sector will do more with less.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2005

Photo-realistic Terrain Modeling and Visualization for Mars Exploration Rover Science Operations

Modern NASA planetary exploration missions employ complex systems of hardware and software managed by large teams of. engineers and scientists in order to study remote environments. The most complex and successful of these recent projects is the Mars Exploration Rover mission. The Computational Sciences Division at NASA Ames Research Center delivered a 30 visualization program, Viz, to the MER mission that provides an immersive, interactive environment for science analysis of the remote planetary surface. In addition, Ames provided the Athena Science Team with high-quality terrain reconstructions generated with the Ames Stereo-pipeline. The on-site support team for these software systems responded to unanticipated opportunities to generate 30 terrain models during the primary MER mission. This paper describes Viz, the Stereo-pipeline, and the experiences of the on-site team supporting the scientists at JPL during the primary MER mission.

Edwards, Laurence↗

NASA NTRS2007

Triaxial Probe Magnetic Data Analysis

The Triaxial Magnetic Moment Analysis software uses measured magnetic field test data to compute dipole and quadrupole moment information from a hardware element. It is used to support JPL projects needing magnetic control and an understanding of the spacecraft-generated magnetic fields. Evaluation of the magnetic moment of an object consists of three steps: acquisition, conditioning, and analysis. This version of existing software was extensively rewritten for easier data acquisition, data analysis, and report presentation, including immediate feedback to the test operator during data acquisition. While prior JPL computer codes provided the same data content, this program has a better graphic display including original data overlaid with reconstructed results to show goodness of fit accuracy and better appearance of the report graphic page. Data are acquired using three magnetometers and two rotations of the device under test. A clean acquisition user interface presents required numeric data and graphic summaries, and the analysis module yields the best fit (least squares) for the magnetic dipole and/or quadrupole moment of a device. The acquisition module allows the user to record multiple data sets, selecting the best data to analyze, and is repeated three times for each of the z-axial and y-axial rotations. In this update, the y-axial rotation starting position has been changed to an option, allowing either the x- or z-axis to point towards the magnetometer. The code has been rewritten to use three simultaneous axes of magnetic data (three probes), now using two "rotations" of the device under test rather than the previous three rotations, thus reducing handling activities on the device under test. The present version of the software gathers data in one-degree increments, which permits much better accuracy of the fit ted data than the coarser data acquisition of the prior software. The data-conditioning module provides a clean data set for the analysis module. For multiple measurements at a given degree, the first measurement is used. For omitted measurements, the missing field is estimated by linear interpolation between the two nearest measurements. The analysis module was rewritten for the dual rotation, triaxial probe measurement process and now has better moment estimation accuracy, based on the finer one degree of data acquisition resolution. The magnetic moments thus computed are used as an input to summarize the total spacecraft field.

Shultz, Kimberly↗

DOE OSTI2018

COOLR: A New System for Dynamic Thermal-Aware Computing

Extreme-scale systems need to walk a fine line between the amount of cooling they receive and the thermal-induced performance and reliability degradation they can sustain. System managers are extremely motivated to battle cooling-energy cost: the largest line item in their total operating cost. On the other hand, pushing the nodes to peak performance in tightened cooling regimes places the burden on the dynamic thermal management (DTM) to protect the hardware. DTM schemes throttle performance to relieve heat accumulation within nodes when cooling cannot mitigate the problem. These interventions prevent fatal failures but introduce inevitable performance degradations and variations. Such variations can have drastic consequences on the performance of extreme-scale systems. Furthermore, distribution of heat across different system nodes depends not only on the amount of workload. Even if a strictly equal share of computational load is assigned to all nodes, there are physical attributes and topological features of each system that inherently cause uneven accumulation of heat. These can cause one subcomponent to trigger DTM prematurely and penalize the overall system. Our goal in this project is to create a holistic thermal-aware view of the system, capturing both inherent physical attributes and dynamic system state. We developed COOLR, a dynamic system with the ability to evaluate the interplay between management of computation, data, power dissipation, and thermal state. Thereby, COOLR will achieve higher overall performance at the same energy and cooling cost. First, we performed a systematic power and thermal modeling and of high performance computing architectures. Analytical and empirical techniques were blended to generate a light-weight thermal model of target systems. Second, we developed a thermal-aware OS and runtime system. Policies for managing resources and schedules for processes has been designed to co-optimize the thermal state and performance of the system. The main outcome of our proposed thermal-aware dynamic computing system are scheduling and allocation of processes and memory accesses leading to an appropriately “skewed” distribution of activity across the system. The ultimate result of this new arrangement resulted in reduced occurrences of hotspots and reduced episodes of DTM intervention, leading to improvements in performance. The overall thermal characterization and modeling methodology (thermal instrumentation techniques, model building, and systematic model reduction) resulting from this project can be applied to systems beyond our immediate scope and will be applicable to future scaling. The thermal-aware dynamic computing paradigm will impact the efficiency of extreme-scale systems. The gained “thermal slack” can be given back to further tighten the cooling budget, benefiting the management cost of next generation extreme-scale systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

A high-performance implementation of atomistic spin dynamics simulations on x86 CPUs

Atomistic spin dynamics simulations provide valuable information about the energy spectrum of magnetic materials in different phases, allowing one to identify instabilities and the nature of their excitations. However, the time cost of evaluating the dynamical correlation function S(q,t) increases quadratically as the number of spins N, leading to significant computational effort, making the simulation of large spin systems very challenging. In this work, here, we propose to use a highly optimized general matrix multiply (GEMM) subroutine to calculate the dynamical spin-spin correlation function that can achieve near-optimal hardware utilization. Furthermore, we fuse the element-wise operations in the calculation of S(q,t) into the in-house GEMM kernel, which results in further performance improvements of 44\% - 71\% on several relatively large lattice sizes when compared to the implementation that uses the GEMM subroutine in OpenBLAS, which is the state-of-the-art open source library for Basic Linear Algebra Subroutine (BLAS).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

A Length Adaptive Algorithm-Hardware Co-design of Transformer on FPGA Through Sparse Attention and Dynamic Pipelining

Transformers are considered one of the most important deep learning models since 2018, in part because it establishes state-of-the-art (SOTA) records and could potentially replace existing Deep Neural Networks (DNNs). Despite the remarkable triumphs, the prolonged turnaround time of Transformer models is a widely recognized roadblock. The variety of sequence lengths imposes additional computing overhead where inputs need to be zero-padded to the maximum sentence length in the batch to accommodate the parallel computing platforms. This paper targets the field-programmable gate array (FPGA) and proposes a coherent sequence length adaptive algorithm–hardware co-design for Transformer acceleration. Particularly, we develop a hardware-friendly sparse attention operator and a length-aware hardware resource scheduling algorithm. The proposed sparse attention operator brings the complexity of attention-based models down to linear complexity and alleviates the off-chip memory traffic. The proposed length-aware resource hardware scheduling algorithm dynamically allocates the hardware resources to fill up the pipeline slots and eliminates bubbles for NLP tasks. Experiments show that our design has very small accuracy loss and has 80.2 × and 2.6 × speedup compared to CPU and GPU implementation, and 4 × higher energy efficiency than state-of-the-art GPU accelerator optimized via CUBLAS GEMM.

Peng, Hongwu↗

NASA NTRS1976

The 727 approach energy management system avionics specification (preliminary)

Hardware and software requirements for an Approach Energy Management System (AEMS) consisting of an airborne digital computer and cockpit displays are presented. The displays provide the pilot with a visual indication of when to manually operate the gear, flaps, and throttles during a delayed flap approach so as to reduce approach time, fuel consumption, and community noise. The AEMS is an independent system that does not interact with other navigation or control systems, and is compatible with manually flown or autopilot coupled approaches. Operational use of the AEMS requires a DME ground station colocated with the flight path reference.

Jackson, D. O.↗

NASA NTRS1989

Space station Simulation Computer System (SCS) study for NASA/MSFC. Volume 5: Study analysis report

The Simulation Computer System (SCS) is the computer hardware, software, and workstations that will support the Payload Training Complex (PTC) at the Marshall Space Flight Center (MSFC). The PTC will train the space station payload scientists, station scientists, and ground controllers to operate the wide variety of experiments that will be on-board the Freedom Space Station. The further analysis performed on the SCS study as part of task 2-Perform Studies and Parametric Analysis-of the SCS study contract is summarized. These analyses were performed to resolve open issues remaining after the completion of task 1, and the publishing of the SCS study issues report. The results of these studies provide inputs into SCS task 3-Develop and present SCS requirements, and SCS task 4-develop SCS conceptual designs. The purpose of these studies is to resolve the issues into usable requirements given the best available information at the time of the study. A list of all the SCS study issues is given.

Source record↗

NASA NTRS1991

Laser terminal attitude determination via autonomous star tracking

Freespace laser communication systems require an initial spatial acquisition process to coalign the cooperating terminal line of sites (typically to sub-microrad accuracy). The complexity and time required for this process can grow dramatically with the size of the host platform(s) initial uncertainty field-of-view. The feasibility of significantly refining the laser terminal attitude knowledge prior to the initiation of the spatial acquisition process via a preliminary star tracking operation is addressed. It is shown that currently planned laser terminal designs are capable of supporting this star tracking function with minimal hardware augmentation. An analysis of stellar spectral irradiance is presented which enables the computation of the tracking noise equivalent angle versus stellar class and visual magnitude.

Chapman, William↗

NASA NTRS2000

Performance Monitoring of Distributed Data Processing Systems

Test and checkout systems are essential components in ensuring safety and reliability of aircraft and related systems for space missions. A variety of systems, developed over several years, are in use at the NASA/KSC. Many of these systems are configured as distributed data processing systems with the functionality spread over several multiprocessor nodes interconnected through networks. To be cost-effective, a system should take the least amount of resource and perform a given testing task in the least amount of time. There are two aspects of performance evaluation: monitoring and benchmarking. While monitoring is valuable to system administrators in operating and maintaining, benchmarking is important in designing and upgrading computer-based systems. These two aspects of performance evaluation are the foci of this project. This paper first discusses various issues related to software, hardware, and hybrid performance monitoring as applicable to distributed systems, and specifically to the TCMS (Test Control and Monitoring System). Next, a comparison of several probing instructions are made to show that the hybrid monitoring technique developed by the NIST (National Institutes for Standards and Technology) is the least intrusive and takes only one-fourth of the time taken by software monitoring probes. In the rest of the paper, issues related to benchmarking a distributed system have been discussed and finally a prescription for developing a micro-benchmark for the TCMS has been provided.

Ojha, Anand K.↗

NASA NTRS2016

Performance Analysis of a Hardware Implemented Complex Signal Kurtosis Radio-Frequency Interference Detector

Radio-frequency interference (RFI) is a known problem for passive remote sensing as evidenced in the L-band radiometers SMOS, Aquarius and more recently, SMAP. Various algorithms have been developed and implemented on SMAP to improve science measurements. This was achieved by the use of a digital microwave radiometer. RFI mitigation becomes more challenging for microwave radiometers operating at higher frequencies in shared allocations. At higher frequencies larger bandwidths are also desirable for lower measurement noise further adding to processing challenges. This work focuses on finding improved RFI mitigation techniques that will be effective at additional frequencies and at higher bandwidths. To aid the development and testing of applicable detection and mitigation techniques, a wide-band RFI algorithm testing environment has been developed using the Reconfigurable Open Architecture Computing Hardware System (ROACH) built by the Collaboration for Astronomy Signal Processing and Electronics Research (CASPER) Group. The testing environment also consists of various test equipment used to reproduce typical signals that a radiometer may see including those with and without RFI. The testing environment permits quick evaluations of RFI mitigation algorithms as well as show that they are implementable in hardware. The algorithm implemented is a complex signal kurtosis detector which was modeled and simulated. The complex signal kurtosis detector showed improved performance over the real kurtosis detector under certain conditions. The real kurtosis is implemented on SMAP at 24 MHz bandwidth. The complex signal kurtosis algorithm was then implemented in hardware at 200 MHz bandwidth using the ROACH. In this work, performance of the complex signal kurtosis and the real signal kurtosis are compared. Performance evaluations and comparisons in both simulation as well as experimental hardware implementations were done with the use of receiver operating characteristic (ROC) curves. The complex kurtosis algorithm has the potential to reduce data rate due to onboard processing in addition to improving RFI detection performance.

microwaves↗

NASA NTRS2016

Performance Analysis of a Hardware Implemented Complex Signal Kurtosis Radio-Frequency Interference Detector

radiometers↗

DOE OSTI2017

Extreme-Scale Algorithms & Software Resilience (EASIR) Architecture-Aware Algorithms for Scalable Performance and Resilience on Heterogeneous Architectures

This project addresses both communication-avoiding algorithms, and reproducible floating-point computation. Communication, i.e. moving data, either between levels of memory or processors over a network, is much more expensive per operation than arithmetic (measured in time or energy), so we seek algorithms that greatly reduce communication. We developed many new algorithms for both dense and sparse, and both direct and iterative linear algebra, attaining new communication lower bounds, and getting large speedups in many cases. We also extended this work in several ways: (1) We minimize writes separately from reads, since writes may be much more expensive than reads on emerging memory technologies, like Flash, sometimes doing asymptotically fewer writes than reads. (2) We extend the lower bounds and optimal algorithms to arbitrary algorithms that may be expressed as perfectly nested loops accessing arrays, where the array subscripts may be arbitrary affine functions of the loop indices (eg A(i), B(i,j+k, k+3*m-7, …) etc.). (3) We extend our communication-avoiding approach to some machine learning algorithms, such as support vector machines. This work has won a number of awards. We also address reproducible floating-point computation. We define reproducibility to mean getting bitwise identical results from multiple runs of the same program, perhaps with different hardware resources or other changes that should ideally not change the answer. Many users depend on reproducibility for debugging or correctness. However, dynamic scheduling of parallel computing resources, combined with nonassociativity of floating point addition, makes attaining reproducibility a challenge even for simple operations like summing a vector of numbers, or more complicated operations like the Basic Linear Algebra Subprograms (BLAS). We describe an algorithm that computes a reproducible sum of floating point numbers, independent of the order of summation. The algorithm depends only on a subset of the IEEE Floating Point Standard 754-2008, uses just 6 words to represent a “reproducible accumulator,” and requires just one read-only pass over the data, or one reduction in parallel. New instructions based on this work are being considered for inclusion in the future IEEE 754-2018 floating-point standard, and new reproducible BLAS are being considered for the next version of the BLAS standard.

97 MATHEMATICS AND COMPUTING↗