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All sources NASA NTRS DOE OSTI

NASA NTRS2011

Visual Object Recognition and Tracking of Tools

A method has been created to automatically build an algorithm off-line, using computer-aided design (CAD) models, and to apply this at runtime. The object type is discriminated, and the position and orientation are identified. This system can work with a single image and can provide improved performance using multiple images provided from videos. The spatial processing unit uses three stages: (1) segmentation; (2) initial type, pose, and geometry (ITPG) estimation; and (3) refined type, pose, and geometry (RTPG) calculation. The image segmentation module files all the tools in an image and isolates them from the background. For this, the system uses edge-detection and thresholding to find the pixels that are part of a tool. After the pixels are identified, nearby pixels are grouped into blobs. These blobs represent the potential tools in the image and are the product of the segmentation algorithm. The second module uses matched filtering (or template matching). This approach is used for condensing synthetic images using an image subspace that captures key information. Three degrees of orientation, three degrees of position, and any number of degrees of freedom in geometry change are included. To do this, a template-matching framework is applied. This framework uses an off-line system for calculating template images, measurement images, and the measurements of the template images. These results are used online to match segmented tools against the templates. The final module is the RTPG processor. Its role is to find the exact states of the tools given initial conditions provided by the ITPG module. The requirement that the initial conditions exist allows this module to make use of a local search (whereas the ITPG module had global scope). To perform the local search, 3D model matching is used, where a synthetic image of the object is created and compared to the sensed data. The availability of low-cost PC graphics hardware allows rapid creation of synthetic images. In this approach, a function of orientation, distance, and articulation is defined as a metric on the difference between the captured image and a synthetic image with an object in the given orientation, distance, and articulation. The synthetic image is created using a model that is looked up in an object-model database. A composable software architecture is used for implementation. Video is first preprocessed to remove sensor anomalies (like dead pixels), and then is processed sequentially by a prioritized list of tracker-identifiers.

English, James↗

NASA NTRS2017

First Order Temporal Logic Monitoring with BDDs

Runtime verification is aimed at analyzing execution traces stemming from a running program or system. The traditional purpose is to detect the lack of conformance with respect to a formal specification. Numerous efforts in the field have focused on monitoring so-called parametric specifications, where events carry data, and formulas can refer to such. Since a monitor for such specifications has to store observed data, the challenge is to have an efficient representation and manipulation of Boolean operators, quantification, and lookup of data. The fundamental problem is that the actual values of the data are not necessarily bounded or provided in advance. In this work we explore the use of Binary Decision Diagrams (BDDs) for representing observed data. Our experiments show a substantial improvement in performance compared to related work.

Ulus, Dogan↗

NASA NTRS1996

An interactive parallel programming environment applied in atmospheric science

This article introduces an interactive parallel programming environment (IPPE) that simplifies the generation and execution of parallel programs. One of the tasks of the environment is to generate message-passing parallel programs for homogeneous and heterogeneous computing platforms. The parallel programs are represented by using visual objects. This is accomplished with the help of a graphical programming editor that is implemented in Java and enables portability to a wide variety of computer platforms. In contrast to other graphical programming systems, reusable parts of the programs can be stored in a program library to support rapid prototyping. In addition, runtime performance data on different computing platforms is collected in a database. A selection process determines dynamically the software and the hardware platform to be used to solve the problem in minimal wall-clock time. The environment is currently being tested on a Grand Challenge problem, the NASA four-dimensional data assimilation system.

Interactive Display Devices↗

DOE OSTI2012

Advanced, Integrated Control for Building Operations to Achieve 40% Energy Saving

We developed and demonstrated a software based integrated advanced building control platform called Smart Energy Box (SEB), which can coordinate building subsystem controls, integrate variety of energy optimization algorithms and provide proactive and collaborative energy management and control for building operations using weather and occupancy information. The integrated control system is a low cost solution and also features: Scalable component based architecture allows to build a solution for different building control system configurations with needed components; Open Architecture with a central data repository for data exchange among runtime components; Extendible to accommodate variety of communication protocols. Optimal building control for central loads, distributed loads and onsite energy resource; uses web server as a loosely coupled way to engage both building operators and building occupants in collaboration for energy conservation. Based on the open platform of SEB, we have investigated and evaluated a variety of operation and energy saving control strategies on Carnegie Mellon University Intelligent Work place which is equipped with alternative cooling/heating/ventilation/lighting methods, including radiant mullions, radiant cooling/heating ceiling panels, cool waves, dedicated ventilation unit, motorized window and blinds, and external louvers. Based on the validation results of these control strategies, they were integrated in SEB in a collaborative and dynamic way. This advanced control system was programmed and computer tested with a model of the Intelligent Workplace's northern section (IW n ). The advanced control program was then installed in the IW n control system; the performance was measured and compared with that of the state of the art control system to verify the overall energy savings great than 40%. In addition advanced human machine interfaces (HMI's) were developed to communicate both with building occupants and the building operator. Lifecycle cost analyses of the advanced building control were performed, and a Building Control System Guide was prepared and published to inform owners, architects, and engineers dealing with new construction or renovation of buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

PIPER: Performance Insight for Programmers and Exascale Runtimes (Final Technical Report)

This project concentrated on the development of novel performance tools and analysis techniques for large scale parallel systems and applications, eventually targeting exascale platforms. Activities conducted included work to novel root cause detection approaches and an auto-tuning framework for parallel systems. The work has led to several software solutions, which are available as open source.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2013

Adaptive Distributed Environment for Procedure Training (ADEPT)

ADEPT (Adaptive Distributed Environment for Procedure Training) is designed to provide more effective, flexible, and portable training for NASA systems controllers. When creating a training scenario, an exercise author can specify a representative rationale structure using the graphical user interface, annotating the results with instructional texts where needed. The author's structure may distinguish between essential and optional parts of the rationale, and may also include "red herrings" - hypotheses that are essential to consider, until evidence and reasoning allow them to be ruled out. The system is built from pre-existing components, including Stottler Henke's SimVentive instructional simulation authoring tool and runtime. To that, a capability was added to author and exploit explicit control decision rationale representations. ADEPT uses SimVentive's Scalable Vector Graphics (SVG)- based interactive graphic display capability as the basis of the tool for quickly noting aspects of decision rationale in graph form. The ADEPT prototype is built in Java, and will run on any computer using Windows, MacOS, or Linux. No special peripheral equipment is required. The software enables a style of student/ tutor interaction focused on the reasoning behind systems control behavior that better mimics proven Socratic human tutoring behaviors for highly cognitive skills. It supports fast, easy, and convenient authoring of such tutoring behaviors, allowing specification of detailed scenario-specific, but content-sensitive, high-quality tutor hints and feedback. The system places relatively light data-entry demands on the student to enable its rationale-centered discussions, and provides a support mechanism for fostering coherence in the student/ tutor dialog by including focusing, sequencing, and utterance tuning mechanisms intended to better fit tutor hints and feedback into the ongoing context.

Domeshek, Eric↗

NASA NTRS2018

Refining the Safety - Liveness Classification of Temporal Properties According to Monitorability

Runtime veriﬁcation is the topic of analyzing execution traces using formal techniques. It includes monitoring the execution of a system against temporal properties, commonly to detect violations. Not every temporal property is fully monitorable however: in some cases, the correctness of the execution does not depend on any ﬁnite preﬁx. We study the connection between monitorability and Lamport’s classiﬁcation of properties to safety and liveness and their dual classes.We reﬁne the deﬁnition of monitorability and provide algorithms to check which verdicts can be expected, a priori and during runtime veriﬁcation.

Peled, Doron↗

DOE OSTI2025

Characterizing the Impact of GPU Power Management on an Exascale System

As GPU-accelerated high-performance computing (HPC) systems approach exascale performance, controlling energy consumption without compromising throughput is essential. Architectures such as the AMD MI250X-based Frontier supercomputer provide runtime mechanisms like frequency and power capping, enabling energy tuning without modifying application code. Although both target energy reduction, they operate via distinct hardware control paths and influence workloads differently. We present a comprehensive evaluation of these strategies on a leadership-class system using diverse HPC proxy applications representative of production workloads. Our study analyzes performance–energy trade-offs across multiple capping levels, node counts (1 and 32), and application profiles. Results show that frequency capping generally achieves higher energy efficiency and scalability, with gains of up to 13.2% without performance loss, while power capping is more effective for single-node runs or bursty GPU utilization. We also provide practical guidelines to help system administrators and users balance energy efficiency and performance in large-scale scientific workloads.

Costa, Mariana [Universidade Federal do Rio Grande↗

DOE OSTI2018

SHAD: the Scalable High-performance Algorithms and Data-structures Library

The unprecedented amount of data that needs to be processed in emerging data analytics applications poses novel challenges to industry and academia. Scalability and high performance become more than a desirable feature because, due to the scale and the nature of the problems, they draw the line between what is achievable and what is unfeasible. In this paper, we propose SHAD, the Scalable High-performance Algorithms and Data-structures library. SHAD adopts a modular design that confines low level details and promotes reuse. SHAD's core is built on an Abstract Runtime Interface which enhances portability and identifies the minimal set of features of the underlying system required by the framework. The core library includes common data-structures such as: Array, Vector, Map and Set. These are designed to accommodate significant amount of data which can be accessed in massively parallel environments, and used as building blocks for SHAD extensions, i.e. higher level software libraries. We have validated and evaluated our design with a performance and scalability study of the core components of the library. We have validated the design flexibility by proposing a Graph Library as an example of SHAD extension, which implements two different graph data-structures; we evaluate their performance with a set of graph applications. Experimental results show that the approach is promising in terms of both performance and scalability. On a distributed system with 320 cores, SHAD Arrays are able to sustain a throughput of 65 billion operations per second, while SHAD Maps sustain 1 billion of operations per second. Algorithms implemented using the Graph Library exhibit performance and scalability comparable to a custom solution, but with smaller development effort.

Castellana, Vito G.↗

DOE OSTI2025

Investigation into the Performance Benefits of Exposing Network Backpressure in UPC++ and GASNet-EX

This document is a brief summary of the research, and supporting development efforts, conducted by the project "Investigation into Improving Dynamic Adaptivity to System-Level Asynchrony in UPC++". We tested the hypothesis "The UPC++ and GASNet-EX runtimes can expose information from the network stack that enables applications to dynamically adapt to congestion, improving total throughput". We present experimental results from both a microbenchmark and an application benchmark that support this hypothesis.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Ground and excited state gradients with end-to-end differentiable semiempirical quantum chemistry

Accurate and efficient gradients of molecular energy with respect to nuclear degrees of freedom are essential for geometry optimization and molecular dynamics, including simulations that go beyond the Born–Oppenheimer regime. A common approach involves deriving analytical formulas for new electronic structure methods, which is often conceptually difficult and requires tedious coding. Here, we implement analytical, semi-numerical, and automatic differentiation (AD)-based gradient pathways for semiempirical Hamiltonian models in the PYSEQM software package, leveraging both graphics processing unit (GPU) and central processing unit (CPU) architectures. We further extend these capabilities to excited states calculated using the configuration interaction singles and time-dependent Hartree–Fock ansätze. We benchmark wall time, peak memory usage, and accuracy across three molecular families of varying chemical complexity, including systems of up to a thousand atoms. For ground-state simulations, analytical and AD gradients achieve near-identical GPU runtimes, while semi-numerical gradients are slower on GPU but remain competitive on CPU. For excited states, both analytical and custom AD approaches using implicit differentiation show similar performance and low memory requirements, whereas gradients with full AD are memory-limited. AD gradients match analytical ones in accuracy across all tested systems, aided by a quaternion-based diatomic frame rotation for two-center quantities that ensures smooth energy surfaces. Overall, automatic differentiation emerges as a practical alternative to analytical gradients in semiempirical quantum chemistry, offering high accuracy while allowing seamless integration in AI-driven workflows and popular packages, such as PyTorch and JAX. Our results provide actionable guidance for selecting optimal gradient strategies in large-scale ground- and excited-state molecular dynamics simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2010

Adaptive Fault Tolerance for Many-Core Based Space-Borne Computing

This paper describes an approach to providing software fault tolerance for future deep-space robotic NASA missions, which will require a high degree of autonomy supported by an enhanced on-board computational capability. Such systems have become possible as a result of the emerging many-core technology, which is expected to offer 1024-core chips by 2015. We discuss the challenges and opportunities of this new technology, focusing on introspection-based adaptive fault tolerance that takes into account the specific requirements of applications, guided by a fault model. Introspection supports runtime monitoring of the program execution with the goal of identifying, locating, and analyzing errors. Fault tolerance assertions for the introspection system can be provided by the user, domain-specific knowledge, or via the results of static or dynamic program analysis. This work is part of an on-going project at the Jet Propulsion Laboratory in Pasadena, California.

fault tolerance↗

DOE OSTI2014

Technology Solutions Case Study: Boiler Control Replacement for Hydronically Heated Multifamily Buildings, Cambridge, Massachusetts

The ARIES Collaborative, a U.S. Department of Energy Building America research team, partnered with NeighborWorks America affiliate Homeowners' Rehab Inc. (HRI) of Cambridge, Massachusetts, to study improvements to the central hydronic heating system in one of the nonprofit's housing developments. The heating controls in the three-building, 42-unit Columbia Cambridge Alliance for Spanish Tenants housing development were upgraded. Fuel use in the development was excessive compared to similar properties. A poorly insulated thermal envelope contributed to high energy bills, but adding wall insulation was not cost-effective or practical. The more cost-effective option was improving heating system efficiency, which faced several obstacles, including inflexible boiler controls and failed thermostatic radiator valves. Boiler controls were replaced with systems that offer temperature setbacks and one that controls heat based on apartment temperature in addition to outdoor temperature. Utility bill analysis shows that post-retrofit weather-normalized heating energy use was reduced by 10%-31% (average of 19%). Indoor temperature cutoff reduced boiler runtime (and therefore heating fuel consumption) by 28% in the one building in which it was implemented. Nearly all savings were obtained during night which had a lower indoor temperature cut off (68°F) than day (73° F). This implies that the outdoor reset curve was appropriately adjusted for this building for daytime operation. Nighttime setback of heating system supply water temperature had no discernable impact on boiler runtime or gas bills.

ARIES↗

DOE OSTI2014

The Wind Forecast Improvement Project (WFIP). A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations -- the Northern Study Area

This report contains the results from research aimed at improving short-range (0-6 hour) hub-height wind forecasts in the NOAA weather forecast models through additional data assimilation and model physics improvements for use in wind energy forecasting. Additional meteorological observing platforms including wind profilers, sodars, and surface stations were deployed for this study by NOAA and DOE, and additional meteorological data at or near wind turbine hub height were provided by South Dakota State University and WindLogics/NextEra Energy Resources over a large geographical area in the U.S. Northern Plains for assimilation into NOAA research weather forecast models. The resulting improvements in wind energy forecasts based on the research weather forecast models (with the additional data assimilation and model physics improvements) were examined in many different ways and compared with wind energy forecasts based on the current operational weather forecast models to quantify the forecast improvements important to power grid system operators and wind plant owners/operators participating in energy markets. Two operational weather forecast models (OP_RUC, OP_RAP) and two research weather forecast models (ESRL_RAP, HRRR) were used as the base wind forecasts for generating several different wind power forecasts for the NextEra Energy wind plants in the study area. Power forecasts were generated from the wind forecasts in a variety of ways, from very simple to quite sophisticated, as they might be used by a wide range of both general users and commercial wind energy forecast vendors. The error characteristics of each of these types of forecasts were examined and quantified using bulk error statistics for both the local wind plant and the system aggregate forecasts. The wind power forecast accuracy was also evaluated separately for high-impact wind energy ramp events. The overall bulk error statistics calculated over the first six hours of the forecasts at both the individual wind plant and at the system-wide aggregate level over the one year study period showed that the research weather model-based power forecasts (all types) had lower overall error rates than the current operational weather model-based power forecasts, both at the individual wind plant level and at the system aggregate level. The bulk error statistics of the various model-based power forecasts were also calculated by season and model runtime/forecast hour as power system operations are more sensitive to wind energy forecast errors during certain times of year and certain times of day. The results showed that there were significant differences in seasonal forecast errors between the various model-based power forecasts. The results from the analysis of the various wind power forecast errors by model runtime and forecast hour showed that the forecast errors were largest during the times of day that have increased significance to power system operators (the overnight hours and the morning/evening boundary layer transition periods), but the research weather model-based power forecasts showed improvement over the operational weather model-based power forecasts at these times.

17 WIND ENERGY↗

DOE OSTI2014

Hydronic Heating Retrofits for Low-Rise Multifamily Buildings: Boiler Control Replacement and Monitoring

The ARIES Collaborative, a U.S. Department of Energy Building America research team, partnered with NeighborWorks America affiliate Homeowners' Rehab Inc. (HRI) of Cambridge, Massachusetts, to study improvements to the central hydronic heating system in one of the nonprofit's housing developments. The heating controls in the three-building, 42-unit Columbia Cambridge Alliance for Spanish Tenants housing development were upgraded. Fuel use in the development was excessive compared to similar properties. A poorly insulated thermal envelope contributed to high energy bills, but adding wall insulation was not cost-effective or practical. The more cost-effective option was improving heating system efficiency. Efficient operation of the heating system faced several obstacles, including inflexible boiler controls and failed thermostatic radiator valves. Boiler controls were replaced with systems that offer temperature setbacks and one that controls heat based on apartment temperature in addition to outdoor temperature. Utility bill analysis shows that post-retrofit weather-normalized heating energy use was reduced by 10%-31% (average of 19%). Indoor temperature cutoff reduced boiler runtime (and therefore heating fuel consumption) by 28% in the one building in which it was implemented. Nearly all savings were obtained during night which had a lower indoor temperature cut off (68 degrees F) than day (73 degrees F). This implies that the outdoor reset curve was appropriately adjusted for this building for daytime operation. Nighttime setback of heating system supply water temperature had no discernable impact on boiler runtime or gas bills.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2014

Building America Case Study: Boiler Control Replacement for Hydronically Heated Multifamily Buildings, Cambridge, Massachusetts (Fact Sheet)

The ARIES Collaborative, a U.S. Department of Energy Building America research team, partnered with NeighborWorks America affiliate Homeowners' Rehab Inc. (HRI) of Cambridge, Massachusetts, to study improvements to the central hydronic heating system in one of the nonprofit's housing developments. The heating controls in the three-building, 42-unit Columbia Cambridge Alliance for Spanish Tenants housing development were upgraded. Fuel use in the development was excessive compared to similar properties. A poorly insulated thermal envelope contributed to high energy bills, but adding wall insulation was not cost-effective or practical. The more cost-effective option was improving heating system efficiency. Efficient operation of the heating system faced several obstacles, including inflexible boiler controls and failed thermostatic radiator valves. Boiler controls were replaced with systems that offer temperature setbacks and one that controls heat based on apartment temperature in addition to outdoor temperature. Utility bill analysis shows that post-retrofit weather-normalized heating energy use was reduced by 10%-31% (average of 19%). Indoor temperature cutoff reduced boiler runtime (and therefore heating fuel consumption) by 28% in the one building in which it was implemented. Nearly all savings were obtained during night which had a lower indoor temperature cut off (68 degrees F) than day (73 degrees F). This implies that the outdoor reset curve was appropriately adjusted for this building for daytime operation. Nighttime setback of heating system supply water temperature had no discernable impact on boiler runtime or gas bills.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2016

TH-EF-BRB-05: 4pi Non-Coplanar IMRT Beam Angle Selection by Convex Optimization with Group Sparsity Penalty

Purpose: Integrated beam orientation and fluence map optimization is expected to be the foundation of robust automated planning but existing heuristic methods do not promise global optimality. We aim to develop a new method for beam angle selection in 4π non-coplanar IMRT systems based on solving (globally) a single convex optimization problem, and to demonstrate the effectiveness of the method by comparison with a state of the art column generation method for 4π beam angle selection. Methods: The beam angle selection problem is formulated as a large scale convex fluence map optimization problem with an additional group sparsity term that encourages most candidate beams to be inactive. The optimization problem is solved using an accelerated first-order method, the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). The beam angle selection and fluence map optimization algorithm is used to create non-coplanar 4π treatment plans for several cases (including head and neck, lung, and prostate cases) and the resulting treatment plans are compared with 4π treatment plans created using the column generation algorithm. Results: In our experiments the treatment plans created using the group sparsity method meet or exceed the dosimetric quality of plans created using the column generation algorithm, which was shown superior to clinical plans. Moreover, the group sparsity approach converges in about 3 minutes in these cases, as compared with runtimes of a few hours for the column generation method. Conclusion: This work demonstrates the first non-greedy approach to non-coplanar beam angle selection, based on convex optimization, for 4π IMRT systems. The method given here improves both treatment plan quality and runtime as compared with a state of the art column generation algorithm. When the group sparsity term is set to zero, we obtain an excellent method for fluence map optimization, useful when beam angles have already been selected. NIH R43CA183390, NIH R01CA188300, Varian Medical Systems; Part of this research took place while D. O’Connor was a summer intern at RefleXion Medical.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

Aspen-based performance and energy modeling frameworks

Now with the anticipation of exascale architectures, energy consumption is becoming one of the critical design parameters, especially in light of the energy budget of 20-30 Megawatts set by the U.S. Department of Energy. Understanding an application’s execution pattern and its energy footprint is critical to improving application operation on a diverse heterogeneous architecture. Applying application-specific performance optimization can consequently improve energy consumption. However, this approach is only applicable to current systems. As we enter a new era of exascale architectures that is projected to contain more complex memory hierarchies, increased levels of parallelism, heterogeneity in hardware, and complex programming models and techniques, energy and performance management is getting more cumbersome. We therefore propose techniques that predict the energy consumption beforehand or at runtime to enable proactive tuning. Such energy prediction approaches must be generic and adapt themselves at runtime to changing application and hardware configurations. Most existing energy estimation and prediction approaches are empirical in nature and thus tied to current systems. To overcome this limitation, we propose two energy estimation techniques: ACEE (Algorithmic and Categorical Energy Estimation), which uses a combination of analytical and empirical modeling techniques; and AEEM (Aspen’s Embedded Energy Estimation), a system-level analytical energy estimation technique. Both of these models incorporate the Aspen domain specific language for performance modeling. We present the methodologies of these two models and test their accuracy using five proxy applications. We also describe three use cases.

79 ASTRONOMY AND ASTROPHYSICS↗