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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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At least 541 records · Page 30

All sources NASA NTRS DOE OSTI

NASA NTRS2006

ASPEN Version 3.0

The Automated Scheduling and Planning Environment (ASPEN) computer program has been updated to version 3.0. ASPEN is a modular, reconfigurable, application software framework for solving batch problems that involve reasoning about time, activities, states, and resources. Applications of ASPEN can include planning spacecraft missions, scheduling of personnel, and managing supply chains, inventories, and production lines. ASPEN 3.0 can be customized for a wide range of applications and for a variety of computing environments that include various central processing units and random access memories.

Rabideau, Gregg↗

NASA NTRS1992

A principled approach to the measurement of situation awareness in commercial aviation

The issue of how to support situation awareness among crews of modern commercial aircraft is becoming especially important with the introduction of automation in the form of sophisticated flight management computers and expert systems designed to assist the crew. In this paper, cognitive theories are discussed that have relevance for the definition and measurement of situation awareness. These theories suggest that comprehension of the flow of events is an active process that is limited by the modularity of attention and memory constraints, but can be enhanced by expert knowledge and strategies. Three implications of this perspective for assessing and improving situation awareness are considered: (1) Scenario variations are proposed that tax awareness by placing demands on attention; (2) Experimental tasks and probes are described for assessing the cognitive processes that underlie situation awareness; and (3) The use of computer-based human performance models to augment the measures of situation awareness derived from performance data is explored. Finally, two potential example applications of the proposed assessment techniques are described, one concerning spatial awareness using wide field of view displays and the other emphasizing fault management in aircraft systems.

Tenney, Yvette J.↗

DOE OSTI2019

Oblivion Landscapes - 19008

This paper and presentation reflect on proposals that may offer ways to integrate hidden landscapes - such as the ones that house nuclear waste or other toxic/hazardous conditions - into concepts of collective memory, and mental/perceptive maps of populations - with the larger goal of creating new levels of awareness, intelligence and operability regarding their existence and potential future role in civilization. The proposals belong to a series of Architecture Design Studios called and dedicated to the theme of 'Planetology', within which is inserted the theme of Massive Oblivion Landscapes. In the context of this Symposium, these reflections may help rethink how to design modes of communication that lay out concepts of danger, waste and cultural memory. And, in this way, reclaim for these infrastructural landscapes, the role of information rich and culturally relevant typologies. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2019

Application health monitoring for extreme-scale resiliency using cooperative fault management

Resiliency is and will be a critical factor in determining scientific productivity on current and exascale supercomputers, and beyond. Applications oblivious to and incapable of handling transient soft and hard errors could waste supercomputing resources or, worse, yield misleading scientific insights. In this work, we introduce a novel application-driven silent error detection and recovery strategy based on application health monitoring. Our methodology uses application output that follows known patterns, as indicators of an application's health and knowledge that violation of these patterns could be indication of faults. Information from system monitors that report hardware and software health status is used to corroborate faults. Collectively, this information is used by a fault coordinator agent to take preventive and corrective measures by applying computational steering to an application between checkpoints. This cooperative fault management system uses the Fault Tolerance Backplane as a communication channel. The benefits of this framework are demonstrated with two real application case studies, molecular dynamics, and quantum chemistry simulations, on scalable clusters with simulated memory and I/O corruptions. Lastly, the developed approach is general and can be easily applied to other applications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Warming of the Willamette River, 1850–present: the effects of climate change and river system alterations

Abstract. Using archival research methods, we recovered and combined data from multiple sources to produce a unique, 140-year record of daily water temperature (Tw) in the lower Willamette River, Oregon (1881–1890, 1941–present). Additional daily weather and river flow records from the 1850s onwards are used to develop and validate a statistical regression model of Tw for 1850–2020. The model simulates the time-lagged response of Tw to air temperature and river flow and is calibrated for three distinct time periods: the late 19th, mid-20th, and early 21st centuries. Results show that Tw has trended upwards at 1.1 ∘C per century since the mid-19th century, with the largest shift in January and February (1.3 ∘C per century) and the smallest in May and June (∼ 0.8 ∘C per century). The duration that the river exceeds the ecologically important threshold of 20 ∘C has increased by about 20 d since the 1800s, to about 60 d yr−1. Moreover, cold-water days below 2 ∘C have virtually disappeared, and the river no longer freezes. Since 1900, changes are primarily correlated with increases in air temperature (Tw increase of 0.81 ± 0.25 ∘C) but also occur due to alterations in the river system such as depth increases from reservoirs (0.34 ± 0.12 ∘C). Managed release of water affects Tw seasonally, with an average reduction of up to 0.56 ∘C estimated for September. River system changes have decreased variability (σ) in daily minimum Tw by 0.44 ∘C, increased thermal memory, reduced interannual variability, and reduced the response to short-term meteorological forcing (e.g., heat waves). These changes fundamentally alter the response of Tw to climate change, posing additional stressors on fauna.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1980

Optical mass memory system (AMM-13). AMM-13 system segment specification

The performance, design, development, and test requirements for an optical mass data storage and retrieval system prototype (AMM-13) are established. This system interfaces to other system segments of the NASA End-to-End Data System via the Data Base Management System segment and is designed to have a storage capacity of 10 to the 13th power bits (10 to the 12th power bits on line). The major functions of the system include control, input and output, recording of ingested data, fiche processing/replication and storage and retrieval.

Bailey, G. A.↗

DOE OSTI2016

Processor register error correction management

Processor register protection management is disclosed. In embodiments, a method of processor register protection management can include determining a sensitive logical register for executable code generated by a compiler, generating an error-correction table identifying the sensitive logical register, and storing the error-correction table in a memory accessible by a processor. The processor can be configured to generate a duplicate register of the sensitive logical register identified by the error-correction table.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1972

Modular space station, phase B extension. Information management advanced development. Volume 4: Data processing assembly

The computation and logical functions which are performed by the data processing assembly of the modular space station are defined. The subjects discussed are: (1) requirements analysis, (2) baseline data processing assembly configuration, (3) information flow study, (4) throughput simulation, (5) redundancy study, (6) memory studies, and (7) design requirements specification.

Gerber, C. R.↗

DOE OSTI2014

Evict on write, a management strategy for a prefetch unit and/or first level cache in a multiprocessor system with speculative execution

In a multiprocessor system with at least two levels of cache, a speculative thread may run on a core processor in parallel with other threads. When the thread seeks to do a write to main memory, this access is to be written through the first level cache to the second level cache. After the write though, the corresponding line is deleted from the first level cache and/or prefetch unit, so that any further accesses to the same location in main memory have to be retrieved from the second level cache. The second level cache keeps track of multiple versions of data, where more than one speculative thread is running in parallel, while the first level cache does not have any of the versions during speculation. A switch allows choosing between modes of operation of a speculation blind first level cache.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

EqualChance: Addressing Intra-set Write Variation to Increase Lifetime of Non-volatile Caches

To address the limitations of SRAM such as high-leakage and low-density, researchers have explored use of non-volatile memory (NVM) devices, such as ReRAM (resistive RAM) and STT-RAM (spin transfer torque RAM) for designing on-chip caches. A crucial limitation of NVMs, however, is that their write endurance is low and the large intra-set write variation introduced by existing cache management policies may further exacerbate this problem, thereby reducing the cache lifetime significantly. We present EqualChance, a technique to increase cache lifetime by reducing intra-set write variation. EqualChance works by periodically changing the physical cache-block location of a write-intensive data item within a set to achieve wear-leveling. Simulations using workloads from SPEC CPU2006 suite and HPC (high-performance computing) field show that EqualChance improves the cache lifetime by 4.29X. Also, its implementation overhead is small, and it incurs very small performance and energy loss.

cache lifetime↗

NASA NTRS2010

Kepler Science Operations Center Pipeline Framework

The Kepler mission is designed to continuously monitor up to 170,000 stars at a 30 minute cadence for 3.5 years searching for Earth-size planets. The data are processed at the Science Operations Center (SOC) at NASA Ames Research Center. Because of the large volume of data and the memory and CPU-intensive nature of the analysis, significant computing hardware is required. We have developed generic pipeline framework software that is used to distribute and synchronize the processing across a cluster of CPUs and to manage the resulting products. The framework is written in Java and is therefore platform-independent, and scales from a single, standalone workstation (for development and research on small data sets) to a full cluster of homogeneous or heterogeneous hardware with minimal configuration changes. A plug-in architecture provides customized control of the unit of work without the need to modify the framework itself. Distributed transaction services provide for atomic storage of pipeline products for a unit of work across a relational database and the custom Kepler DB. Generic parameter management and data accountability services are provided to record the parameter values, software versions, and other meta-data used for each pipeline execution. A graphical console allows for the configuration, execution, and monitoring of pipelines. An alert and metrics subsystem is used to monitor the health and performance of the pipeline. The framework was developed for the Kepler project based on Kepler requirements, but the framework itself is generic and could be used for a variety of applications where these features are needed.

Klaus, Todd C.↗

DOE OSTI2022

Machine Learning Assisted Reservoir Operation Model for Long–Term Water Management Simulation

This study explores strategies for long-term reservoir simulations by combining generic rule-based reservoir management model (RMM) and machine learning (ML) models for two major multipurpose reservoirs — Allatoona Lake and Lake Sidney Lanier in the southeastern United States. First, a standalone RMM is developed to simulate daily release and storage during Water Year 1981–2015. Next, using Long-Short Term Memory (LSTM) as the ML technique, a standalone LSTM model is trained based on reservoir inflow and meteorological observations to simulate reservoir release and estimate reservoir storage through water balance calculation. Three hybrid modeling strategies are developed, one using RMM output as an additional LSTM input (H1), another using LSTM as the initial release estimate in RMM (H2), and the third combining the first two strategies (H3). The Nash–Sutcliffe efficiency (NSE) for release (NSE-r), storage (NSE-s), and their mean (NSE-avg) are used for model evaluation. Overall, H1 improves NSE-r to 0.65 and 0.54 for Allatoona and Lanier, respectively, compared to standalone RMM (0.44 and 0.21); however, its storage trajectory did not produce a physically feasible solution, similar to LSTM. H2 and especially H3 show that they can retain the best features from RMM and LSTM, with H3 NSE-avg being 0.695 and 0.55 for Allatoona and Lanier outperforming RMM (0.615 and 0.29). In conclusion, the findings suggest a robust simulation capacity for large-scale water management in future studies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

FLYING SERVING: On-the-Fly Parallelism Switching for Large Language Model Serving

Production LLM serving must simultaneously deliver high throughput, low latency, and sufficient context capacity under non-stationary traffic and mixed request requirements. Data parallelism (DP) maximizes throughput by running independent replicas, while tensor parallelism (TP) reduces per-request latency and pools memory for long-context inference. However, existing serving stacks typically commit to a static parallelism configuration at deployment; adapting to bursts, priorities, or long-context requests is often disruptive and slow. We present Flying Serving, a vLLM-based system that enables online DP-TP switching without restarting engine workers. Flying Serving makes reconfiguration practical by virtualizing the state that would otherwise force data movement: (i) a zero-copy Model Weights Manager that exposes TP shard views on demand, (ii) a KV Cache Adaptor that preserves request KV state across DP/TP layouts, (iii) an eagerly initialized Communicator Pool to amortize collective setup, and (iv) a deadlock-free scheduler that coordinates safe transitions under execution skew. Across three popular LLMs and realistic serving scenarios, Flying Serving improves performance by up to 4.79 × under high load and 3.47 × under low load while supporting latency- and memory-driven requests.

Gao, Shouwei [ORNL]↗

DOE OSTI2017

Deploy Nalu/Kokkos algorithmic infrastructure with performance benchmarking.

The former Nalu interior heterogeneous algorithm design, which was originally designed to manage matrix assembly operations over all elemental topology types, has been modified to operate over homogeneous collections of mesh entities. This newly templated kernel design allows for removal of workset variable resize operations that were formerly required at each loop over a Sierra ToolKit (STK) bucket (nominally, 512 entities in size). Extensive usage of the Standard Template Library (STL) std::vector has been removed in favor of intrinsic Kokkos memory views. In this milestone effort, the transition to Kokkos as the underlying infrastructure to support performance and portability on many-core architectures has been deployed for key matrix algorithmic kernels. A unit-test driven design effort has developed a homogeneous entity algorithm that employs a team-based thread parallelism construct. The STK Single Instruction Multiple Data (SIMD) infrastructure is used to interleave data for improved vectorization. The collective algorithm design, which allows for concurrent threading and SIMD management, has been deployed for the core low-Mach element- based algorithm. Several tests to ascertain SIMD performance on Intel KNL and Haswell architectures have been carried out. The performance test matrix includes evaluation of both low- and higher-order methods. The higher-order low-Mach methodology builds on polynomial promotion of the core low-order control volume nite element method (CVFEM). Performance testing of the Kokkos-view/SIMD design indicates low-order matrix assembly kernel speed-up ranging between two and four times depending on mesh loading and node count. Better speedups are observed for higher-order meshes (currently only P=2 has been tested) especially on KNL. The increased workload per element on higher-order meshes bene ts from the wide SIMD width on KNL machines. Combining multiple threads with SIMD on KNL achieves a 4.6x speedup over the baseline, with assembly timings faster than that observed on Haswell architecture. The computational workload of higher-order meshes, therefore, seems ideally suited for the many-core architecture and justi es further exploration of higher-order on NGP platforms. A Trilinos/Tpetra-based multi-threaded GMRES preconditioned by symmetric Gauss Seidel (SGS) represents the core solver infrastructure for the low-Mach advection/diffusion implicit solves. The threaded solver stack has been tested on small problems on NREL's Peregrine system using the newly developed and deployed Kokkos-view/SIMD kernels. fforts are underway to deploy the Tpetra-based solver stack on NERSC Cori system to benchmark its performance at scale on KNL machines.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Predictor-corrector models for lightweight massive machine-type communications in Industry 4.0

Future Industry 4.0 scenarios are characterized by seamless integration between computational and physical processes. To achieve this objective, dense platforms made of small sensing nodes and other resource constraint devices are ubiquitously deployed. All these devices have a limited number of computational resources, just enough to perform the simple operation they are in charge of. The remaining operations are delegated to powerful gateways that manage sensing nodes, but resources are never unlimited, and as more and more devices are deployed on Industry 4.0 platforms, gateways present more problems to handle massive machine-type communications. Although the problems are diverse, those related to security are especially critical. To enable sensing nodes to establish secure communications, several semiconductor companies are currently promoting a new generation of devices based on Physical Unclonable Functions, whose usage grows every year in many real industrial scenarios. Those hardware devices do not consume any computational resource but force the gateway to keep large key-value catalogues for each individual node. In this context, memory usage is not scalable and processing delays increase exponentially with each new node on the platform. In this paper, we address this challenge through predictor-corrector models, representing the key-value catalogues. Models are mathematically complex, but we argue that they consume less computational resources than current approaches. The lightweight models are based on complex functions managed as Laurent series, cubic spline interpolations, and Boolean functions also developed as series. Unknown parameters in these models are predicted, and eventually corrected to calculate the output value for each given key. The initial parameters are based on the Kane Yee formula. An experimental analysis and a performance evaluation are provided in the experimental section, showing that the proposed approach causes a significant reduction in the resource consumption.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Evaluating Awkward Arrays, uproot, and coffea as a query platform for High Energy Physics Data

Query languages for High Energy Physics (HEP) are an ever present topic within the field. A query language that can efficiently represent the nested data structures that encode the statistical and physical meaning of HEP data will help analysts by ensuring their code is more clear and pertinent. As the result of a multi-year effort to develop an in-memory columnar representation of high energy physics data, the NumPy, Awkward Array, and uproot Python packages present a mature and efficient interface to HEP data. Atop that base, the coffea package adds functionality to launch queries at scale, manage and apply experiment-specific transformations to data, and present a rich object-oriented columnar data representation to the analyst. Recently, a set of Analysis Description Language (ADL) benchmarks has been established to compare HEP queries in multiple languages and frameworks. In this paper we present these benchmark queries implemented within the coffea framework and discuss their readability and performance characteristics. We find that the columnar queries perform as well or better than the implementations given in previous studies.

Gray, L.↗

NASA NTRS2011

Sptrace

Sptrace is a general-purpose space utilization tracing system that is conceptually similar to the commercial Purify product used to detect leaks and other memory usage errors. It is designed to monitor space utilization in any sort of heap, i.e., a region of data storage on some device (nominally memory; possibly shared and possibly persistent) with a flat address space. This software can trace usage of shared and/or non-volatile storage in addition to private RAM (random access memory). Sptrace is implemented as a set of C function calls that are invoked from within the software that is being examined. The function calls fall into two broad classes: (1) functions that are embedded within the heap management software [e.g., JPL's SDR (Simple Data Recorder) and PSM (Personal Space Management) systems] to enable heap usage analysis by populating a virtual time-sequenced log of usage activity, and (2) reporting functions that are embedded within the application program whose behavior is suspect. For ease of use, these functions may be wrapped privately inside public functions offered by the heap management software. Sptrace can be used for VxWorks or RTEMS realtime systems as easily as for Linux or OS/X systems.

Burleigh, Scott C.↗

NASA NTRS2015

Computational Modeling and Experimental Characterization of Martensitic Transformations in Nicoal for Self-Sensing Materials

Fundamental changes to aero-vehicle management require the utilization of automated health monitoring of vehicle structural components. A novel method is the use of self-sensing materials, which contain embedded sensory particles (SP). SPs are micron-sized pieces of shape-memory alloy that undergo transformation when the local strain reaches a prescribed threshold. The transformation is a result of a spontaneous rearrangement of the atoms in the crystal lattice under intensified stress near damaged locations, generating acoustic waves of a specific spectrum that can be detected by a suitably placed sensor. The sensitivity of the method depends on the strength of the emitted signal and its propagation through the material. To study the transition behavior of the sensory particle inside a metal matrix under load, a simulation approach based on a coupled atomistic-continuum model is used. The simulation results indicate a strong dependence of the particle's pseudoelastic response on its crystallographic orientation with respect to the loading direction and suggest possible ways of optimizing particle sensitivity. The technology of embedded sensory particles will serve as the key element in an autonomous structural health monitoring system that will constantly monitor for damage initiation in service, which will enable quick detection of unforeseen damage initiation in real-time and during onground inspections.

Wallace, T. A.↗