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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 253 records · Page 14

All sources NASA NTRS DOE OSTI

NASA NTRS2016

Share Data with OPeNDAP Hyrax: New Features and Improvements

During the upcoming Summer 2016 meeting of the ESIP Federation (July 19-22), OpenDAP will hold a Developers and Users Workshop. While a broad set of topics will be covered, a key focus is capitalizing on recent EOSDIS-sponsored advances in Hyrax, OPeNDAPs own software for server-side realization of the DAP2 and DAP4 protocols. These Hyrax advances are as important to data users as to data providers, and the workshop will include hands-on experiences of value to both. Specifically, a balanced set of presentations and hands-on tutorials will address advances in 1. server installation, 2. server configuration, 3. Hyrax aggregation capabilities, 4. support for data-access from clients that are HTTP-based, JSON-based or OGC-compliant (especially WCS and WMS), 5. support for DAP4,6.use and extension of server-side computational capabilities, and7.several performance-affecting matters. Topics 2 through 7 will be relevant to data consumers, data providers and notably, due to the open-source nature of all OPeNDAP software to developers wishing to extend Hyrax, to build compatible clients and servers, and/or to employ Hyrax as middleware that enables interoperability across a variety of end-user and source-data contexts. A session for contributed talks will elaborate the topics listed above and embrace additional ones.

OPeNDAP↗

NASA NTRS2018

System and Method for Providing a Climate Data Persistence Service

A system, method and computer-readable storage devices for providing a climate data persistence service. A system configured to provide the service can include a climate data server that performs data and metadata storage and management functions for climate data objects, a compute-storage platform that provides the resources needed to support a climate data server, provisioning software that allows climate data server instances to be deployed as virtual climate data servers in a cloud computing environment, and a service interface, wherein persistence service capabilities are invoked by software applications running on a client device. The climate data objects can be in various formats, such as International Organization for Standards (ISO) Open Archival Information System (OAIS) Reference Model Submission Information Packages, Archive Information Packages, and Dissemination Information Packages. The climate data server can enable scalable, federated storage, management, discovery, and access, and can be tailored for particular use cases.

Schnase, John L.↗

NASA NTRS2016

WebGeocalc: Web Interface to SPICE

The WebGeocalc tool (WGC) provides a web-based graphical user interface to many of the observation geometry calculations available from the “SPICE” system. WGC is based on client-server architecture. A user selects calculation parameters using GUI widgets in a Web browser. The request is sent to a WGC geometry server that performs the calculation and sends results back to the client. WGC can compute various geometric parameters such as positions, orientations, and surface intercept coordinates for a series of times; perform geometric event searches for time intervals when a geometric condition is met; and perform time conversions. WGC offers many features such as plotting and downloading results. Currently only the NAIF group at JPL operates a WGC server that has access to all data on the NAIF web site but WGC can be deployed on any computer that has Java, Apache Tomcat server, MySQL Community Server, and SPICE data.

Semenov, Boris↗

NASA NTRS2016

WebGeocalc: Web Interface to SPICE

Semenov, Boris↗

DOE OSTI2012

ShadowNet: An Active Defense Infrastructure for Insider Cyber Attack Prevention

The ShadowNet infrastructure for insider cyber attack prevention is comprised of a tiered server system that is able to dynamically redirect dangerous/suspicious network traffic away from production servers that provide web, ftp, database and other vital services to cloned virtual machines in a quarantined environment. This is done transparently from the point of view of both the attacker and normal users. Existing connections, such as SSH sessions, are not interrupted. Any malicious activity performed by the attacker on a quarantined server is not reflected on the production server. The attacker is provided services from the quarantined server, which creates the impression that the attacks performed are successful. The activities of the attacker on the quarantined system are able to be recorded much like a honeypot system for forensic analysis.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2014

Distributed road assessment system

A system that detects damage on or below the surface of a paved structure or pavement is provided. A distributed road assessment system includes road assessment pods and a road assessment server. Each road assessment pod includes a ground-penetrating radar antenna array and a detection system that detects road damage from the return signals as the vehicle on which the pod is mounted travels down a road. Each road assessment pod transmits to the road assessment server occurrence information describing each occurrence of road damage that is newly detected on a current scan of a road. The road assessment server maintains a road damage database of occurrence information describing the previously detected occurrences of road damage. After the road assessment server receives occurrence information for newly detected occurrences of road damage for a portion of a road, the road assessment server determines which newly detected occurrences correspond to which previously detected occurrences of road damage.

47 OTHER INSTRUMENTATION↗

DOE OSTI2011

Linux-Based Software Control for Xenon Systems

This paper will review software control solutions for systems requiring automated control and analysis. Written in the C++ language under the Linux operating system (OS), a client/server architecture was developed for automated systems enabling modularized processes to perform system control. In this scheme, narrowly focused clients and servers communicate with each other to perform tasks necessary for system execution. Each hardware interface, such as analog input/output modules, instrumentation, and detectors, have a server associated with it that can be accessed individually with various client interface programs. These servers also inherently support reporting all sensor values to a state-of-health server. One advantage to this software architecture is that all software servers can be accessed and altered independently without stopping the entire control system providing flexibility during development and operation. Automation is achieved using a state-machine based control program that implements the process necessary to run the system. The state-machine provides a well-defined process flow with each state defining one step in the overall control process. The state-machine framework is highly flexible. During development, states can easily be modified, added, or removed as needed.

Suarez, Reynold↗

DOE OSTI2019

Partitioned key-value store with one-sided communications for secondary global key lookup by range-knowledgeable clients

Partitioned key-value stores with one-sided communications are provided for secondary global key lookup by range-knowledgeable clients. A secondary global lookups is performed in a partitioned key-value store by receiving a request from an application for a secondary global key; accessing metadata to identify a first server storing the secondary global key; instructing the identified first server to store a first value corresponding to the requested secondary global key into a memory address of the client; and returning program control to the application, wherein the first server identifies a primary key corresponding to the requested secondary global key and instructs a second server storing the corresponding primary key to store a second value corresponding to the primary key into the memory address of the client. The second server stores the second value corresponding to the primary key into the first memory address of the client.

Bent, John M.↗

DOE OSTI2015

Knowledge management information tool analytics with distributed database engine - 15737

The D and D KM-IT is a web-based knowledge management information tool custom built for the deactivation and decommissioning (D and D) user community. The system allows interested users to post information which goes through a custom work flow. To better improve the performance and user experience, the analysis of server logs is being conducted using MongoDB. MongoDB is a document database that provides high performance, high availability, and easy scalability. Objectives: - Feasibility study of Distributed Database Engine, MongoDB for web analytics; - Analyze server logs to evaluate D and D KM-IT module usage; - Generate useful information from the analysis. Method: - The server logs were loaded and stored in MongoDB for analysis; - Analysis and visualization was performed using NodeJS and Java Script library. Results: - It was possible to store log files in MongoDB; - Log files with different time stamps and groups from different servers can be aggregated; - Queries can be written to extract meaningful information from aggregated log files (e.g. users from different countries/organizations). Conclusions: - MongoDB is useful for server log analytics as the analyst can immediately access, analyze, and visualize MongoDB data based on the requirements; - The information can be extracted to develop custom reports; - Optimized queries can be run for fast data access. Path forward: - Custom queries need to be developed to extract information leading to improved user information.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Publication and Impact of Preprints Included in the First 100 Editions of the CDC COVID-19 Science Update: Content Analysis

Preprints are publicly available manuscripts posted to various servers that have not been peer reviewed. Although preprints have existed since 1961, they have gained increased popularity during the COVID-19 pandemic due to the need for immediate, relevant information. The aim of this study is to evaluate the publication rate and impact of preprints included in the Centers for Disease Control and Prevention (CDC) COVID-19 Science Update and assess the performance of the COVID-19 Science Update team in selecting impactful preprints. All preprints in the first 100 editions (April 1, 2020, to July 30, 2021) of the Science Update were included in the study. Preprints that were not published were categorized as “unpublished preprints.” Preprints that were subsequently published exist in 2 versions (in a peer-reviewed journal and on the original preprint server), which were analyzed separately and referred to as “peer-reviewed preprint” and “original preprint,” respectively. Time to publish was the time interval between the date on which a preprint was first posted and the date on which it was first available as a peer-reviewed article. Impact was quantified by Altmetric Attention Score and citation count for all available manuscripts on August 6, 2021. Preprints were analyzed by publication status, publication rate, preprint server, and time to publication. Of the 275 preprints included in the CDC COVID-19 Science Update during the study period, most came from three servers: medRxiv (n=201, 73.1%), bioRxiv (n=41, 14.9%), and SSRN (n=25, 9.1%), with 8 (2.9%) coming from other sources. Additionally, 152 (55.3%) were eventually published. The median time to publish was 2.3 (IQR 1.4-3.7). When preprints posted in the last 2.3 months were excluded (to account for the time to publish), the publication rate was 67.8%. Moreover, 76 journals published at least one preprint from the CDC COVID-19 Science Update, and 18 journals published at least three. The median Altmetric Attention Score for unpublished preprints (n=123, 44.7%) was 146 (IQR 22-552) with a median citation count of 2 (IQR 0-8); for original preprints (n=152, 55.2%), these values were 212 (IQR 22-1164) and 14 (IQR 2-40), respectively; for peer-review preprints, these values were 265 (IQR 29-1896) and 19 (IQR 3-101), respectively. Prior studies of COVID-19 preprints found publication rates between 5.4% and 21.1%. Preprints included in the CDC COVID-19 Science Update were published at a higher rate than overall COVID-19 preprints, and those that were ultimately published were published within months and received higher attention scores than unpublished preprints. These findings indicate that the Science Update process for selecting preprints had a high fidelity in terms of their likelihood to be published and their impact. The incorporation of high-quality preprints into the CDC COVID-19 Science Update improves this activity’s capacity to inform meaningful public health decision-making.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment

Here we present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were difficult targets for which only distantly related templates were found for the individual subunits. Twenty-five CAPRI groups including eight automatic servers submitted ~1250 models per target. Twenty groups including six servers participated in the CAPRI scoring challenge submitted ~190 models per target. The accuracy of the predicted models was evaluated using the classical CAPRI criteria. The prediction performance was measured by a weighted scoring scheme that takes into account the number of models of acceptable quality or higher submitted by each group as part of their five top-ranking models. Compared to the previous CASP-CAPRI challenge, top performing groups submitted such models for a larger fraction (70–75%) of the targets in this Round, but fewer of these models were of high accuracy. Scorer groups achieved stronger performance with more groups submitting correct models for 70–80% of the targets or achieving high accuracy predictions. Servers performed less well in general, except for the MDOCKPP and LZERD servers, who performed on par with human groups. In addition to these results, major advances in methodology are discussed, providing an informative overview of where the prediction of protein assemblies currently stands.

protein complexes↗

DOE OSTI2020

Striped Data Analysis Framework

A columnar data representation is known to be an efficient way for data storage, specifically in cases when the analysis is often done based only on a small fragment of the available data structures. A data representation like Apache Parquet is a step forward from a columnar representation, which splits data horizontally to allow for easy parallelization of data analysis. Based on the general idea of columnar data storage, working on the [LDRD Project], we have developed a striped data representation, which, we believe, is better suited to the needs of High Energy Physics data analysis. A traditional columnar approach allows for efficient data analysis of complex structures. While keeping all the benefits of columnar data representations, the striped mechanism goes further by enabling easy parallelization of computations without requiring special hardware. We will present an implementation and some performance characteristics of such a data representation mechanism using a distributed no-SQL database or a local file system, unified under the same API and data representation model. The representation is efficient and at the same time simple so that it allows for a common data model and APIs for wide range of underlying storage mechanisms such as distributed no-SQL databases and local file systems. Striped storage adopts Numpy arrays as its basic data representation format, which makes it easy and efficient to use in Python applications. The Striped Data Server is a web service, which allows to hide the server implementation details from the end user, easily exposes data to WAN users, and allows to utilize well known and developed data caching solutions to further increase data access efficiency. We are considering the Striped Data Server as the core of an enterprise scale data analysis platform for High Energy Physics and similar areas of data processing. We have been testing this architecture with a 2TB dataset from a CMS dark matter search and plan to expand it to multiple 100 TB or even PB scale. We will present the striped format, Striped Data Server architecture and performance test results.

Gutsche, Oliver↗

DOE OSTI2015

Engineering the CernVM-Filesystem as a High Bandwidth Distributed Filesystem for Auxiliary Physics Data

A common use pattern in the computing models of particle physics experiments is running many distributed applications that read from a shared set of data files. We refer to this data is auxiliary data, to distinguish it from (a) event data from the detector (which tends to be different for every job), and (b) conditions data about the detector (which tends to be the same for each job in a batch of jobs). Relatively speaking, conditions data also tends to be relatively small per job where both event data and auxiliary data are larger per job. Unlike event data, auxiliary data comes from a limited working set of shared files. Since there is spatial locality of the auxiliary data access, the use case appears to be identical to that of the CernVM- Filesystem (CVMFS). However, we show that distributing auxiliary data through CVMFS causes the existing CVMFS infrastructure to perform poorly. We utilize a CVMFS client feature called 'alien cache' to cache data on existing local high-bandwidth data servers that were engineered for storing event data. This cache is shared between the worker nodes at a site and replaces caching CVMFS files on both the worker node local disks and on the site's local squids. We have tested this alien cache with the dCache NFSv4.1 interface, Lustre, and the Hadoop Distributed File System (HDFS) FUSE interface, and measured performance. In addition, we use high-bandwidth data servers at central sites to perform the CVMFS Stratum 1 function instead of the low-bandwidth web servers deployed for the CVMFS software distribution function. We have tested this using the dCache HTTP interface. As a result, we have a design for an end-to-end high-bandwidth distributed caching read-only filesystem, using existing client software already widely deployed to grid worker nodes and existing file servers already widely installed at grid sites. Files are published in a central place and are soon available on demand throughout the grid and cached locally on the site with a convenient POSIX interface. This paper discusses the details of the architecture and reports performance measurements.

Dykstra, D.↗

DOE OSTI2015

TRIO: Burst Buffer Based I/O Orchestration, In: 2015 IEEE International Conference on Cluster Computing

The growing computing power on leadership HPC systems is often accompanied by ever-escalating failure rates. Checkpointing is a common defensive mechanism used by scientific applications for failure recovery. However, directly writing the large and bursty checkpointing dataset to parallel file systems can incur significant I/O contention on storage servers. Such contention in turn degrades bandwidth utilization of storage servers and prolongs the average job I/O time of concurrent applications. Recently burst buffers have been proposed as an intermediate layer to absorb the bursty I/O traffic from compute nodes to storage backend. But an I/O orchestration mechanism is still desirable to efficiently move checkpointing data from burst buffers to storage backend. In this paper, we propose a burst buffer based I/O orchestration framework, named TRIO, to intercept and reshape the bursty writes for better sequential write traffic to storage servers. Meanwhile, TRIO coordinates the flushing orders among concurrent burst buffers to alleviate the contention on storage server. Our experimental results demonstrated that TRIO could efficiently utilize storage bandwidth and reduce the average job I/O time by 37% on average for data-intensive applications in typical checkpointing scenarios.

Computer Science↗

DOE OSTI2026

Energy–Performance Trade-offs in Privacy-Preserving Federated Learning on SmartNIC-Enabled HPC Systems

Federated learning (FL) is increasingly deployed on accelerator-rich high-performance computing (HPC) systems, yet the system-level energy cost of privacy-aware FL remains poorly understood, particularly across heterogeneous networking and server-placement options. We present a measurement-driven study of energy–performance trade-offs for FL on GH200-class nodes across three deployment configurations: CPU-Ethernet, CPU-InfiniBand (RDMA-capable), and a DPU-hosted FL server over InfiniBand using a BlueField-3 SmartNIC/DPU. Using NVIDIA FLARE (NVFLARE), we align node-level power telemetry with per-round timing extracted from NVFLARE logs to quantify time-to-solution (TTS), energy-to-solution (ETS), energy-delay product (EDP), and synchronization behavior for three transformer models (ALBERT, DistilBERT, BERT), trained with and without differential privacy (DP). We find that interconnect choice is the dominant driver of runtime and energy: host-managed InfiniBand consistently reduces communication overhead versus Ethernet, yielding lower TTS/ETS/EDP. In contrast, in our NVFLARE deployment, placing the FL server on the DPU does not consistently match CPU-InfiniBand performance and can be slower—especially for larger models—highlighting that server placement alone is not sufficient to guarantee end-to-end gains. Finally, under our fixed-round protocol, DP increases per-round cost and runtime variance; ETS increases largely in proportion to TTS because average node power remains relatively stable across configurations.

Kotevska, Olivera [ORNL] (ORCID:0000000316772243)↗

DOE OSTI2022

Network Anomaly Detection Using Federated Learning

The internet is turning out to be an integral part of every-one's lives as more and more devices are being connected to serve societal needs. Our work is motivated by two ma-jor observations. Firstly, one drawback of connecting to the network is the threat of network attacks that can compromise users' private information, leading to data loss and adversely affecting productivity. There are several traditional security mechanisms to defend against these attacks, such as firewalls, virtual private networks (VPNs), demilitarized zones (DMZs), and vulnerability scanners. One way to prevent these attacks is early detection and prevention. However, these kinds of architecture do not scale very well because of their centralized nature. Secondly, we observe from heuristics and data set distributions that the majority of the requests made to a server are innocuous. Therefore, almost all server request data sets are highly imbalanced, weighted highly towards the harmless requests.

Marfo, William↗

DOE OSTI2020

Differential Power Processing for Ultra-Efficient Data Storage

Here this paper presents the hardware, software, and power codesign of an ultra-efficient data storage server with differential power processing (DPP). DPP can reduce the power conversion stress, improve the efficiency, and enhance the functionality of modular power electronics systems. The power inputs of a large number of hard disk drives (HDDs) were connected in series and supported by a multiport ac-coupled differential power processing (MAC-DPP) converter through a multiwinding transformer. Methods for controlling the multi-input multi-output power flow in the multiwinding transformer while avoiding core saturation were investigated. A ten-port MAC-DPP prototype with 700-W/in 3 power density was built to support a 450-W HDD storage system with ten series-stacked voltage domains. The prototype was tested on a 50-HDD server testbench, and the overall system loss is below 1 W (99.77% system efficiency). The server was able to maintain high-speed reading and writing operation of all 50 HDDs against the worst hot-swapping scenarios. A variety of hardware/software configurations and many cloud storage techniques were tested on the fully functioning server. Experimental results show that the energy efficiency of large-scale information systems (CPU/GPU clusters, memory banks, HDD arrays, etc.) can be greatly improved by software, hardware, and power codesign.

42 ENGINEERING↗

DOE OSTI2025

CAFE AU LAIT: Compute-Aware Federated Augmented Low-Rank AI Training

Federated finetuning is crucial for unlocking the knowledge embedded in pretrained Large Language Models (LLMs) when data are geographically distributed across clients. Unlike finetuning with data from a single institution, federated finetuning allows collaboration across multiple institutions, enabling the utilization of diverse and decentralized datasets while preserving data privacy. Given the high computing costs of LLM training and the emphasis on energy efficiency in Federated Learning (FL), Low-Rank Adaptation (LoRA) has emerged as a widely adopted algorithm due to its significantly reduced number of trainable parameters. However, this assumes that all data silos have the necessary computing resources to compute local updates of LLMs. Nevertheless, in practice, the computing resources across clients are highly heterogeneous: while some may have access to hundreds of GPUs, others might have limited or no GPU access. Recently, federated finetuning using synthetic data has been proposed, allowing clients to participate in a collaborative training run without training LLMs locally. However, our experimental results reveal a performance gap between models trained using synthetic data and those trained using local updates. Motivated by the observed heterogeneity in computing resources and the performance gap, we propose a novel two-stage algorithm that leverages the storage and computing capabilities of a strong server. In the first stage, under the coordination of the strong server, clients with limited computing resources collaborate to generate synthetic data, which is transferred to and stored on the strong server. In the second stage, the strong server uses this synthetic data on behalf of the resource-constrained clients to perform federated LoRA finetuning alongside clients with sufficient computing resources. This approach ensures that all clients can participate in the finetuning process. Experimental results demonstrate that incorporating local updates from even a small fraction of clients improves performance compared to using synthetic data for all clients. Furthermore, we incorporate the Gaussian mechanism in both stages to guarantee client-level differential privacy.

Wang, Jiayi [ORNL]↗