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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Distributed metadata servers in a file system with separate metadata servers for file metadata and directory metadata

A file system is provided having a plurality of distributed metadata servers with separate metadata servers for file metadata and directory metadata. An exemplary distributed metadata storage system comprises a plurality of compute nodes, wherein at least one compute node comprises a file system client that generates a directory metadata request and/or a file metadata request; a plurality of directory metadata servers to serve directory metadata in response to a given directory metadata request; and a plurality of file metadata servers to serve file metadata in response to a given file metadata request. The plurality of directory metadata servers are distinct from the plurality of file metadata servers. The compute nodes optionally comprise an object client that requests object data from an object store system. The number of directory metadata servers and the number of file metadata servers are optionally determined independently to balance for different workloads, and can be dynamically readjusted for evolving workloads.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Data decomposition of Monte Carlo particle transport simulations via tally servers

An algorithm for decomposing large tally data in Monte Carlo particle transport simulations is developed, analyzed, and implemented in a continuous-energy Monte Carlo code, OpenMC. The algorithm is based on a non-overlapping decomposition of compute nodes into tracking processors and tally servers. The former are used to simulate the movement of particles through the domain while the latter continuously receive and update tally data. A performance model for this approach is developed, suggesting that, for a range of parameters relevant to LWR analysis, the tally server algorithm should perform with minimal overhead on contemporary supercomputers. An implementation of the algorithm in OpenMC is then tested on the Intrepid and Titan supercomputers, supporting the key predictions of the model over a wide range of parameters. We thus conclude that the tally server algorithm is a successful approach to circumventing classical on-node memory constraints en route to unprecedentedly detailed Monte Carlo reactor simulations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

LigSearch: a knowledge-based web server to identify likely ligands for a protein target

LigSearch is a web server for identifying ligands likely to bind to a given protein. Identifying which ligands might bind to a protein before crystallization trials could provide a significant saving in time and resources. LigSearch, a web server aimed at predicting ligands that might bind to and stabilize a given protein, has been developed. Using a protein sequence and/or structure, the system searches against a variety of databases, combining available knowledge, and provides a clustered and ranked output of possible ligands. LigSearch can be accessed at http://www.ebi.ac.uk/thornton-srv/databases/LigSearch.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2012

CORAL Server and CORAL Server Proxy: Scalable Access to Relational Databases from CORAL Applications

The CORAL software is widely used at CERN by the LHC experiments to access the data they store on relational databases, such as Oracle. Two new components have recently been added to implement a model involving a middle tier 'CORAL server' deployed close to the database and a tree of 'CORAL server proxies', providing data caching and multiplexing, deployed close to the client. A first implementation of the two new components, released in the summer 2009, is now deployed in the ATLAS online system to read the data needed by the High Level Trigger, allowing the configuration of a farm of several thousand processes. This paper reviews the architecture of the software, its development status and its usage in ATLAS.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2017

Characteristics and Energy Use of Volume Servers in the United States

Servers’ field energy use remains poorly understood, given heterogeneous computing loads, configurable hardware and software, and operation over a wide range of management practices. This paper explores various characteristics of 1- and 2-socket volume servers that affect energy consumption, and quantifies the difference in power demand between higher-performing SPEC and ENERGY STAR servers and our best understanding of a typical server operating today. We first establish general characteristics of the U.S. installed base of volume servers from existing IDC data and the literature, before presenting information on server hardware configurations from data collection events at a major online retail website. We then compare cumulative distribution functions of server idle power across three separate datasets and explain the differences between them via examination of the hardware characteristics to which power draw is most sensitive. We find that idle server power demand is significantly higher than ENERGY STAR benchmarks and the industry-released energy use documented in SPEC, and that SPEC server configurations—and likely the associated power-scaling trends—are atypical of volume servers. Next, we examine recent trends in server power draw among high-performing servers across their full load range to consider how representative these trends are of all volume servers before inputting weighted average idle power load values into a recently published model of national server energy use. Finally, we present results from two surveys of IT managers (n=216) and IT vendors (n=178) that illustrate the prevalence of more-efficient equipment and operational practices in server rooms and closets; these findings highlight opportunities to improve the energy efficiency of the U.S. server stock.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Comparing datasets of volume servers to illuminate their energy use in data centers

As data centers proliferate, their energy intensity deserves close attention. Always-on operations and growing usage for cloud and other backend processes make servers the fundamental driver of data center energy use. Yet servers’ power draw under real-world conditions is poorly understood. This paper explores characteristics of volume servers that affect energy use, quantifying differences in power draw between higher-performing Standard Performance Evaluation Corporation (SPEC) and ENERGY STAR servers and that of a typical server. First, we establish general characteristics of the US installed base, before reporting hardware configurations from a major online retail website. We then compare idle power across three datasets (one unique to this paper) and explain their differences via the hardware characteristics to which power draw is most sensitive. We find idle server power demand to be significantly higher than benchmarks from ENERGY STAR and the industry-released SPEC database, and SPEC server configurations—and likely their power scaling—to be atypical of volume servers. Next, we examine power draw trends among high-performing servers across their load range to consider whether these trends are representative of volume servers, before inputting average idle power load values into a recent national server energy use model. Lastly, results from two surveys of IT professionals illustrate the incidence of more efficient equipment and operational practices in server rooms/closets. Future work should include server power field measurements in data centers of different sizes, accounting for variations in configurations and setting changes post-purchase, as well as investigating the linkage between time and server energy efficiency.

Fuchs, Heidi (ORCID:0000000300105411)↗
DOE OSTI2014

Energy Efficiency in Small Server Rooms: Field Surveys and Findings

Fifty-seven percent of US servers are housed in server closets, server rooms, and localized data centers, in what are commonly referred to as small server rooms, which comprise 99percent of all server spaces in the US. While many mid-tier and enterprise-class data centers are owned by large corporations that consider energy efficiency a goal to minimize business operating costs, small server rooms typically are not similarly motivated. They are characterized by decentralized ownership and management and come in many configurations, which creates a unique set of efficiency challenges. To develop energy efficiency strategies for these spaces, we surveyed 30 small server rooms across eight institutions, and selected four of them for detailed assessments. The four rooms had Power Usage Effectiveness (PUE) values ranging from 1.5 to 2.1. Energy saving opportunities ranged from no- to low-cost measures such as raising cooling set points and better airflow management, to more involved but cost-effective measures including server consolidation and virtualization, and dedicated cooling with economizers. We found that inefficiencies mainly resulted from organizational rather than technical issues. Because of the inherent space and resource limitations, the most effective measure is to operate servers through energy-efficient cloud-based services or well-managed larger data centers, rather than server rooms. Backup power requirement, and IT and cooling efficiency should be evaluated to minimize energy waste in the server space. Utility programs are instrumental in raising awareness and spreading technical knowledge on server operation, and the implementation of energy efficiency measures in small server rooms.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Comparing Server Energy Use and Efficiency Using Small Sample Sizes

This report documents a demonstration that compared the energy consumption and efficiency of a limited sample size of server-type IT equipment from different manufacturers by measuring power at the server power supply power cords. The results are specific to the equipment and methods used. However, it is hoped that those responsible for IT equipment selection can used the methods described to choose models that optimize energy use efficiency. The demonstration was conducted in a data center at Lawrence Berkeley National Laboratory in Berkeley, California. It was performed with five servers of similar mechanical and electronic specifications; three from Intel and one each from Dell and Supermicro. Server IT equipment is constructed using commodity components, server manufacturer-designed assemblies, and control systems. Server compute efficiency is constrained by the commodity component specifications and integration requirements. The design freedom, outside of the commodity component constraints, provides room for the manufacturer to offer a product with competitive efficiency that meets market needs at a compelling price. A goal of the demonstration was to compare and quantify the server efficiency for three different brands. The efficiency is defined as the average compute rate (computations per unit of time) divided by the average energy consumption rate. The research team used an industry standard benchmark software package to provide a repeatable software load to obtain the compute rate and provide a variety of power consumption levels. Energy use when the servers were in an idle state (not providing computing work) were also measured. At high server compute loads, all brands, using the same key components (processors and memory), had similar results; therefore, from these results, it could not be concluded that one brand is more efficient than the other brands. The test results show that the power consumption variability caused by the key components as a group is similar to all other components as a group. However, some differences were observed. The Supermicro server used 27 percent more power at idle compared to the other brands. The Intel server had a power supply control feature called cold redundancy, and the data suggest that cold redundancy can provide energy savings at low power levels. Test and evaluation methods that might be used by others having limited resources for IT equipment evaluation are explained in the report.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2011

Asynchronous data change notification between database server and accelerator controls system

Database data change notification (DCN) is a commonly used feature. Not all database management systems (DBMS) provide an explicit DCN mechanism. Even for those DBMS's which support DCN (such as Oracle and MS SQL server), some server side and/or client side programming may be required to make the DCN system work. This makes the setup of DCN between database server and interested clients tedious and time consuming. In accelerator control systems, there are many well established software client/server architectures (such as CDEV, EPICS, and ADO) that can be used to implement data reflection servers that transfer data asynchronously to any client using the standard SET/GET API. This paper describes a method for using such a data reflection server to set up asynchronous DCN (ADCN) between a DBMS and clients. This method works well for all DBMS systems which provide database trigger functionality. Asynchronous data change notification (ADCN) between database server and clients can be realized by combining the use of a database trigger mechanism, which is supported by major DBMS systems, with server processes that use client/server software architectures that are familiar in the accelerator controls community (such as EPICS, CDEV or ADO). This approach makes the ADCN system easy to set up and integrate into an accelerator controls system. Several ADCN systems have been set up and used in the RHIC-AGS controls system.

43 PARTICLE ACCELERATORS↗
DOE OSTI2018

Liquid-Cooled Server Technology that Dramatically Reduces Electricity in Building-Connected Data Centers and Enables 95% Waste Heat Reuse

Today data centers in the U.S consume about 70 TWh of electricity annually. What if there was an easy way to reduce this electricity use by up to 35%? Even better, what if data centers were located in ordinary buildings without the need for expensive air conditioning and air handing systems? Better still, what if the heat energy produced by the data center's servers could be easily recovered and reused for building heating? This presentation will provide an overview of a unique and innovative form of liquid immersion cooling for data center servers developed by LiquidCool Solutions that can be easily deployed to achieve the benefits described above. As validation of the benefits, the presentation will describe testing of a small-scale version of this immersion cooling system conducted at the National Renewable Energy Laboratory. Testing demonstrated that the system was able to recover 90% to 95% of server heat energy at a temperature high enough for use by conventional building heating systems while eliminating internal server fans and using just 1% of the server power to circulate cooling fluid through the system. The concluding section of the presentation will focus on development of a roadmap for scaling-up and commercializing this innovative cooling technology. This will include proposals for how specific commercial building types could make effective use of this technology to recover and reuse server waste heat and a strategy for overcoming barriers to adoption of such a new and innovative technology.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Distributed metadata servers for cluster file systems using shared low latency persistent key-value metadata store

A cluster file system is provided having a plurality of distributed metadata servers with shared access to one or more shared low latency persistent key-value metadata stores. A metadata server comprises an abstract storage interface comprising a software interface module that communicates with at least one shared persistent key-value metadata store providing a key-value interface for persistent storage of key-value metadata. The software interface module provides the key-value metadata to the at least one shared persistent key-value metadata store in a key-value format. The shared persistent key-value metadata store is accessed by a plurality of metadata servers. A metadata request can be processed by a given metadata server independently of other metadata servers in the cluster file system. A distributed metadata storage environment is also disclosed that comprises a plurality of metadata servers having an abstract storage interface to at least one shared persistent key-value metadata store.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2010

Cooperative Server Clustering for a Scalable GAS Model on Petascale Cray XT5 Systems

Global Address Space (GAS) programming models are attractive because they retain the easy-to-use addressing model that is the characteristic of shared-memory style load and store operations. The scalability of GAS models depends directly on the design and implementation of runtime libraries on the targeted platforms. In this paper, we examine the memory requirement of a popular GAS run-time library, Aggregate Remote Memory Copy Interface (ARMCI) on petascale Cray XT 5 systems. Then we describe a new technique, cooperative server clustering, that enhances the memory scalability of ARMCI communication servers. In cooperative server clustering, ARMCI servers are organized into clusters, and cooperatively process incoming communication requests among them. A request intervention scheme is also designed to expedite the return of responses to the initiating processes. Our experimental results demonstrate that, with very little impact on ARMCI communication latency and bandwidth, cooperative server clustering is able to significantly reduce the memory requirement of ARMCI communication servers, thereby enabling highly scalable scientific applications. In particular, it dramatically reduces the total execution time of a scientific application, NWChem, by 45% on 2400 processes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Development of intelligent instruments with embedded HTTP servers for control and data acquisition in a cryogenic setup—The hardware, firmware, and software implementation

The power of Ethernet for control and automation technology is being largely understood by the automation industry in recent times. Ethernet with HTTP (Hypertext Transfer Protocol) is one of the most widely accepted communication standards today. Ethernet is best known for being able to control through internet from anywhere in the globe. The Ethernet interface with built-in on-chip embedded servers ensures global connections for crate-less model of control and data acquisition systems which have several advantages over traditional crate-based control architectures for slow applications. This architecture will completely eliminate the use of any extra PLC (Programmable Logic Controller) or similar control hardware in any automation network as the control functions are firmware coded inside intelligent meters itself. Here, we describe the indigenously built project of a cryogenic control system built for linear accelerator at Inter University Accelerator Centre, known as “CADS,” which stands for “Complete Automation of Distribution System.” CADS deals with complete hardware, firmware, and software implementation of the automated linac cryogenic distribution system using many Ethernet based embedded cryogenic instruments developed in-house. Each instrument works as an intelligent meter called device-server which has the control functions and control loops built inside the firmware itself. Dedicated meters with built-in servers were designed out of ARM (Acorn RISC (Reduced Instruction Set Computer) Machine) and ATMEL processors and COTS (Commercially Off-the-Shelf) SMD (Surface Mount Devices) components, with analog sensor front-end and a digital back-end web server implementing remote procedure call over HTTP for digital control and readout functions. At present, 24 instruments which run 58 embedded servers inside, each specific to a particular type of sensor-actuator combination for closed loop operations, are now deployed and distributed across control LAN (Local Area Network). A group of six categories of such instruments have been identified for all cryogenic applications required for linac operation which were designed to build this medium-scale cryogenic automation setup. These devices have special features like remote rebooters, daughter boards for PIDs (Proportional Integral Derivative), etc., to operate them remotely in radiation areas and also have emergency switches by which each device can be taken to emergency mode temporarily. Finally, all the data are monitored, logged, controlled, and analyzed online at a central control room which has a user-friendly control interface developed using LabVIEW{sup ®}. This paper discusses the overall hardware, firmware, software design, and implementation for the cryogenics setup.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2013

Assessment of risk for asthma initiation and cancer and heart disease deaths among patrons and servers due to secondhand smoke exposure in restaurants and bars

Background: Despite efforts to reduce exposure to secondhand smoke (SHS), only 5% of the world’s population enjoy smoke-free restaurants and bars. Methods: Lifetime excess risk (LER) of cancer death, ischaemic heart disease (IHD) death and asthma initiation among non-smoking restaurant and bar servers and patrons in Minnesota and the US were estimated using weighted field measurements of SHS constituents in Minnesota, existing data on tobacco use and multiple dose-response models. Results: A continuous approach estimated a LER of lung cancer death (LCD) of 18×10 –6 (95% CI 13 to 23×10 –6 ) for patrons visiting only designated nonsmoking sections, 80×10 –6 (95% CI 66 to 95×10 –6 ) for patrons visiting only smoking venues/sections and 802×10 –6 (95% CI 658 to 936×10 –6 ) for servers in smoking-permitted venues. An attributable-risk (exposed/ non-exposed) approach estimated a similar LER of LCD, a LER of IHD death about 10 –2 for non-smokers with average SHS exposure from all sources and a LER of asthma initiation about 5% for servers with SHS exposure at work only. These risks correspond to 214 LCDs and 3001 IHD deaths among the general nonsmoking population and 1420 new asthma cases among non-smoking servers in the US each year due to SHS exposure in restaurants and bars alone. Conclusions: Health risks for patrons and servers from SHS exposure in restaurants and bars alone are well above the acceptable level. Restaurants and bars should be a priority for governments’ effort to create smoke-free environments and should not be exempt from smoking bans.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

mada-tools: MCP servers, configurations, skills, and examples for MADA

MADA-tools (Multi-Agent Design Assistant tools) is a library for defining MCP (Model Context Protocol) servers that can be used by AI agents in the MADA project. Each MCP server provides a focused set of tools that enhances an LLM's knowledge and capabilities for a specific domain, for example, how to launch jobs with Flux versus Slurm. The library makes it easy to configure and start multiple MCP servers using configuration files or command line options. Once running, these servers are intended to be consumed by one or more agents in the MADA ecosystem. The system is designed to be extensible so that future projects can contribute their own MCP servers, skills, and toolsets.

Gunnarson, BrianS [Lawrence Livermore National Lab↗
DOE OSTI2025

EnergyPlus-MCP: A model-context-protocol server for ai-driven building energy modeling

Traditional building energy modeling with the EnergyPlus building performance simulation engine requires domain expertise, programming skills, and intensive manual efforts limiting its effective adoption. This paper introduces EnergyPlus-MCP, the first open-source Model Context Protocol (MCP) server specifically designed for EnergyPlus simulation workflows, establishing a new foundational infrastructure for AI-driven building energy modeling. The MCP server implements a layered architecture with 35 specialized tools spanning model management, editing and analysis, HVAC and other systems configuration inspection, and simulation execution, enabling Large Language Models to interact with EnergyPlus through conversational interfaces. The server addresses critical workflow barriers by automating model validation, streamlining energy efficiency measures modification, and providing intelligent output management with interactive visualization. Through practical demonstrations using a multi-zone building retrofit analysis, we show how the EnergyPlus-MCP server significantly reduces manual efforts while maintaining full simulation rigor. By providing accessible natural language interfaces to sophisticated building energy analysis, this approach enables scalable deployment of simulation expertise across public and private organizations, educational institutions, and research teams, fundamentally transforming traditional building energy modeling practices.

AI↗
DOE OSTI2018

Striped Data Server for Scalable Parallel Data Analysis

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. As a result, 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.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗