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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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All sourcesNASA NTRSDOE OSTI
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 OSTI2020

FPDeep: Scalable Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters

In this paper, we propose a framework called FPDeep, which uses a hybrid of model and layer paral- lelism to configure distributed reconfigurable clusters to train DNNs. This approach has numerous benefits. First, the design does not suffer from batch size growth. Second, novel workload and weight partitioning leads to balanced loads of both among nodes. And third, the entire system is fine-grained pipeline. This leads to high parallelism and utilization and also minimizes the time features need to be cached while waiting for back-propagation.

Wang, Tianqi↗
DOE OSTI2015

Towards Batched Linear Solvers on Accelerated Hardware Platforms

As hardware evolves, an increasingly effective approach to develop energy efficient, high-performance solvers, is to design them to work on many small and independent problems. Indeed, many applications already need this functionality, especially for GPUs, which are known to be currently about four to five times more energy efficient than multicore CPUs for every floating-point operation. In this paper, we describe the development of the main one-sided factorizations: LU, QR, and Cholesky; that are needed for a set of small dense matrices to work in parallel. We refer to such algorithms as batched factorizations. Our approach is based on representing the algorithms as a sequence of batched BLAS routines for GPU-contained execution. Note that this is similar in functionality to the LAPACK and the hybrid MAGMA algorithms for large-matrix factorizations. But it is different from a straightforward approach, whereby each of GPU's symmetric multiprocessors factorizes a single problem at a time. We illustrate how our performance analysis together with the profiling and tracing tools guided the development of batched factorizations to achieve up to 2-fold speedup and 3-fold better energy efficiency compared to our highly optimized batched CPU implementations based on the MKL library on a two-sockets, Intel Sandy Bridge server. Compared to a batched LU factorization featured in the NVIDIA's CUBLAS library for GPUs, we achieves up to 2.5-fold speedup on the K40 GPU.

Haidar, Azzam↗
DOE OSTI2021

Computational Offload with BlueField Smart NICs

The recent introduction of a new generation of "smart NICs" have provided new accelerator platforms that include CPU cores or reconfigurable fabric in addition to traditional networking hardware and packet offloading capabilities. While there are currently several proposals for using these smartNICs for low-latency, in-line packet processing operations, there remains a gap in knowledge as to how they might be used as computational accelerators for traditional high-performance applications. This work aims to look at benchmarks and mini-applications to evaluate possible benefits of using a smartNIC as a compute accelerator for HPC applications. We investigate NVIDIA's current-generation BlueField-2 card, which includes eight Arm CPUs along with a small amount of storage, and we test the networking and data movement performance of these cards compared to a standard Intel server host. We then detail how two different applications, YASK and miniMD can be modified to make more efficient use of the BlueField-2 device with a focus on overlapping computation and communication for operations like neighbor building and halo exchanges. Our results show that while the overall compute performance of these devices is limited, using them with a modified miniMD algorithm allows for potential speedups of 5 to 20% over the host CPU baseline with no loss in simulation accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Application configuration selection for energy-efficient execution on multicore systems

Balanced performance and energy consumption are incorporated in the design of modern computer systems. Several runtime factors, such as concurrency levels, thread mapping strategies, and dynamic voltage and frequency scaling (DVFS) should be considered in order to achieve optimal energy efficiency fora workload. Selecting appropriate run-time factors, however, is one of the most challenging tasks because the run-time factors are architecture-specific and workload-specific. And while most existing works concentrate on either static analysis of the workload or run-time prediction results, we present a hybrid two-step method that utilizes concurrency levels and DVFS settings to achieve the energy efficiency configuration for a worldoad. The experimental results based on a Xeon E5620 server with NPB and PARSEC benchmark suites show that the model is able to predict the energy efficient configuration accurately. On average, an additional 10% EDP (Energy Delay Product) saving is obtained by using run-time DVFS for the entire system. An off-line optimal solution is used to compare with the proposed scheme. Finally, the experimental results show that the average extra EDP saved by the optimal solution is within 5% on selective parallel benchmarks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

SU-D-BRD-02: Auto Weekly - An Automated Online Weekly Chart Check System for Medical Physics

Purpose: Radiation therapy has evolved to become not only more precise and potent, but also more complicated to monitor and deliver. More rigorous and comprehensive quality assurance is needed to safeguard ever advancing radiation therapy. ICRU standards dictate that an ever growing set of treatment parameters are manually checked weekly by medical physicists. This “weekly chart check” procedure is laborious and subject to human errors or other factors. A computer-assisted chart checking process will enable more complete and accurate human review of critical parameters, reduce the risk of medical errors, and improve the efficiency. Methods: We developed a web-based software system that enables a thorough weekly quality assurance checks. In the backend, the software retrieves all machine parameters from a Treatment Management System (TMS) and compares them against the corresponding ones from the treatment planning system. They are also checked for validity against preset rules. The results are displayed as a web page in the front-end for physicists to review. Then a summary report is generated and uploaded automatically to the TMS as a record for weekly chart checking. Results: The software system has been deployed on a web server in our department’s intranet, and has been tested thoroughly by our clinical physicists. A plan parameter would be highlighted when it is off the preset limit. The developed system has changed the way of checking charts with significantly improved accuracy, efficiency, and completeness. It has been shown to be robust, fast, and easy to use. Conclusion: A computer-assisted system has been developed for efficient, accurate, and comprehensive weekly chart checking. The system has been extensively validated and is being implemented for routine clinical use.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Improving Data Center Energy Efficiency Through End-to-End Cooling Modeling and Optimization. Final Report

This report summarizes the development and demonstration of the three features developed for the DOE project “Improving Data Center Energy Efficiency through End-to-End Cooling Modeling and Optimization” (Award No. DE-EE0007688). The development of the three software features, Feature 1: Optimization of Cooling System Operation, Feature 2: Optimization of Airflow Management System, and Feature 3: Simultaneous Optimization of Airflow Management and Cooling Systems, are described first. Feature 1 is implemented in Phase 1 of the project and demonstrates 15% energy savings from cooling system optimization by applying the proposed retrofit solutions. Feature 2 is implemented in Phase 2 and the assessments show cooling energy-saving potential between 19.3% and 54.0%, depending on the energy-saving measures employed. In addition, investigating the energy saving potential for a hypothetical simple data center shows about 6.8% energy savings by optimizing the server load placement. Feature 3 is implemented in Phase 3 and the results show energy savings of 53.4% and 74.6% for the two partner data centers, respectively, compared to the baselines defined in Phase 1. Lastly, the project outcomes including software releases and publications are discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

PATCHWORK: A Multipatch Infrastructure for Multiphysics/Multiscale/Multiframe Fluid Simulations

Here, we present a "multipatch" infrastructure for the numerical simulation of fluid problems in which subregions require different grid scales, different grid geometries, different physical equations, or different reference frames. Its key element is a sophisticated client–router–server framework for efficiently linking processors supporting different regions ("patches") that must exchange boundary data. This infrastructure may be used with a wide variety of fluid dynamics codes; the only requirement is that their primary dependent variables be the same in all patches, e.g., fluid mass density, internal energy density, and velocity. Its structure can accommodate either Newtonian or relativistic dynamics. The overhead imposed by this system is both problem and computer cluster architecture dependent. Compared to a conventional simulation using the same number of cells and processors employed on a problem not requiring multipatch methods, the cell update per processor rate decreases by an amount that can range from negligible to a factor of a few; however, even in these problems, the infrastructure can permit substantial decreases in the total number of cell updates required.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Holistic energy analysis method for thermal management architectures of data centers

Modern high-performance computing (HPC) data centers (DCs), particularly those supporting energy-intensive artificial intelligence (AI) workloads, face escalating thermal management challenges that degrade performance through thermal throttling and drive up cooling power consumption and operational costs. To address this challenge, many have developed a wide variety of thermal management solutions (single-phase, two-phase, direct, indirect, hybrid, and more) which attempt to cool HPC DCs effectively while attempting to minimize overall system power consumption. However, the analysis of these solutions and methods to effectively compare one with another is lacking. Overall power usage effectiveness (PUE) and total-power usage effectiveness (TUE) provide a metric to quantify power consumption but fail to identify components in the system which require further optimization. To address this, we propose a holistic analytical framework – the waterfall diagram (WFD) – which leverages a waterfall chart methodology, offering a comprehensive visualization of both the thermal management system loop and heat flow pathways from individual server components to the outdoor ambient. Use of the WFD enables graphical estimations of power efficiency and cooling performance across each component of a DC cooling system and complements Sankey-style energy flow visualizations by additionally resolving stage-wise temperature changes and incremental TUE contributions. The framework is used in conjunction with simulation-based approaches, to conduct a detailed pressure drop and flow distribution analysis aimed at identifying the optimal coolant distribution architecture for a single-phase direct-to-chip water-cooled DC, which serves as the baseline for subsequent WFD analysis. Among the evaluated architectures, the 3 U modular coolant distribution architecture is found to demonstrate the best performance, considering minimal pressure drop and uniform flow distribution. In addition, TUE is calculated for each cooling loop component based on its associated pressure drop and corresponding pumping power, which are integrated into the WFD. This correlation between TUE and local temperature offers immediate insight into the power efficiency and thermal performance contributions of individual components, facilitating further development and optimization. Examples of WFD applications are presented under varying thermal loads and ambient conditions, demonstrating reasonable cooling strategies. Notably, the 3 U modular architecture maintains a consistent chip case temperature of 85°C, achieving a TUE of 1.016 at ambient temperature of 47°C, and a TUE of 1.026 at ambient temperature of 52°C. The WFD methodology provides an efficient, holistic, and streamlined framework for DC thermal management architecture assessment and enables design optimization which is important for addressing the thermal-fluidic energy challenges of current and next-generation DCs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
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 OSTI2022

Emerging Threats and Technology Investigation: Industrial Internet of Things - Risk and Mitigation for Nuclear Infrastructure

Industries supporting the global nuclear infrastructure striving for cost savings, expansions in efficiency, and convenience are likely to adopt components (e.g., hardware, software) that comprise the Internet of Things (IoT) and Industrial Internet of Things (IIoT). These devices offer potential improvements along with security challenges. Modern conveniences achieved through application of technology have propagated through society in the form of interconnected devices, from doorbells to microwave ovens, commonly referred to as IoT. IoT devices are often Internet-connected devices that are designed to send data back to a cloud-based server, where a smart phone application then presents device status and control options. Home-based IoT applications carry a different set of risks when compared to a business or security environment, where there is also a history of convenience and interconnection. Industrial settings have long relied on specifically designed Supervisory Control and Data Acquisition (SCADA) systems for process control where IIoT devices are intended to inform business decisions and augment traditional processes. A recent National Institute of Standards and Technology (NIST) report provides a distinction between process control and IIoT in that traditional process control is not replaced by IIoT, but rather IIoT devices are intended to enhance industrial processes through additional monitoring of various sensors and application of data analytics models using artificial intelligence (AI) and machine learning (ML) (Fagan, Marron, et al. 2021) (Ross, et al. 2021).

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2019

The Open High Throughput Computing Content Delivery Network

LHC experiments make extensive use of web proxy caches, especially for software distribution via the CernVM File System and for conditions data via the Frontier Distributed Database Caching system. Since many jobs read the same data, cache hit rates are high and hence most of the traffic flows efficiently over Local Area Networks. However, it is not always possible to have local web caches, particularly for opportunistic cases whereexperiments have little control over site services. The Open High Throughput Computing (HTC) Content Delivery Network (CDN), openhtc.io, aims to address this by using web proxy caches from a commercial CDN provider. Cloudflare provides a simple interface for registering DNS aliases of any web server and does reverse proxy web caching on those aliases. The openhtc.io domain is hosted on Cloudflare's free tier CDN which has no bandwidth limit and makes use of data centers throughout the world, so the average performance for clients is much improved compared to reading from CERN or a Tier 1. The load on WLCG servers is also significantly reduced. WLCG Web Proxy Auto Discovery is used to select local web caches when they are available and otherwise select openhtc.io caching. This paper describes the Open HTC CDN in detail and provides initial results from its use for LHC@Home and USCMS opportunistic computing.

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 OSTI2023

TeraChem protocol buffers ( TCPB ): Accelerating QM and QM/MM simulations with a client–server model

The routine use of electronic structures in many chemical simulation applications calls for efficient and easy ways to access electronic structure programs. Here, we describe how the graphics processing unit (GPU) accelerated electronic structure program TeraChem can be set up as an electronic structure server, to be easily accessed by third-party client programs. We exploit Google’s protocol buffer framework for data serialization and communication. The client interface, called TeraChem protocol buffers (TCPB), has been designed for ease of use and compatibility with multiple programming languages, such as C++, Fortran, and Python. To demonstrate the ease of coupling third-party programs with electronic structures using TCPB, we have incorporated the TCPB client into Amber for quantum mechanics/molecular mechanics (QM/MM) simulations. The TCPB interface saves time with GPU initialization and I/O operations, achieving a speedup of more than 2× compared to a prior file-based implementation for a QM region with ~250 basis functions. We demonstrate the practical application of TCPB by computing the free energy profile of p-hydroxybenzylidene-2,3-dimethylimidazolinone (p-HBDI - )—a model chromophore in green fluorescent proteins—on the first excited singlet state using Hamiltonian replica exchange for enhanced sampling. All calculations in this work have been performed with the non-commercial freely-available version of TeraChem, which is sufficient for many QM region sizes in common use.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

iez: Resource Contention Aware Load Balancing for Large-Scale Parallel File Systems

Parallel I/O performance is crucial to sustaining scientific applications on large-scale High-Performance Computing (HPC) systems. However, I/O load imbalance in the underlying distributed and shared storage systems can significantly reduce overall application performance. There are two conflicting challenges to mitigate this load imbalance: (i) optimizing systemwide data placement to maximize the bandwidth advantages of distributed storage servers, i.e., allocating I/O resources efficiently across applications and job runs; and (ii) optimizing client-centric data movement to minimize I/O load request latency between clients and servers, i.e., allocating I/O resources efficiently in service to a single application and job run. Moreover, existing approaches that require application changes limit wide-spread adoption in commercial or proprietary deployments. We propose iez, an “end-to-end control plane” where clients transparently and adaptively write to a set of selected I/O servers to achieve balanced data placement. Our control plane leverages realtime load information for distributed storage server global data placement while our design model leverages trace-based optimization techniques to minimize I/O load request latency between clients and servers. We evaluate our proposed system on an experimental cluster for two common use cases: synthetic I/O benchmark IOR for large sequential writes and a scientific application I/O kernel, HACC-I/O. Results show read and write performance improvements of up to 34% and 32%, respectively, compared to the state of the art.

Wadhwa, Bharti↗
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 OSTI2026

Retrofit Energy Analysis and Central Thermal modeling (REACT) v1.0

REACT is a website designed to simplify the analysis and decision-making process for retrofitting existing central plant heating and cooling systems with advanced heat pump technologies. The tool evaluates the technical and economic viability of replacing traditional boilers with various options including water-to-water or air-to-water heat pumps, which can provide efficient and lower-cost heating and cooling. It allows users to compare current central plant configurations with retrofit scenarios, assessing energy consumption, life-cycle costs, and environmental impact. Retrofitting traditional boiler and chiller systems with water-to-water or air-to-water heat pumps can significantly reduce energy consumption and lower lifecycle costs. The REACT provides: User-Friendly Tools: A user friendly web interface for quick, intuitive analysis accessible to non-experts. Advanced Modeling: A Python-powered engine for detailed parametric studies, optimization, and research applications. Comprehensive Analysis: Lifecycle cost evaluation, energy consumption modeling, and environmental impact assessment. Visual Insights: A variety of plots to visualize system performance and design trade-offs. The engine for the website (REACT) bases on several Python libraries, and the website will be hosted on an ETA server.

Kim, Donghun [Lawrence Berkeley National Laborator↗
DOE OSTI2018

Energy savings and usability of zero-client computing in office settings

This study provides a detailed comparison of the power consumption, usability, and applicability of virtual machines (VMs) - accessed through zero-client devices - and traditional laptop computers in office settings. The study analyzed high-level plug loads across two office spaces, one using traditional laptops, the other using zero clients. In addition, the individual power consumption of four workstations was monitored and compared. Each workstation user switched between using a laptop or zero client for one week and then using the alternate system the second week. Results of the high-level and workstation analysis show that average workstation plug loads are lower for occupants using zero clients. However, this does not include power consumed by the data center managing VMs. This study calculates the affiliated data center power draw of VMs and shows that server-related loads push total zero-client computing energy higher than that of traditional laptops. Finally, a questionnaire was administered to building occupants to determine the appropriateness of zero-client computing for various user groups, with the results indicating that VMs are most appropriate for more basic software functions. The findings of this study suggest that advances in server technology can help improve both the overall efficiency and the usability of zero-client computing.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗