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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Evaluation of Topology-Aware Broadcast Algorithms for Dragonfly Networks

Two-tiered direct network topologies such as Dragonflies have been proposed for future post-petascale and exascale machines, since they provide a high-radix, low-diameter, fast interconnection network. Such topologies call for redesigning MPI collective communication algorithms in order to attain the best performance. Yet as increasingly more applications share a machine, it is not clear how these topology-aware algorithms will react to interference with concurrent jobs accessing the same network. In this paper, we study three topology-aware broadcast algorithms, including one designed by ourselves. We evaluate their performance through event-driven simulation for small- and large-sized broadcasts (in terms of both data size and number of processes). We study the effect of different routing mechanisms on the topology-aware collective algorithms, as well as their sensitivity to network contention with other jobs. Our results show that while topology-aware algorithms dramatically reduce link utilization, their advantage in terms of latency is more limited.

Dorier, Matthieu↗
DOE OSTI2019

Software Defined Networking for HPC Interconnects and its Extension across Domains (Final Technical Report for DE-SC0016039)

This project seeks to incorporate OpenFlow-style Software Defined Networking capability into existing High Performance Computing (HPC) interconnect technology, InfiniBand, and to extend such capability beyond one domain. We develop SDN-enhanced InfiniBand that supports OpenFlow functionality for HPC interconnects, investigate the required techniques and the potential benefits for applying SDN on different types of HPC interconnects, and develop software to control the network and expose SDN capability to external networks through the OpenFlow interface. We have also developed techniques to evaluate communication characteristics of parallel applications, which determines the workload on interconnects, investigated newly proposed Interconnection network technologies including new network topologies and routing schemes, and designed novel interconnect routing and modeling techniques. Major activities and findings are summarized as follows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Efficient hybrid explicit-implicit learning for multiscale problems

Splitting method is a powerful method to handle application problems by splitting physics, scales, domain, and so on. Many splitting algorithms have been designed for efficient temporal discretization. Here, in this paper, our goal is to use temporal splitting concepts in designing machine learning algorithms and, at the same time, help splitting algorithms by incorporating data and speeding them up. We propose a machine learning assisted splitting scheme which improves the efficiency of the scheme meanwhile preserves the accuracy. We consider a recently introduced multiscale splitting algorithms, where the multiscale problem is solved on a coarse grid. To approximate the dynamics, only a few degrees of freedom are solved implicitly, while others explicitly. This splitting concept allows identifying degrees of freedom that need implicit treatment. In this paper, we use this splitting concept in machine learning and propose several strategies. First, the implicit part of the solution can be learned as it is more difficult to solve, while the explicit part can be computed. This provides a speed-up and data incorporation for splitting approaches. Secondly, one can design a hybrid neural network architecture because handling explicit parts requires much fewer communications among neurons and can be done efficiently. Thirdly, one can solve the coarse grid component via PDEs or other approximation methods and construct simpler neural networks for the explicit part of the solutions. We discuss these options and implement one of them by interpreting it as a machine translation task. This interpretation of the splitting scheme successfully enables us using the Transformer since it can perform model reduction for multiple time series and learn the connection between them. We also find that the splitting scheme is a great platform to predict the coarse solution with insufficient information of the target model: the target problem is partially given and we need to solve it through a known problem which approximates the target. Our machine learning model can incorporate and encode the given information from two different problems and then solve the target problems. We conduct four numerical examples and the results show that our method is stable and accurate.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Union: An Automatic Workload Manager for Accelerating Network Simulation

With the rapid growth of the machine learning applications, the workloads of future HPC systems are anticipated to be a mix of scientific simulation, big data analytics, and machine learning applications. Simulation is a great research vehicle to understand the performance implications of co-running scientific applications with big data and machine learning workloads on large-scale systems. In this paper, we present Union, a workload manager that provides an automatic framework to facilitate hybrid workload simulation in CODES. Furthermore, we use Union, along with CODES, to investigate various hybrid workloads composed of traditional simulation applications and emerging learning applications on two dragonfly systems. The experiment results show that both message latency and communication time are important performance metrics to evaluate network interference. Network interference on HPC applications is more reflected by the message latency variation, whereas ML application performance depends more on the communication time.

heterogeneous workloads↗
DOE OSTI2014

A Communication-Optimal Framework for Contracting Distributed Tensors

Tensor contractions are extremely compute intensive generalized matrix multiplication operations encountered in many computational science fields, such as quantum chemistry and nuclear physics. Unlike distributed matrix multiplication, which has been extensively studied, limited work has been done in understanding distributed tensor contractions. In this paper, we characterize distributed tensor contraction algorithms on torus networks. We develop a framework with three fundamental communication operators to generate communication-efficient contraction algorithms for arbitrary tensor contractions. We show that for a given amount of memory per processor, our framework is communication optimal for all tensor contractions. We demonstrate performance and scalability of our framework on up to 262,144 cores of BG/Q supercomputer using five tensor contraction examples.

communication efficiency↗
DOE OSTI2025

Implementing Directive-Based Deferred Execution for Effective Network Aggregation

Remote direct memory access technology provides an efficient mechanism for one-sided communication that can be leveraged to implement a distributed shared memory programming model. However, when applications generate large numbers of small, irregular messages, network congestion often arises. Existing solutions address this small message problem by facilitating message aggregation but typically require disruptive code transformations that detract from the algorithmic intent of applications, or can be limited by dependent operations on aggregated data between synchronisation points. A solution is to use a directive-assisted approach that enables compilers to transform code dependent on aggregated communication for deferred execution. This paper presents an algorithm that a compiler can use to implement and optimise deferred execution for code dependent on aggregated data, based on an "aggregation context" extension for the OpenSHMEM partitioned global address space library. This new capability addresses a key challenge of message aggregation, allowing its full potential to reduce network congestion and enhance programmability to be realised.

Welch, Aaron [ORNL]↗
DOE OSTI2023

Space-based quantum networks are an essential component of future architecture for distributed quantum computers and quantum-enhanced secure communication

Space-based quantum links show great promise for connecting and communicating between quantum computers over ultra-long distances without the high loss incurred through fiber. A successful US quantum satellite would require large investment, national priority, and a diverse set of expertise. But, it would deliver US-owned quantum links that would allow for quantum-enhanced secure communications and the ability to connect quantum computers over long distances.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Inventory of Public Key Cryptography in US Electric Vehicle Charging

Electric vehicles (EVs) and charging infrastructure are networked systems, which employ high-level communications in support of charging and grid service decisions. Public key cryptography (PKC) underlies much of the security and privacy protections of the information exchange. We are entering a new epoch where quantum computing threats must be seriously considered. A sufficiently large quantum computer, so named Cryptographically Relevant Quantum Computer (QRQC), will be able to perform the mathematical operations to efficiently attack the underpinnings of traditional PKC, thus jeopardizing the digital foundations for trust, communications security, and data security. Estimates suggest a QRQC can break public key encryption and digital signatures in the manner of tens to hundreds of hours, compared to traditional computing that would demand more than 10 18 years in a brute force-style attack. A consensus belief of quantum theorists, quantum experimenters, and cryptographers suggest that the quantum threat will be likely realized in the next twenty years. To address the threat, post-quantum cryptography, which is cryptosystems that are designed to be secure against both traditional and quantum computing threats, must be adopted. Migration from traditional PKC to quantum-resilient cryptography is a global undertaking and likely represents the largest transition in computing history. The nascent state of EV public key infrastructure, combined with limited adoption of the vehicle secure charging features, presents an opportunity to establish a preference for quantum-resistant cryptography as a step on the migration path. Delays will stunt the efforts as rapidly accelerating EVs sales and huge infrastructure investments will create large growing bases of long-lived vehicles and infrastructure. Migration preparations can commence while NIST continues the process to standardize post-quantum cryptography (PQC), which are quantum-resilient algorithms designed to be secure against traditional and quantum computing threats. The first step in preparing EV charging is to identify the presence of traditional public key cryptography algorithms and applications. With this objective in mind, this report is intended to advise the vehicle manufacturers, charging station manufacturers, charging station operators, charge network providers and other EV charging stakeholders with information on traditional PKC application and the potential risks when PKC becomes insecure. This report, the first in a series of reports discussing the topics existing at the confluence of post-quantum cryptography adoption and EV charging, identifies traditional public key applications employed and identifies potential consequences of leaving EV charging infrastructure vulnerable to quantum computing. The focus remains squarely on the of EV charging and infrastructure with respect to PKC and is believed by the authors to complement the NIST SP 1800-38 Migration to Post-Quantum Cryptography. While the report is centered on infrastructure, there are implications to vehicles.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Distributed Inference with Sparse and Quantized Communication

Here, we consider the problem of distributed inference where agents in a network observe a stream of private signals generated by an unknown state, and aim to uniquely identify this state from a finite set of hypotheses. We focus on scenarios where communication between agents is costly, and takes place over channels with finite bandwidth. To reduce the frequency of communication, we develop a novel event-triggered distributed learning rule that is based on the principle of diffusing low beliefs on each false hypothesis. Building on this principle, we design a trigger condition under which an agent broadcasts only those components of its belief vector that have adequate innovation, to only those neighbors that require such information. We prove that our rule guarantees convergence to the true state exponentially fast almost surely despite sparse communication, and that it has the potential to significantly reduce information flow from uninformative agents to informative agents. Next, to deal with finite-precision communication channels, we propose a distributed learning rule that leverages the idea of adaptive quantization. We show that by sequentially refining the range of the quantizers, every agent can learn the truth exponentially fast almost surely, while using just 1 bit to encode its belief on each hypothesis. For both our proposed algorithms, we rigorously characterize the trade-offs between communication-efficiency and the learning rate.

42 ENGINEERING↗
DOE OSTI2020

Global functional connectivity density alterations in patients with bipolar disorder with auditory verbal hallucinations and modest short‐term effects of transcranial direct current stimulation augmentation treatment—Baseline and follow‐up study

Abstract Objectives To investigate the neuroimaging characteristics of auditory verbal hallucinations (AVHs) in patients with bipolar disorder (BP) experiencing depressive episodes with and without AVHs, and alterations in those characteristics after transcranial direct current stimulation (tDCS). Methods For a baseline pilot study, we recruited 80 patients with BP and depressive status (40 with and 40 without AVHs), and 40 healthy controls (HCs). Their global functional connectivity density (gFCD) was screened by functional magnetic resonance imaging (fMRI). Voxel‐wise one‐way analysis of covariance (ANCOVA) was conducted to detect intergroup differences in gFCD. In a follow‐up study, the effects of 5 weeks of tDCS augmentation treatment on clinical symptoms and gFCD were assessed in the 40 BP patients with AVHs. Results Compared to HCs, BP patients with and without AVHs exhibited increased gFCD in the central parietal lobe, insular lobe, and middle cingulate cortex, with decreased gFCD in the posterior parietal cortex, lateral prefrontal cortex, and occipital lobe (all bilateral). Only patients with AVHs showed increased gFCD in the Broca and Wernicke regions, and decreased gFCD in the hippocampus (all bilateral). After 5 weeks of tDCS, AVHs were slightly alleviated and gFCD abnormalities in the hippocampus were mildly attenuated. Conclusions Patients with BP and AVHs showed disturbances in the brain's communication capacity mainly in the left frontoparietal network, control network, and memory circuitry. Five weeks of tDCS alleviated AVHs slightly, without improving depressive symptoms, and attenuated hippocampal gFCD alterations in these patients.

Zhuo, Chuanjun↗
DOE OSTI2014

Proposal for efficient mode converter based on cavity quantum electrodynamics dark mode in a semiconductor quantum dot coupled to a bimodal microcavity

The ability to engineer and convert photons between different modes in a solid-state approach has extensive technological implications not only for classical communication systems but also for future quantum networks. In this paper, we put forward a scheme for coherent mode conversion of optical photons by utilizing the intermediate coupling between a single quantum dot and a bimodal photonic crystal microcavity via a waveguide. Here, one mode of the photonic crystal microcavity is coherently driven by an external single-frequency continuous-wave laser field and the two cavity modes are not coupled to each other due to their orthogonal polarizations. The undriven cavity mode is thus not directly coupled to the input driving laser and the only way it can get light is via the quantum dot. The influences of the system parameters on the photon-conversion efficiency are analyzed in detail in the limit of weak probe field and it is found that high photon-conversion efficiency can be achieved under appropriate conditions. It is shown that the cavity dark mode, which is a superposition of the two optical modes and is decoupled from the quantum dot, can appear in such a hybrid optical system. We discuss the properties of the dark mode and indicate that the formation of the dark mode enables the efficient transfer of optical fields between the two cavity modes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Distributed Frequency Control of Prosumer-Based Electric Energy Systems

In this paper, we propose a distributed frequency regulation framework for prosumer-based electric energy systems, where a prosumer (producer-consumer) is defined as an intelligent agentwhich can produce, consume, and/or store electricity. Despite the frequency regulators being distributed, stability can be ensured while avoiding inter-area oscillations using a limited control effort. To achieve this, a fully distributed one-step model-predictive control protocol is proposed and analyzed, whereby each prosumer communicates solely with its neighbors in the network. The efficacy of the proposed frequency regulation framework is shown through simulations on two real-world electric energy systems of different scale and complexity. We show that prosumers can indeed bring frequency and power deviations to their desired values after small perturbations.

Nazari, MH↗
DOE OSTI2020

Advances in High-Power Wireless Charging Systems: Overview and Design Considerations

Wireless charging systems are foreseen as an effective solution to improve the convenience and safety of conventional conductive chargers. As this technology has matured, recent broad applications of wireless chargers to electrified transportation have indicated a trend toward higher power, power density, modularity, and scalability of designs. In this article, commercial systems and laboratory prototypes are reviewed, focusing mostly on the advances in high-power wireless charging systems. The recent endeavors in magnetic pad designs, compensation networks, power electronics converters, control strategies, and communication protocols are illustrated. Both stationary and dynamic (in-motion) wireless charging systems are discussed, and critical differences in their designs and applications are emphasized. On that basis, the comparisons among different solutions and design considerations are summarized to present the essential elements and technology roadmap that will be necessary to support large-scale deployment of high-power wireless charging systems. The review is concluded with the discussion of several fundamental challenges and prospects of high-power wireless power transfer (WPT) systems. Foreseen challenges include utilization of advanced materials, electric and electromagnetic field measurement and mitigation, customization, communications, power metering, and cybersecurity.

25 ENERGY STORAGE↗
DOE OSTI2018

Bell state optimizations for reliable quantum applications

Well-defined and stable quantum networks are essential to realize functional quantum communication applications. In particular, the quantum states must be precisely controlled to produce meaningful results. To counteract the unstable phase shifts in photonic systems, we apply local Bell state measurements to calibrate a non-local quantum channel. The calibration procedure is tested by applying a time encoded quantum key distribution procedure using entangled photons.

Dasari, Venkat↗
DOE OSTI2018

FAODEL

FAODEL is a collection of libraries for implementing data management services on high-performance computing (HPC) platforms. It is composed of several components: (1) an RDMA portability library called NNTI for transporting data, (2) an in-application, restful API service, (3) a memory management library for network memory named Lunasa, (4) an asynchronous communication engine, and (5) a distributed key/blob service names Kelpie. Application developers may use these libraries to move data between the distributed nodes of one job to another, as well as to distributed storage resources. FAODEL is different than other communication libraries in that it supports both intra- and inter-job communication, features event-driven mechanisms, and provides high-level primitives for developers to reason about a platform’s distributed resources.

ECP↗
DOE OSTI2024

Quantum teleportation coexisting with classical communications in optical fiber

The ability for quantum and conventional networks to operate in the same optical fibers would aid the deployment of quantum network technology on a large scale. Quantum teleportation is a fundamental operation in quantum networking, but has yet to be demonstrated in fibers populated with high-power conventional optical signals. Here we report, to the best of our knowledge, the first demonstration of quantum teleportation over fibers carrying conventional telecommunications traffic. Quantum state transfer is achieved over a 30.2-km fiber carrying 400-Gbps C-band classical traffic with a Bell state measurement performed at the fiber’s midpoint. To protect quantum fidelity from spontaneous Raman scattering noise, we use optimal O-band quantum channels, narrow spectro-temporal filtering, and multi-photon coincidence detection. Fidelity is shown to be well maintained with an elevated C-band launch power of 18.7 dBm for the single-channel 400-Gbps signal, which we project could support multiple classical channels totaling many terabits/s aggregate data rates. These results show the feasibility of advanced quantum and classical network applications operating within a unified fiber infrastructure.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗