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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 OSTI2020

Reducing communication in algebraic multigrid with multi-step node aware communication

Algebraic multigrid (AMG) is often viewed as a scalable [Formula: see text] solver for sparse linear systems. Yet, AMG lacks parallel scalability due to increasingly large costs associated with communication, both in the initial construction of a multigrid hierarchy and in the iterative solve phase. This work introduces a parallel implementation of AMG that reduces the cost of communication, yielding improved parallel scalability. It is common in Message Passing Interface (MPI), particularly in the MPI-everywhere approach, to arrange inter-process communication, so that communication is transported regardless of the location of the send and receive processes. Performance tests show notable differences in the cost of intra- and internode communication, motivating a restructuring of communication. In this case, the communication schedule takes advantage of the less costly intra-node communication, reducing both the number and the size of internode messages. Node-centric communication extends to the range of components in both the setup and solve phase of AMG, yielding an increase in the weak and strong scaling of the entire method.

Computer Science↗
DOE OSTI2022

Peer-to-Peer Communication Trade-Offs for Smart Grid Applications: Preprint

Peer-to-peer energy management systems for smart grids require developers to consider the trade-offs between the amount of communication traffic generated and the quality and speed of convergence of the control algorithms that are deployed. Employing a fully connected communication causes messages to scale exponentially with the number of nodes, while using a sparse connectivity causes less information dissemination leading to degradation of the algorithm performance. The best communication topology for a particular application lies somewhere in between and often requires empirical evaluation by application designers. Existing methods do not put focus on the needs for smart grid applications, which is information dissemination throughout the network and they do not provide a flexible solution for application developers to prototype and deploy different topologies without modifying the application code. This paper introduces a configurable virtual communication topology framework TopLinkMgr, allowing users to specify any chosen communication topology and deploy peer-to-peer applications using it. It also introduces a self-adaptive, fault-tolerant topology management algorithm, Bounded Path Dissemination that can ensure the dissemination of information to all peers within a specified threshold for a sparsely connected topology. Experiments show that the algorithm improves on convergence speed and accuracy over state-of-the-art methods and is also robust against node failures. The results indicate the possibility of achieving a close-to optimal convergence without overloading the network allowing the realization of peer-to-peer control platforms covering larger and more complex power systems.

Bounded Path Dissemination↗
DOE OSTI2019

Coordination of Transmission, Distribution and Communication Systems for Prompt Power System Recovery after Disasters:Report – Grid and Communication Interdependency Review and Characterization of Typical Communication Systems

In this report, we first reviewed communication technologies and systems for the grid as well as their roles (impact) in restoration, aiming to fill the knowledge gap of interdependence between the physical grid and the communication system. We also proposed several key characteristics and metrics of typical communication systems based on analysis of a realistic communication system. These metrics and characteristics will be used as input for developing or generating synthetic communication systems in the next phase. Lastly, we reviewed methods of creating synthetic power system models and proposed improvements to one potential method that will be considered in the next phase of the project.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Analysis of System and Interoperability Impact from Securing Communications for Distributed Energy Resources

As the power grid incorporates increasing amounts of distributed energy resources (DER) that provide new generation sources, new opportunities are created for improving operation of the grid while large challenges also arise for preserving grid reliability and security. To improve grid performance, DERs can be utilized to provide important support functionality, such as supporting frequency and voltage levels, especially if they are assisted by communication schemes as part of an advanced distribution management system (ADMS). Unfortunately, such connectivity and grid support functionality also creates additional cyber security risk with the potential for degradation of grid services, especially under conditions with high amounts of distributed generation. This paper will first discuss the communications needed by DERs to support system and interoperability objectives, as well as the security requirements and impact of securing these communications. Some common security mechanisms are discussed in relation to DERs, and a simulated 15-bus model of a distribution feeder is used to demonstrate aspects of the DER communications and impact to grid performance. These results help to advance understanding of the benefits, requirements, and mechanisms for securely implementing DER communications while ensuring that grid reliability is maintained.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Power and Communications Hardware-in-the-Loop CPS Architecture and Platform for DER Monitoring and Control Applications: Preprint

The rapid growth of distributed energy resources (DERs) has prompted increasing interest in the monitoring and control of DERs through hybrid smart grid communications. The deployment of communications and computation has transformed the traditional physical power grid into a smart cyber-physical system (CPS). To fully understand the interdependency between physical grid and cyber netowrks, this study designed a power and communications hardware-in-the-loop (PCommHIL) CPS architecture, which enables the flexible verification of DER monitoring and control with hybrid communications architectures and Internet protocols. Design, development and case study of a PCommHIL testbed for the DER coordination are discussed in detail, and the proposed platform integrates DER devices, Advanced Metering Infrastructures (AMIs), and a suite of hybrid communications networks for distribution automation applications. Case study on DER situational awareness and Volt-Var control validates the efficacy of this proposed PCommHIL platform with hybrid communications designs. Results show that the HAN communication technologies play a critical role in hybrid designs and it is the bottleneck for DER applications. High performance communication technologies are highly recommended to be applied in the HAN for enhanced monitoring and real-time control of DERs.

AMIs↗
DOE OSTI2021

Systems and methods for electronic communication with a device using an unknown communications protocol

Various technologies for communicating with systems that communicate using an unknown communications protocol are described herein. A transceiver intercepts a plurality of communications exchanged between two or more transceivers on a communications network. Pattern-recognition algorithms are executed over the plurality of communications, and features of an unknown communications protocol that governs the communications between the two or more transceivers are inferred based upon output of the pattern-recognition algorithms. A communication is formatted based upon the inferred features of the unknown communications protocol, and the communication transmitted to one or more systems on the communications network by way of the transceiver. The communication at least partially conforms to the unknown communications protocol, and so may be interpreted by systems on the network.

Patel, Silpan M.↗
DOE OSTI2019

Improved Accuracy for Automated Communication Pattern Characterization Using Communication Graphs and Aggressive Search Space Pruning

An understanding of a parallel application’s communication behavior is useful for a range of activities including debugging and optimization, job scheduling, target system selection, and system design. Because it can be challenging to understand communication behavior, especially for those who lack expertise or who are not familiar with the application, I and two colleagues recently developed an automated, search-based approach for recognizing and parameterizing application communication behavior using a library of common communication patterns. This initial approach was effective for characterizing the behavior of many workloads, but I identified some combinations of communication patterns for which the method was inefficient or would fail. In this paper, I discuss one such troublesome pattern combination and propose modifications to the recognition method to handle it. Specifically, I propose an alternative approach that uses communication graphs instead of traditional communication matrices to improve recognition accuracy for collective communication operations, and that uses a non-greedy recognition technique to avoid search space dead-ends that trap the original greedy recognition approach. My modified approach uses aggressive search space pruning and heuristics to control the potential for state explosion caused by its non-greedy pattern recognition method. I demonstrate the improved recognition accuracy and pruning efficacy of the modified approach using several synthetic and real-world communication pattern combinations.

Roth, Philip↗
DOE OSTI2023

Optimizing Irregular Communication with Neighborhood Collectives and Locality-Aware Parallelism

Irregular communication often limits both the performance and scalability of parallel applications. Typically, applications individually implement irregular communication as point-to-point, and any optimizations are integrated directly into the application. As a result, these optimizations lack portability. It is difficult to optimize point-to-point messages within MPI, as the interface for single messages provides no information on the collection of all communication to be performed. However, the persistent neighbor collective API, released in the MPI 4 standard, provides an interface for portable optimizations of irregular communication within MPI libraries. This paper presents methods for implementing existing optimizations for irregular communication within neighborhood collectives, analyzes the impact of replacing point-to-point communication in existing codebases such as Hypre BoomerAMG with neighborhood collectives, and finally shows up to a 1.38x speedup on sparse matrix-vector multiplication communication within a BoomerAMG solve through the use of our optimized neighbor collectives. Here, the authors analyze three implementations of persistent neighborhood collectives for Alltoallv: an unoptimized wrapper of standard point-to-point communication, and two locality-aware aggregating methods. The second locality-aware implementation exposes an non-standard interface to perform additional optimization, and the authors present the additional 0.07x speedup from the extended interface. All optimizations are available in an open-source codebase, MPI Advance, which sits on top of MPI, allowing for optimizations to be added into existing codebases regardless of the system MPI install.

AMG↗
DOE OSTI2022

Harvesting Planck radiation for free-space optical communications in the long-wave infrared band

We demonstrate a free-space optical communication link with an optical transmitter that harvests naturally occurring Planck radiation from a warm body and modulates the emitted intensity. The transmitter exploits an electro-thermo-optic effect in a multilayer graphene device that electrically controls the surface emissivity of the device resulting in control of the intensity of the emitted Planck radiation. We design an amplitude-modulated optical communication scheme and provide a link budget for communications data rate and range based on our experimental electro-optic characterization of the transmitter. Finally, we present an experimental demonstration achieving error-free communications at 100 bits per second over laboratory scales.

Weinstein, Haley A.↗
DOE OSTI2019

Quantum-secured communications overlay for optical fiber communications networks

Quantum secure communication systems communicate quantum signals for quantum key distribution and classical signals with encrypted data and commands via a single optical fiber. In some systems, the single fiber carries classical data in both directions along with quantum communications. For example, quantum keys can be used to encrypt packets for bidirectional communication between two parties. In other systems, a single fiber is used for one way classical communications and quantum communications. The communication systems are secured using a security parameter based on the quantum and classical communications across the optical fiber.

Nordholt, Jane Elizabeth↗
DOE OSTI2020

Reducing Communication in Graph Neural Network Training

Graph Neural Networks (GNNs) are powerful and flexible neural networks that use the naturally sparse connectivity information of the data. GNNs represent this connectivity as sparse matrices, which have lower arithmetic intensity and thus higher communication costs compared to dense matrices, making GNNs harder to scale to high concurrencies than convolutional or fully-connected neural networks. Here, we introduce a family of parallel algorithms for training GNNs and show that they can asymptotically reduce communication compared to previous parallel GNN training methods. We implement these algorithms, which are based on 1D, 1. 5D, 2D, and 3D sparse-dense matrix multiplication, using torch.distributed on GPU-equipped clusters. Our algorithms optimize communication across the full GNN training pipeline. We train GNNs on over a hundred GPUs on multiple datasets, including a protein network with over a billion edges.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Distributed Energy Resources Coordination over Time-varying Directed Communication Networks

In this paper, we consider an optimal coordination problem for distributed energy resources (DERs) including distributed generators and energy storage devices. We first propose an algorithm based on the push-sum and gradient method to optimally coordinate storage devices and distributed generators in a distributed manner. In the proposed algorithm, each DER only maintains a set of variables and updates them through information exchange with a few neighboring DERs over a time-varying directed communication network. We show that the proposed distributed algorithm with appropriately chosen step-sizes solve the optimal DER coordination problem if the time-varying directed communication network is uniformly jointly strongly connected, which is a mild condition on the connectivity of communication topologies. The proposed distributed algorithm is illustrated and validated by numerical simulations.

Yang, Tao↗
DOE OSTI2024

Communication Lower Bounds and Optimal Algorithms for Multiple Tensor-Times-Matrix Computation

Multiple tensor-times-matrix (Multi-TTM) is a key computation in algorithms for computing and operating with the Tucker tensor decomposition, which is frequently used in multidimensional data analysis. Here, we establish communication lower bounds that determine how much data movement is required (under mild conditions) to perform the Multi-TTM computation in parallel. The crux of the proof relies on analytically solving a constrained, nonlinear optimization problem. We also present a parallel algorithm to perform this computation that organizes the processors into a logical grid with twice as many modes as the input tensor. We show that, with correct choices of grid dimensions, the communication cost of the algorithm attains the lower bounds and is therefore communication optimal. Finally, we show that our algorithm can significantly reduce communication compared to the straightforward approach of expressing the computation as a sequence of tensor-times-matrix operations when the input and output tensors vary greatly in size.

HBL-inequalities↗
DOE OSTI2024

SEAS Communication Engine: An Extensible, Flexible Wrapper for Co-Simulation Agents

When modeling and analyzing the power grid and other large scale systems, researchers often express scenarios as optimization problems and feed them into advanced software solvers. In order to allow multiple solvers to communicate with each other and share data from different domains, the National Renewable Energy Laboratory (NREL) and associated Department of Energy (DOE) labs have developed a software framework called the Hierarchical Engine for Large-scale Infrastructure Co-Simulation (HELICS). HELICS allows cosimulation via a collection of client libraries for different languages that can be called from the appropriate optimization software. However, these client libraries do not provide a higher level of abstraction beyond reading and writing data off of the shared HELICS bus. In this paper, we describe a new software library called the SEAS Communication Engine that exposes a higher-level API for running cosimulation problems. The SEAS Engine provides a class-based abstraction on top of the Python HELICS client, in order to allow users to implement their domain-specific cosimulations without needing to interact with core HELICS primitives. This will make adoption of HELICS and cosimulation in general easier, by exposing a simpler API. In the second part of the paper, we validate our library on a collection of different simulation examples, including the canonical IEEE 13 Bus Feeder. Lastly, we demonstrate using the SEAS Engine to directly call domain-specific code written in the Julia programming language. Our hope is that this will serve as a template for easily calling software in different programming languages via the SEAS Engine, thereby avoiding code duplication and complexity.

co-simulation↗
DOE OSTI2024

Robust Free-Space Optical Communication Utilizing Polarization for the Advancement of Quantum Communication

Free-space optical (FSO) communication can be subject to various types of distortion and loss as the signal propagates through non-uniform media. In experiment and simulation, we demonstrate that the state of polarization and degree of polarization of light passed though underwater bubbles, causing turbulence, is preserved. Our experimental setup serves as an efficient, low cost alternative approach to long distance atmospheric or underwater testing. We compare our experimental results with those of simulations, in which we model underwater bubbles, and separately, atmospheric turbulence. Our findings suggest potential improvements in polarization based FSO communication schemes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Devices, base stations, and methods for communicating scheduling requests via an underlay control channel in a wireless communication system

Wireless communication systems, base stations, and user equipment are disclosed that enable communication of scheduling requests via an underlay control channel that has an energy below a noise level of the spectrum. The scheduling requests may be sent and received at any time, including during downlink and uplink data communication periods of the base station.

42 ENGINEERING↗
DOE OSTI2020

Hyperparameter Optimization in Binary Communication Networks for Neuromorphic Deployment

Training neural networks for neuromorphic deployment is non-trivial. There have been a variety of approaches proposed to adapt back-propagation or back-propagation-like algorithms appropriate for training. Considering that these networks often have very different performance characteristics than traditional neural networks, it is often unclear how to set either the network topology or the hyperparameters to achieve optimal performance. In this work, we introduce a Bayesian approach for optimizing the hyperparameters of an algorithm for training binary communication networks that can be deployed to neuromorphic hardware. We show that by optimizing the hyperparameters on this algorithm for each dataset, we can achieve improvements in accuracy over the previous state-of-the-art for this algorithm on each dataset (by up to 15 percent). This jump in performance continues to emphasize the potential when converting traditional neural networks to binary communication applicable to neuromorphic hardware.

Parsa, Maryam↗