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At least 721 records · Page 40

Feasibility Study of Distributed Decision-Making on the Edge for Urban Air Mobility

The Concept of Operations for Urban Air Mobility (UAM) put forward by FAA, NASA, and several industry stakeholders acknowledges the diversity and complexity in UAM operations and, thereby, envisions a federated architecture for UAM management. In this architecture, the decision-making is distributed to a set of service providers who collectively manage the shared airspace usage by different stakeholders. This notionally brings autonomy closer to the UAM businesses and encourages to explore the feasibility of decision making on the very edge, which is the topic of the presented research. This paper reports research conducted on the hypothesis based on which the residual compute capability onboard smart unmanned aerial systems (UASs) is utilized to build situational awareness and resolve conflicts by passive and active coordination among multiple UASs, thereby implementing a layer of distributed autonomy in UAM. Key features of the edge-computing approach involve inter-UAS information exchange, independent assessment of own flight and environmental conditions, and estimation of other UASs’ flight preferences, incorporating machine learning techniques in the last two. Parallel computing on portable graphics processing unit (GPU) enables the machine learning workflow on the edge. A custom-built 3D simulator is used to evaluate the efficacy of the distributed decision-making on the edge. Each edge node, representing a smart UAS, connects to the simulator from a remote location and independently controls the behavior of the corresponding virtual asset in the simulator, analogous to participants in an online multi-player game. The presented edge-computing-based distributed decision-making framework is envisioned to pave the way for collective mobility of autonomous air vehicles in the future shared airspace, while allowing the inclusion of the business preferences of the UAS operators within allowed regulatory limits.

Edge computing↗

Feasibility Study of Distributed Decision-Making on the Edge for Urban Air Mobility

The Concept of Operations for Urban Air Mobility (UAM) put forward by FAA, NASA, and several industry stakeholders acknowledges the diversity and complexity in UAM operations and, thereby, envisions a federated architecture for UAM management. In this architecture, the decision-making is distributed to a set of service providers who collectively manage the shared airspace usage by different stakeholders. This notionally brings autonomy closer to the UAM businesses and encourages to explore the feasibility of decision making on the very edge, which is the topic of the presented research. This paper reports research conducted on the hypothesis based on which the residual compute capability onboard smart unmanned aerial systems (UASs) is utilized to build situational awareness and resolve conflicts by passive and active coordination among multiple UASs, thereby implementing a layer of distributed autonomy in UAM. Key features of the edge-computing approach involve inter-UAS information exchange, independent assessment of own flight and environmental conditions, and estimation of other UASs’ flight preferences, incorporating machine learning techniques in the last two. Parallel computing on portable graphics processing unit (GPU) enables the machine learning workflow on the edge. A custom-built 3D simulator is used to evaluate the efficacy of the distributed decision-making on the edge. Each edge node, representing a smart UAS, connects to the simulator from a remote location and independently controls the behavior of the corresponding virtual asset in the simulator, analogous to participants in an online multi-player game. The presented edge-computing-based distributed decision-making framework is envisioned to pave the way for collective mobility of autonomous air vehicles in the future shared airspace, while allowing the inclusion of the business preferences of the UAS operators within allowed regulatory limits.

Edge computing↗

Shortest Path and Neighborhood Subgraph Extraction on a Spiking Memristive Neuromorphic Implementation

Spiking neuromorphic computers (SNCs) are promising as a post Moore's law technology partly because of their potential for very low power computation. SNCs have primarily been demonstrated on machine learning and neural network applications, but they can also be used for applications beyond machine learning that can leverage SNC properties such as massively parallel computation and collocated processing and memory. Here, we demonstrate two graph problems (shortest path and neighborhood subgraph extraction) that can be solved using SNCs. We discuss the approach for mapping these applications to an SNC. We also estimate the performance of a memristive SNC for these applications on three real-world graphs.

Schuman, Catherine↗

ChatMPI: LLM-Driven MPI Code Generation for HPC Workloads

The Message Passing Interface (MPI) standard plays a crucial role in enabling scientific applications for parallel computing and is an essential component in high-performance computing (HPC). However, implementing MPI code manually—especially applying a proper domain decomposition and communication pattern—is a challenging and error-prone task. We present ChatMPI, an AI assistant for MPI parallelization of sequential C codes. In our analysis, we focus on testing six essential HPC workloads, which are based on Basic Linear Algebra Subprograms levels 1, 2, and 3 as well as sparse, stencil, and iterative operations. We analyze the process of creating ChatMPI by using the ChatHPC library. This lightweight large language model (LLM)–based infrastructure enables HPC experts to efficiently create and supervise trustworthy AI capabilities for critical HPC software tasks. We study the data required for training (fine-tuning) ChatMPI to generate parallel codes that not only use MPI syntax correctly but also apply HPC techniques to reduce memory communication and maximize performance by using proper work decomposition. With a relatively small training dataset composed of a few dozen prompts and fewer than 15 minutes of fine-tuning on one node equipped with two NVIDIA H100 GPUs, ChatMPI elevates trustworthiness for MPI code generation of current LLMs (e.g., Code Llama, ChatGPT-4o and ChatGPT 5). Additionally, we evaluate the performance of the MPI codes generated by ChatMPI in comparison with the ones generated by ChatGPT-4o and ChatGPT-5. The codes generated by ChatMPI provide up to a 4 × boost in performance by using better problem decomposition, communication patterns, and HPC techniques (e.g., communication avoiding).

Valero Lara, Pedro [ORNL] (ORCID:0000000214794310)↗

NWChem

NWChem is a computational chemistry software package which includes quantum chemical and molecular dynamics functionality. It aims to be scalable both in its ability to treat large problems efficiently, and in its usage of available parallel computing resources.

Apra, Edoardo [Pacific Northwest National Laborato↗

NWChem

NWChem is a computational chemistry software package which includes quantum chemical and molecular dynamics functionality. It aims to be scalable both in its ability to treat large problems efficiently, and in its usage of available parallel computing resources.

Apra, Edoardo [Pacific Northwest National Laborato↗

Computational Aero-acoustics As a Tool For Turbo-machinery Noise Reduction

This talk will provide an overview of the field of computational aero-acoustics and its use in fan noise prediction. After a brief history of computational fluid dynamics, some of the recent developments in computational aero-acoustics will be explored. Computational issues concerning sound wave production, propagation, and reflection in practical turbo-machinery applications will be discussed including: (a) High order/High Resolution Numerical Techniques. (b) High Resolution Boundary Conditions. [c] MIMD Parallel Computing. [d] Form of Governing Equations Useful for Simulations. In addition, the basic design of our Broadband Analysis Stator Simulator (BASS) code and its application to a 2 D rotor wake-stator interaction will be shown. An example of the noise produced by the wakes from a rotor impinging upon a stator cascade will be shown.

Dyson, Rodger W.↗

Enabling Thread Safety and Parallelism in the Program to Optimize Simulated Trajectories II

Development of the Program to Optimize Simulated Trajectories (POST) began in the 1970s. Since then, it has become widely utilized across NASA, industry, and academia to solve a variety of atmospheric ascent and entry problems. Its successor, POST2, has undergone many upgrades since its release in the 1990s. Recently, there has been an increasing desire to take advantage of the advances in parallel computing for both offline and online systems. Thus, modifications were made to allow POST2 to simulate multiple trajectories simultaneously without adversely affecting results. This capability is leveraged to calculate optimization solutions in parallel as opposed to sequentially. A demonstration of the benefits is presented using a small set of POST2 regression tests, as well as a project simulating a human-scale Lunar lander.

R. Anthony Williams↗

Enabling Thread Safety and Parallelism in the Program to Optimize Simulated Trajectories II

Development of the Program to Optimize Simulated Trajectories (POST) began in the 1970s. Since then, it has become widely utilized across NASA, industry, and academia to solve a variety of atmospheric ascent and entry problems. Its successor, POST2, has undergone many upgrades since its release in the 1990s. Recently, there has been an increasing desire to take advantage of the advances in parallel computing for both offline and online systems. Thus, modifications were made to allow POST2 to simulate multiple trajectories simultaneously without adversely affecting results. This capability is leveraged to calculate optimization solutions in parallel as opposed to sequentially. A demonstration of the benefits is presented using a small set of POST2 regression tests, as well as a project simulating a human-scale Lunar lander.

Anthony Williams↗

Practical Implementation of GPU-based Computing at the Grid Edge for Resilience Scenarios

This paper presents a practical implementation of GPU-accelerated computing at the grid edge to enhance power system resilience through next-generation smart meters. Advanced Metering Infrastructure (AMI) systems rely predominantly on centralized processing architectures, which limit real-time response capabilities during grid disturbances. This work proposes the integration of GPU-enabled computational platforms directly within smart meter to enable local execution support for power system analytics, fault detection algorithms, and optimization routines. The proposed framework uses the Julia programming language to leverage highperformance parallel computing capabilities while maintaining code portability and development efficiency. We use two experimental scenarios to benchmark the computational feasibility of this approach: sparse linear system solutions representative of power flow analyses, and multi-stage production cost simulations incorporating unit commitment and economic dispatch operations. Results demonstrate that computationally intensive power system algorithms, such as those supporting resilience scenario calculations, can be effectively executed at the distribution edge using commercially available embedded GPU hardware. Keywords—GPU acceleration, edge computing, smart meters, grid resilience, AMI, resilience.

De Souza, Reubun [School of Electrical Engineering↗

Progress Toward Generation of a Navier-Stokes Database for a Harrier in Ground Effect

The Harrier YAV-8B aircraft is capable of vertical and short-field take-off and landing (V/STOL) by directing its four exhaust nozzles toward the ground, or conventional flight by rotating its nozzles into a horizontal position. The British Royal Air Force and the United States Marine Corps have used this aircraft for more than 30 years to provide a quick reaction time for troop support, and reduce the need for long runways. The success of this powered-lift (PL) vehicle has also prompted the more recent design of the Joint Strike Fighter (JSF). However there are significant safety issues that must be addressed when operating a PL vehicle in close proximity to the ground. Hot Gas Ingestion (HGI) by the inlets can result in a rapid loss of powered lift; and high-speed jet flows along the ground plane can induce low pressures underneath the vehicle, causing a 'suck-down' effect. Under these conditions, departure from controlled flight may occur. Moreover, unsteady ground vortices and jet fountains can affect the aircraft,s controllability and its proximity to ground troops. The viscous, time-dependent flow fields of PL vehicles are difficult to accurately and efficiently predict using Computational Fluid Dynamics (CFD). A number of researchers have used the time-dependent Reynolds-averaged Navier-Stokes (RANS) equations to compute flows for single and multiple jets in a cross-flow. A few have added some geometric complexity to the problem by computing flows for jet-augmented delta wings near a ground plane. Smith et.al. computed for the first time a single RANS solution about a simplified Harrier. This geometry included a fuselage, wing, leading edge root extension (LERX), inlets, and exhaust nozzles. All of these investigations cite two practical problems with computing these flows: 1) the need for improved solution accuracy; and, 2) the need for faster solution methods. We view the need for faster solution methods as key to improving the solution accuracy and making this class of computation more routine. One can hardly refine grids, explore the use of advanced turbulence models, and generate databases when it takes weeks of dedicated computer time for a single solution. Chaderjian, Ahmad, Pandya, and Murman have focused on reducing the time-to-solution for this very difficult and complex problem through process automation and exploitation of parallel computing. They began with the Harrier geometry reported, and added a deflected wing flap and empennage for greater realism. To date more than 80 solutions have been carried out. This paper will describe this process and progress made in reducing the time required to generate a simple longitudinal force and moment database for a Harrier in ground effect. It shows a typical snap-shot from an unsteady streakline animation, where fluid particles are colored by temperature. The ground vortex and a jet-fountain vortex are highlighted. It also shows a similar streakline image, where HGI occurs due to the vehicle in close proximity to the ground. It is show the mean lift coefficient as a function of angle of attack and height. The angle of attack range was 4 deg less than or = alpha less than or = 10 deg with an increment of 1 degree, and the height range was 10 ft less than or = h less than or = 30ft with an increment of 5 feet. This 35 solution database was extended to over 2500 cases using a monotone cubic-spline interpolation procedure. The suck-down effect (reduction of lift near the ground) is highlighted in the figure. The "cushion effect," the conventional reduction of lift as the vehicle moves out of ground effect, is also indicated. All 35 RANS solutions were obtained using 952 Silicon Graphics Origin 2000 and 3000 processors in dedicated mode for one week. Typically, 112 processors were assigned to each case. Some other cases used fewer processors to utilize all available CPUS. The final paper will report on the automation of the solution process, including: grid generation, job monitoring, solution completion criteria, and post processing. Moreover, improvements in parallel efficiency for a dual time-step algorithm for the RANS equations will also be presented. Results will be discussed in detail using unsteady streakline flow visualization to correlate unsteady flow structures with dominant aerodynamic frequencies. The stability derivatives, CL, and CL, will also be presented.

Chaderjian, Neal M.↗

MPMC and MCMD: Free High-Performance Simulation Software for Atomistic Systems

Advancements in parallel computing and hardware have allowed computational exploration of chemical systems of interest with unprecendented accuracy and efficiency. The typical development of molecular simulation software is initially inspired by a particular scientific inquiry. The softwares presented herein, MPMC (Massively Parallel Monte Carlo) and MCMD (Monte Carlo/Molecular Dynamics) were born out of a pursuit to simulate condensed phase physical and chemical interactions in porous materials. MPMC first began in 2005 and has been used for dozens of published results in the literature but has not yet been introduced in a standalone paper. MCMD is a more recent expansion and re-write with some published work, focused on adding molecular dynamics algorithms for transport and other time-dependent properties of chemical systems. Each software functions as a standalone with some unique and other overlapping features. Here, a driving aim of this work is to consider periodic, long-range polarization effects in classical simulation, and both codes are optimized to perform such calculations. Sample results will be presented which highlight methodically that inclusion of explicit polarization produces simulation results with predictive power which in general are in greater agreement with experiment than non-polarizable analogous simulations.

97 MATHEMATICS AND COMPUTING↗

Computing Flow Transition On Parallel Processors

Parallel algorithm developed on multiple-microprocessor computer. Program initiated to develop computer codes capable of directly simulating and mathematically modeling transition process at mach numbers ranging from subsonic to hypersonic. Parallel computers potentially offer reduction of processing time; processing time inversely proportional to number of available processors.

Bokhari, S.↗

Computation of Coupled Thermal-Fluid Problems in Distributed Memory Environment

The thermal-fluid coupling problems are very important to aerospace and engineering applications. Instead of analyzing heat transfer and fluid flow separately, this study merged two well-accepted engineering solution methods, SINDA for thermal analysis and FDNS for fluid flow simulation, into a unified multi-disciplinary thermal fluid prediction method. A fully conservative patched grid interface algorithm for arbitrary two-dimensional and three-dimensional geometry has been developed. The state-of-the-art parallel computing concept was used to couple SINDA and FDNS for the communication of boundary conditions through PVM (Parallel Virtual Machine) libraries. Therefore, the thermal analysis performed by SINDA and the fluid flow calculated by FDNS are fully coupled to obtain steady state or transient solutions. The natural convection between two thick-walled eccentric tubes was calculated and the predicted results match the experiment data perfectly. A 3-D rocket engine model and a real 3-D SSME geometry were used to test the current model, and the reasonable temperature field was obtained.

Wei, H.↗

A parallel algorithm for computing the eigenvalues of a symmetric tridiagonal matrix

A parallel algorithm, called polysection, is presented for computing the eigenvalues of a symmetric tridiagonal matrix. The method is based on a quadratic recurrence in which the characteristic polynomial is constructed on a binary tree from polynomials whose degree doubles at each level. Intervals that contain exactly one zero are determined by the zeros of polynomials at the previous level which ensures that different processors compute different zeros. The signs of the polynomials at the interval endpoints are determined a priori and used to guarantee that all zeros are found. The use of finite-precision arithmetic may result in multiple zeros; however, in this case, the intervals coalesce and their number determines exactly the multiplicity of the zero. For an N x N matrix the eigenvalues can be determined in O(log-squared N) time with N-squared processors and O(N) time with N processors. The method is compared with a parallel variant of bisection that requires O(N-squared) time on a single processor, O(N) time with N processors, and O(log N) time with N-squared processors.

Swarztrauber, Paul N.↗

RAPID

Parallel computer code for the simulator for dynamics of power systems which has the capability to initiate the system and create different faults for the dynamic analysis. The code is based on time-parallel method (Parareal) with Adaptive Method Reduction (AMR). The coarse solvers for the Parareal algorithm include several Semi Analytical Solution methods. Also, Integrated simulation of coupled transmission and distribution systems can be studied.

Simunovic, Srdjan [Oak Ridge National Lab. (ORNL),↗

Application of the hypercube parallel processor to a large-scale moment method code

The applicability of a parallel computing architecture to the solution of a large-scale moment-method code is investigated. Specifically, the NEC (Numerical Electromagnetics Code) method-of-moments scattering program is implemented on a hypercube parallel processor. The accuracy and the increase in the speed of execution on this parallel architecture are demonstrated. The results show a very large reduction in execution time for large problems. The great potential of this parallel processor is shown for interactive solution of large NEC problems as well as other moment-method techniques such as the finite-element method.

Manshadi, Farzin↗

Recognizing Patterns In Log-Polar Coordinates

Log-Hough transform is basis of improved method for recognition of patterns - particularly, straight lines - in noisy images. Takes advantage of rotational and scale invariance of mapping from Cartesian to log-polar coordinates, and offers economy of representation and computation. Unification of iconic and Hough domains simplifies computations in recognition and eliminates erroneous quantization of slopes attributable to finite spacing of Cartesian coordinate grid of classical Hough transform. Equally efficient recognizing curves. Log-Hough transform more amenable to massively parallel computing architectures than traditional Cartesian Hough transform. "In-place" nature makes it possible to apply local pixel-neighborhood processing.

Weiman, Carl F. R.↗