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At least 1,045 records · Page 58

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DOE OSTI2017

Partial differential equations preconditioner resilient to soft and hard faults

We present a domain-decomposition-based preconditioner for the solution of partial differential equations (PDEs) that is resilient to both soft and hard faults. The algorithm reformulates the PDE as a sampling problem, followed by a solution update through data manipulation that is resilient to both soft and hard faults. This reformulation allows us to recast the problem as a set of independent tasks, and exploit data locality to reduce global communication. We discuss two different parallel implementations: (a) a single program multiple data (SPMD) version based on a one-to-one mapping between subdomain and MPI processes responsible for both state and computation; and (b) an asynchronous server–client implementation where all state information is held by the servers and clients are designed solely as computational units. We present a scalability comparison of both implementations under nominal conditions, showing efficiency within ~80% for up to 12,000 cores. We present a resilience analysis under different fault scenarios based on the server–client implementation. This framework provides resiliency to hard faults such that if a client crashes, it stops asking for work, and the servers simply distribute the work among all of the other clients alive. Erroneous subdomain solves (e.g. due to soft faults) appear as corrupted data, which is either rejected if that causes a task to fail, or is seamlessly filtered out during the regression stage through a suitable noise model. Three different types of faults are modeled: hard faults modeling nodes (or clients) crashing; soft faults occurring during the communication of the tasks between server and clients; and soft faults occurring during task execution. We demonstrate the resiliency of the approach for a 2D elliptic PDE, and explore the effect of the faults at various failure rates.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Algorithm sensitivity analysis and parameter tuning for tissue image segmentation pipelines

Motivation: Sensitivity analysis and parameter tuning are important processes in large-scale image analysis. They are very costly because the image analysis workflows are required to be executed several times to systematically correlate output variations with parameter changes or to tune parameters. An integrated solution with minimum user interaction that uses effective methodologies and high performance computing is required to scale these studies to large imaging datasets and expensive analysis workflows. Results: The experiments with two segmentation workflows show that the proposed approach can (i) quickly identify and prune parameters that are non-influential; (ii) search a small fraction (about 100 points) of the parameter search space with billions to trillions of points and improve the quality of segmentation results (Dice and Jaccard metrics) by as much as 1.42 x compared to the results from the default parameters; (iii) attain good scalability on a high performance cluster with several effective optimizations. Conclusions: Our work demonstrates the feasibility of performing sensitivity analyses, parameter studies and auto-tuning with large datasets. The proposed framework can enable the quantification of error estimations and output variations in image segmentation pipelines.

Biochemistry & Molecular Biology↗

DOE OSTI2018

A Rainbow Coverage Path Planning for a Patrolling Mobile Robot With Circular Sensing Range

In this paper, we proposed a coverage path (CP) planning approach for a mobile robot moving in a dynamically changing environment. Our family of algorithms begins by finding the first path around the center of interior obstacles/disks of a given target region (TR) through the Graham scan algorithm. The first path is then used as a foundational path to generate a collision free, first order differentiable, and observable CP through a complete set of input and output transformation algorithms which together form the rainbow CP planning approach. The last algorithm of the rainbow CP planning approach finds a sufficient number of observation points on the CP needed to observe the TR. The novelty of our rainbow CP planning approach is that it partitions the TR into different shapes with different properties needed to obtain complete coverage while achieving first order path differentiability. The main technical contributions of the proposed approach is to provide a holistic solution that segments any TR, uses triangulation to determine the line of sights and observation points, and computes the collision-free CP within a quadratic runtime. The proposed method can be readily generalized to address problems of higher dimensions, and it is scalable with respect to the size of TR and the number of robots. Computer simulations are used to illustrate the effectiveness and correctness of the proposed approach.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Entity—Hardware-agnostic Particle-in-cell Code for Plasma Astrophysics. I. Curvilinear Special Relativistic Module

Entity is a new-generation, fully open-source particle-in-cell (PIC) code developed to overcome key limitations in astrophysical plasma modeling, particularly the extreme separation of scales and the performance challenges associated with evolving, GPU-centric computing infrastructures. It achieves hardware-agnostic performance portability across various GPU and CPU architectures using the Kokkos library. Crucially, Entity maintains a high standard for usability, clarity, and customizability, offering a robust and easy-to-use framework for developing new algorithms and grid geometries, which allows extensive control without requiring edits to the core source code. This paper details the core general-coordinate special relativistic module. Entity is the first PIC code designed to solve the Vlasov–Maxwell system in general coordinates, enabling a coordinate-agnostic framework that provides the foundational structure for straightforward extension to arbitrary coordinate geometries. The core methodology achieves numerical stability by solving particle equations of motion in the global orthonormal Cartesian basis, despite using generalized coordinates like Cartesian, axisymmetric spherical, and quasi-spherical grids. Charge conservation is ensured via a specialized current deposition technique using conformal currents. The code exhibits robust scalability and performance portability on major GPU platforms (AMD MI250X, NVIDIA A100, and Intel Max Series), with the 3D particle pusher and the current deposition operating efficiently at about 2 ns per particle per time step. Functionality is validated through a comprehensive suite of standard Cartesian plasma tests and the accurate modeling of relativistic magnetospheres in curvilinear axisymmetric geometries.

Hakobyan, Hayk [Flatiron Institute, New York, NY (↗

DOE OSTI2021

Trust: Triangle Counting Reloaded on GPUs

Triangle counting is a building block for a wide range of graph applications. Here, traditional wisdom suggests that i) hashing is not suitable for triangle counting, ii) edge-centric triangle counting beats vertex-centric design, and iii) communication-free and workload balanced graph partitioning is a grand challenge for triangle counting. On the contrary, we advocate that i) hashing can help the key operations for scalable triangle counting on Graphics Processing Units (GPUs), i.e., list intersection and graph partitioning, ii) vertex-centric option reduces both hash table construction cost and memory consumption, which is limited on GPUs. In addition, iii) we exploit graph and workload collaborative, and hash-based 2D partitioning to scale vertex-centric triangle counting over 1,000 GPUs with sustained scalability. In this work, we present TRUST, which performs triangle counting with the hash operation and vertex-centric paradigm. To the best of our knowledge, TRUST is the first work that achieves over one trillion Traversed Edges Per Second (TEPS) rate for triangle counting.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2019

Forming Aggregations using Virtual Sharding: Lessons Learned from Simple Scalable Storage (S3)

Data aggregation is the ability to combine separate datasets to form a single new logical dataset provides users with a powerful abstraction. The advantage of an aggregate dataset is that the users are freed from having to understand, and incorporate into their workflow, knowledge about the (ad hoc) organization of the constituent datasets. However, aggregating large numbers of files can be computationally complex with data server systems performing many repetitive operations. As part of the authors work on subsetting data stored on Amazon Web Service (AWS) Simple Storage Service (S3), we developed technology to read portions of otherwise monolithic data files. This enables the formation of virtual shards for user in subsetting data stored in HDF5 (hierarchical data format, version 5) files. This same tool can be used to form aggregations that combine data stored in many HDF5 files when those files are stored on S3. The nature of the virtual sharding and the algorithm that exploits it for subsetting is such that it can also be used for aggregation with the need for many of the repetitive operations required by the per file aggregation techniques. We will present timing information that demonstrates the flexibility of this approach. However, the lessons learned is that while this is a useful result in and of itself, these very same techniques can be applied in other contexts where data are stored in services and on media other than S3. For example, this same technique can be applied to data stored on spinning disk. Pushing the envelope for S3 forced a reexamination of our data access techniques which lead to unexpected positive benefits.

Gallagher, James↗

DOE OSTI2022

Noisy-Intermediate-Scale Quantum Electromagnetic Transients Program

Quantum-empowered electromagnetic transients program (QEMTP) is a promising paradigm for tackling EMTP's computational burdens. Nevertheless, no existing studies truly achieve a practical and scalable QEMTP operable on today's noisy-intermediate-scale quantum (NISQ) computers. The strong reliance on noise-free and fault-tolerant quantum devices--which appears to be decades away--hinder practical applications of current QEMTP methods. Here, we devise a NISQ-QEMTP methodology which for the first time transitions the QEMTP operations from ideal, noise-free quantum simulators to real, noisy quantum computers. The main contributions lie in: (1) a shallow-depth QEMTP quantum circuit for mitigating noises on NISQ quantum devices; (2) practical QEMTP linear solvers incorporating executable quantum state preparation and measurements for nodal voltage computations; (3) a noise-resilient QEMTP algorithm leveraging quantum resources logarithmically scaled with power system dimension; (4) a quantum shifted frequency analysis (QSFA) for accelerating QEMTP by exploiting dynamic phasor simulations with larger time steps; (5) a systematical analysis on QEMTPs performance under various noisy quantum environments. Extensive experiments systematically verify the accuracy, efficacy, universality and noise-resilience of QEMTP on both noise-free simulators and IBM real quantum computers.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Epistemology of voltage control in DER-rich power system

Despite the recent development of several scalable, robust, and resilient control approaches with superior convergence properties considering an increasing penetration of distributed energy resources (DERs), cognitive oversights often simplify several aspects of the cyber–physical power system in the controller development. Here, following the identification of the limitations of classical controller definitions, we justify alternative definitions of voltage control approaches classifiers considering three inter-disciplinary domains: (i) power system, (ii) optimization and decision-making, and (iii) networking and cyber-security, to develop a taxonomy for helping in real-world comparative performance analysis and deployability of these controllers. We observe that classical and introduced domain-based definitions together can better classify the control algorithms.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Learning Optimal Aerodynamic Designs

This project created a framework for efficient, accurate, and scalable deep neural network representations of design optimization problem solutions. The inputs to these DNN representations are the vector of design requirement parameters, the outputs are the optimal design variables, and the goal is to learn the map from inputs to outputs (i.e., inverse design). The team addressed the problem of the optimal shape design of aerodynamic lifting surfaces—in particular aircraft wings—using a Reynolds-Average Navier Stokes model to govern the CFD-based aerodynamic shape optimization. The inverse design map for such problems is very complex and high-dimensional, involving inputs and outputs on the order of 1000s. To approximate this inverse design map, the team developed algorithms to construct parsimonious DNN architectures, which automatically identify low-dimensional manifolds in which design requirements affect optimal shape parameters, and trained these architectures with multifidelity optimization methods. The resulting methodology accurately and automatically designs optimal aerodynamic lifting surfaces with very high accuracy (99%) at interactive speeds, of the order of milliseconds, resulting in factors of one million or more speedup relative to CFD-based design optimization.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2010

Reconfigurable Hardware for Compressing Hyperspectral Image Data

High-speed, low-power, reconfigurable electronic hardware has been developed to implement ICER-3D, an algorithm for compressing hyperspectral-image data. The algorithm and parts thereof have been the topics of several NASA Tech Briefs articles, including Context Modeler for Wavelet Compression of Hyperspectral Images (NPO-43239) and ICER-3D Hyperspectral Image Compression Software (NPO-43238), which appear elsewhere in this issue of NASA Tech Briefs. As described in more detail in those articles, the algorithm includes three main subalgorithms: one for computing wavelet transforms, one for context modeling, and one for entropy encoding. For the purpose of designing the hardware, these subalgorithms are treated as modules to be implemented efficiently in field-programmable gate arrays (FPGAs). The design takes advantage of industry- standard, commercially available FPGAs. The implementation targets the Xilinx Virtex II pro architecture, which has embedded PowerPC processor cores with flexible on-chip bus architecture. It incorporates an efficient parallel and pipelined architecture to compress the three-dimensional image data. The design provides for internal buffering to minimize intensive input/output operations while making efficient use of offchip memory. The design is scalable in that the subalgorithms are implemented as independent hardware modules that can be combined in parallel to increase throughput. The on-chip processor manages the overall operation of the compression system, including execution of the top-level control functions as well as scheduling, initiating, and monitoring processes. The design prototype has been demonstrated to be capable of compressing hyperspectral data at a rate of 4.5 megasamples per second at a conservative clock frequency of 50 MHz, with a potential for substantially greater throughput at a higher clock frequency. The power consumption of the prototype is less than 6.5 W. The reconfigurability (by means of reprogramming) of the FPGAs makes it possible to effectively alter the design to some extent to satisfy different requirements without adding hardware. The implementation could be easily propagated to future FPGA generations and/or to custom application-specific integrated circuits.

Aranki, Nazeeh↗

NASA NTRS2013

Scheduling Operations for Massive Heterogeneous Clusters

High-performance computing (HPC) programming has become increasingly difficult with the advent of hybrid supercomputers consisting of multicore CPUs and accelerator boards such as the GPU. Manual tuning of software to achieve high performance on this type of machine has been performed by programmers. This is needlessly difficult and prone to being invalidated by new hardware, new software, or changes in the underlying code. A system was developed for task-based representation of programs, which when coupled with a scheduler and runtime system, allows for many benefits, including higher performance and utilization of computational resources, easier programming and porting, and adaptations of code during runtime. The system consists of a method of representing computer algorithms as a series of data-dependent tasks. The series forms a graph, which can be scheduled for execution on many nodes of a supercomputer efficiently by a computer algorithm. The schedule is executed by a dispatch component, which is tailored to understand all of the hardware types that may be available within the system. The scheduler is informed by a cluster mapping tool, which generates a topology of available resources and their strengths and communication costs. Software is decoupled from its hardware, which aids in porting to future architectures. A computer algorithm schedules all operations, which for systems of high complexity (i.e., most NASA codes), cannot be performed optimally by a human. The system aids in reducing repetitive code, such as communication code, and aids in the reduction of redundant code across projects. It adds new features to code automatically, such as recovering from a lost node or the ability to modify the code while running. In this project, the innovators at the time of this reporting intend to develop two distinct technologies that build upon each other and both of which serve as building blocks for more efficient HPC usage. First is the scheduling and dynamic execution framework, and the second is scalable linear algebra libraries that are built directly on the former.

Humphrey, John↗

NASA NTRSDate not supplied

International Coordination and Cooperation on LunaNet Spectrum

LunaNet is planned to be the network of networks operated by a set of cooperating organizations to provide interoperable Communications, networking, Position, Navigation, and Timing (CPNT) services to users on and around the Moon based on a framework of mutually agreed-upon standards, protocols, frequency bands and interface requirements. LunaNet follows a service-oriented architecture that is agnostic about the types of organizations that provide services, e.g., government, industry, or academia. LunaNet is open, scalable, resilient, secure, and extensible. To achieve these goals, LunaNet Service Providers (LNSP) must coordinate with each other to define and develop the architecture, to plan initial and evolved capabilities, and to operate their networks. One of the central LunaNet tenets is the use of shared spectrum. For example, the Lunar Augmented Navigation Service (LANS) acts like a Global Navigation Satellite System (GNSS) such as the US Global Positioning System (GPS) or European Galileo but the LNSPs’ contributions to LunaNet must use the same frequency band (2483.5 MHz-2500.0 MHz) and transmit the same waveform synchronized by highly accurate clocks so that Users ‘see’ one virtual network and use the same multilateration algorithm to determine their positions. This necessitates a high degree of spectrum coordination. NASA’s Lunar and Human Spaceflight Spectrum Management Team has been actively supporting development of the LunaNet Interoperability Specification (LNIS), soliciting inputs from spectrum policy and planning experts across NASA, ESA and JAXA. Cislunar spectrum use considerations have been studied and adjudicated within the Space Frequency Coordination Group (SFCG) and inform the ongoing discussion of a lunar communication and navigation architecture within the existing radio regulatory framework of the International Telecommunication Union, leading to the 2027 World Radiocommunication Conference (WRC-27). The frequency plan contained in the publicly released draft of the LunaNet Interoperability Specification reflects the initial phase of exploration (roughly to 2030) defining an optimal set of radio frequencies in appropriately allocated services consistent with WRC-23 decisions for use by known or planned CPNT applications, while striving to maximize coexistence and compatibility amongst cislunar systems and other systems within the near-Earth regime (< 2 million km from Earth). Important considerations include: protection of extremely sensitive receive-only radio astronomy systems on the lunar far side, known as the Shielded Zone of the Moon (SZM); compatibility between Direct with Earth (DWE) communications links and links needed to support relay satellites in lunar orbit with their customer systems on orbit or on the lunar surface; compatibility between multiple lunar surface communications systems and capabilities over varied and challenging terrain and distances; as well as ensuring compatibility and interoperability between navigation systems which either leverage Earth-based or in-situ lunar systems. In addition, the lunar CPNT architecture is envisioned to be the basis – with adjustments – of the future Mars CPNT architecture as we expand into the solar system using Interplanetary Networking (IPN). The second phase of lunar spectrum definition will address planned international capabilities for the next decade that will require action at WRC-27 and beyond. This paper will discuss each of these considerations in more depth and how the current LunaNet frequency plan addresses them.

LunaNet↗

DOE OSTI2018

Simplifying Energy Efficiency for Homeowners - Results from a California Utility's Pilot Study of a Scalable Solution to Assess Retrofit and Solar Opportunities: Preprint

For homeowners, typical choices to reduce net energy use in their homes include: upgrading their home with a tailored efficiency retrofit or investing in roof-top solar. Determining the unique combination of these options that will provide the most cost-effective solution requires analysis for each home. This analysis needs to be presented in a simple and actionable way to homeowners. The solution should also be easily scalable to thousands of homes to enable utilities to better target efficiency program options and support solar installations. Sacramento Municipal Utility District (SMUD) piloted a new tool developed by Vistar Energy called XeroHome that automates the assessment of energy efficiency and solar opportunities in existing homes. XeroHome demonstrates an approach to using building energy modeling, energy use data analytics and homeowner engagement to identify and target energy efficiency opportunities at scale. XeroHome uses automation to build an EnergyPlus model for each home, based on information from public data sources such as property assessment records, building permit records, aerial/satellite photography, and template models derived from ResStock. Utilizing cloud computing, machine learning and optimization algorithms, energy upgrade packages are ranked by cost-effectiveness for each home and presented over an interactive web-portal. This paper discusses SMUD's pilot project using XeroHome to conduct analysis for 1,500 existing homes in Sacramento, CA. Development of XeroHome's advanced analytics capabilities are discussed in the context of applicability for utility programs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2018

Simplifying Energy Efficiency for Homeowners - Results from a California Utility's Pilot Study of a Scalable Solution to Assess Retrofit and Solar Opportunities

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRSDate not supplied

Verification and Validation of Safety-Critical Aircraft Systems Operating under Off-Nominal, Contingency, and Emergency Conditions

Verification and validation (V&V) of safety-critical technologies developed for loss of control (LOC) prevention and recovery and other aviation safety concerns pose significant challenges. Aircraft LOC can result from a wide spectrum of hazards, often occurring in combination, which cannot be fully replicated during evaluation. Technologies developed for LOC prevention and recovery must therefore be effective under a wide variety of hazardous and uncertain conditions, and the verification and validation of these technologies must provide some measure of assurance that the new vehicle safety technologies do no harm (i.e., that they themselves do not introduce new safety risks). V&V technologies must also enable the identification of system limitations and constraints, as well as enable the identification of safe and unsafe operating conditions (and their boundaries). Additionally, the V&V of complex, increasingly autonomous systems is a fundamental concern. Scalable, reproducible and cost-effective techniques for the assurance of safety critical systems during their design and operation is a key barrier to fielding new systems or updating current systems. Moreover, these techniques need to provide artifacts that enable a comprehensive evidence-based approach to certification. This briefing summarizes research performed under NASA’s Aviation Safety Program and follow-on research for the V&V of safety-critical aircraft system technologies developed for LOC prevention and recovery and increasingly autonomous systems, and for a broad assurance capability in both current and emerging aviation applications. Note that, in this briefing, the term “validation” refers to a confirmation that the system implementation (e.g., algorithms etc.) is performing the intended function(s), as well as an affirmation of effectiveness in these functions. “Verification” refers to a confirmation that the system implementation in the software and hardware meets its (hopefully validated) specifications (e.g., correctly executes algorithms as designed).

Validation↗

DOE OSTI2019

Multi-architecture Monte-Carlo (MC) simulation of soft coarse-grained polymeric materials: SOft coarse grained Monte-Carlo Acceleration (SOMA)

Not Available

Schneider, L. (ORCID:0000000239108217)↗

DOE OSTI2020

Development of a CPU/GPU portable software library for Lagrangian–Eulerian simulations of liquid sprays

The Lagrangian–Eulerian method is widely used for simulations of fuel sprays in turbulent combustion because of the advantage of treating the spray droplets as discrete points. One challenge of the Lagrangian–Eulerian method is the intense computational requirement when tracking the large number of Lagrangian particles needed for high fidelity. We have developed a performance-portable library, Grit, to track the Lagrangian particles in parallel on central processing unit (CPU) and graphics processing unit (GPU) accelerated high performance computing (HPC) architectures. Grit is a C++ library which employs Message Passing Interface (MPI) for distributed memory parallelism and Kokkos programming model for on-node shared memory parallelism with performance portability across different architectures of GPUs and multi-core/manycore CPUs. The parallel algorithms, key parallel kernels, and their performances on the pre-exascale supercomputer, Summit, are presented. Grit is coupled with a direct numerical simulation (DNS) solver, S3D, for multiphase simulations. A conservative formulation has been developed and implemented in Grit for phase coupling with thermodynamic consistency. The formulation separates the conservation of mass, momentum and energy from the physical models to prevent accidental violation of conservation laws due to inconsistent models. The formulation also enforces consistent definitions of enthalpies of the fuel for both phases and the latent heat of evaporation. Finally, simulations of turbulent particle-laden flow and the evaporation of dilute turbulent spray jet are performed to verify the software implementation, and to demonstrate the scalability of Grit for large-scale multiphase simulations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Energy-Screened Many-Body Expansion for Protein–Ligand Interactions: Examining Convergence for Metalloenzymes Through Seven–Body Interactions

Fragment-based quantum chemistry is a powerful strategy for calculating protein−ligand interaction energies using quantum chemistry methods. Rigorous convergence often requires hundreds of atoms in the protein binding-site model, especially if that model is constructed using distance-based criteria to select amino acid residues, while three- and four-body calculations exhibit instability related to combinatorial proliferation in the number of subsystem calculations. Here, we report an energy-based screening protocol for the many-body expansion applied to protein−ligand interactions, implemented in the open-source FRAGME∩T code. Using a combination of aggressive screening based on semiempirical quantum chemistry, with an improved graph-theoretical algorithm to eliminate unimportant subsystems, we are able to perform n-body calculations up to n = 7 using density functional theory in triple-ζ basis sets. Distance cutoffs further reduce the cost without compromising accuracy. Rapid and stable convergence of the many-body expansion is obtained by n = 4, for a pair of metalloenzymes in which a divalent ion coordinates directly to the ligand. As compared to previous results that relied solely on distance cutoffs, oscillations in the n-body corrections are reduced or eliminated, although residual errors remain in one case. This work demonstrates that benchmark-quality protein−ligand interaction energies can be systematically converged using a method with excellent parallel efficiency and scalability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗