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At least 1,135 records · Page 63

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DOE OSTI2024

Enabling Low-Overhead HT-HPC Workflows at Extreme Scale using GNU Parallel

GNU Parallel is a versatile and powerful tool for process parallelization widely used in scientific computing. This paper demonstrates its effective application in high-performance computing (HPC) environments, particularly focusing on its scalability and efficiency in executing large-scale high-throughput high-performance computing (HT-HPC) workflows. Through real-world examples, we highlight GNU Parallel’s performance across various HPC workloads, including GPU computing, container-based workloads, and node-local NVMe storage. Our results on two leading supercomputers, OLCF’s Frontier and NERSC’s Perlmutter, showcase GNU Parallel’s rapid process dispatching ability and its capacity to maintain low overhead even at extreme scales. We explore GNU Parallel’s application in massive parallel file transfers using a scheduled Data Transfer Node (DTN) cluster, emphasizing its broad utility in diverse scientific workflows. Beyond its direct application as a viable workflow manager, GNU Parallel can be employed in conjunction with other workflow systems as a "last-mile" parallelizing driver and as a quick prototyping tool to design and extract parallel profiles from application executions. We then argue that the potential for GNU Parallel to transform workflow management at extreme scales is substantial, paving the way for more efficient and effective scientific discoveries.

Maheshwari, Ketan↗

DOE OSTI2022

Emulation Framework for Distributed Large-Scale Systems Integration

Recent trends in systems engineering include integration of very large-scale systems, which entails significant challenges when they are geographically dispersed. In these scenarios, intelligent integration of distributed large-scale systems requires significant coordination among hardware elements as well as all software components. The approach of integrated systems (both computing platform and experimental equipment) for end-to-end orchestration is called federation. Virtual frameworks can aid in the testing, assessment, and implementation of a functional system of interconnected resources. We present an emulation framework that replicates the software environments of multi-site federations of computing systems and instruments. Our emulation framework allows systems engineers to reduce developmentcost and avoid disruptions to production infrastructure. Our framework was effectively used to develop and test software modules for various tasks including container orchestration and instrument access. For performance assessment, however, the emulated framework is severely limited in providing accurate network and IO measurements at 10 Gbps and higher data rates. The data transfer performance profiles estimated using these emulated measurements are usually inaccurate for high bandwidth and high latency connections, since emulation does not accurately reflect the critical network transport dynamics.We utilize measurements from a physical testbed with hardware network emulators to obtain data transfer profiles that closely match the expected profiles for the emulated federations. We show the effectiveness of our approach by an illustrative example of integrated (federated) multi-site ultra large-scale systems that are connected via high speed wide area networks.

Imam, Neena↗

DOE OSTI2020

The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism

Here, we present scalable hybrid-parallel algorithms for training large-scale 3D convolutional neural networks. Deep learning-based emerging scientific workflows often require model training with large, high-dimensional samples, which can make training much more costly and even infeasible due to excessive memory usage. We solve these challenges by extensively applying hybrid parallelism throughout the end-to-end training pipeline, including both computations and I/O. Our hybrid-parallel algorithm extends the standard data parallelism with spatial parallelism, which partitions a single sample in the spatial domain, realizing strong scaling beyond the mini-batch dimension with a larger aggregated memory capacity. We evaluate our proposed training algorithms with two challenging 3D CNNs, CosmoFlow and 3D U-Net. Our comprehensive performance studies show that good weak and strong scaling can be achieved for both networks using up to 2K GPUs. More importantly, we enable training of CosmoFlow with much larger samples than previously possible, realizing an order-of-magnitude improvement in prediction accuracy.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Large Scale Bilevel Optimization for N-K SCOPF Using Adversarial Robustness

Ensuring a secure dispatch against multiple simultaneous outages has long been desired to maintain grid security in the presence of severe events, such as extreme weather phenomena. Traditionally denoted as N-k security constrained optimal power flow (N-k SCOPF), this problem is intractable to solve due to its size being combinatorial in the number of simultaneous outages and due to the non-convex nature of the AC network constraints. This hinders the use of N-k SCOPF for operating realistic-scale systems. In this paper, we introduce a methodology to scalably solve an AC-feasible dispatch that improves security over k simultaneous outages. Our methodology poses N-k SCOPF as a bilevel optimization problem and solves it using an adversarial robustness approach. We develop new efficient methods to solve each level of the bilevel optimization by employing knowledge of the physics of the underlying system. This yields significant improvements in speed and convergence that enable us to address the N-k SCOPF problem at scale. We demonstrate the effectiveness of our method by conducting a comprehensive analysis of an N-3 SCOPF for a 500-bus network. Furthermore, we emphasize the ability of our physics-driven techniques to handle larger systems by successfully scaling up to 12,000 buses.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Towards Interactive, Reproducible Analytics at Scale on HPC Systems

The growth in scientific data volumes has resulted in a need to scale up processing and analysis pipelines using High Performance Computing (HPC) systems. These workflows need interactive, reproducible analytics at scale. The Jupyter platform provides core capabilities for interactivity but was not designed for HPC systems. In this paper, we outline our efforts that bring together core technologies based on the Jupyter Platform to create interactive, reproducible analytics at scale on HPC systems. Our work is grounded in a real world science use case-applying geophysical simulations and inversions for imaging the subsurface. Our core platform addresses three key areas of the scientific analysis workflow-reproducibility, scalability, and interactivity. We describe our implemention of a system, using Binder, Science Capsule, and Dask software. We demonstrate the use of this software to run our use case and interactively visualize real-Time streams of HDF5 data.

containers↗

DOE OSTI2022

Tailoring critical Al concentration to form external Al 2 O 3 scale on Ni–Al alloys by computational approach

Abstract Nickel (Ni)‐based superalloys for high‐temperature applications are often designed to form a continuous and slow‐growing oxide scale by adding Al and Cr and other beneficial elements. In the present work, the critical Al concentration in Ni–Al alloys needed to establish an α‐Al 2 O 3 scale in contrast to internal oxide formation is predicted as a function of temperature by means of the CALPHAD approach coupled with models in the literature, which account for the thermodynamics and kinetics of oxidation. The present thermodynamic remodeling of the Ni–O system results in a better agreement with experimental data of oxygen solubility in Ni at high temperatures. The oxygen solubility is combined with kinetic parameters to determine oxygen permeability in Ni, and the critical Al concentration needed to establish an α‐Al 2 O 3 scale at a given exposure temperature. Good agreement is found with available experimental data for both oxygen permeability and critical Al concentration, indicating the capacity of the CALPHAD approach to tailor oxidation resistance for materials of interest using thermodynamic and kinetic knowledge.

Ross, Austin↗

DOE OSTI2021

A Novel Approach for Bounding the Stress Experienced by the Core of Utility-Scale Printed Circuit Heat Exchangers Under Thermohydraulic Loads

Printed circuit heat exchangers (PCHEs) have rapidly gained popularity since being introduced nearly three decades ago, and they are currently widely deployed in the petrochemical and aviation industry. Their compactness, thermohydraulic efficiency, inherent suitability for high temperature/pressure fluids containment, and demonstrated durability are some of the reasons the nuclear industry is seeking to adopt this technology as well. However, the relatively strict nuclear-related regulatory design code, especially when classified as critical to the safety of the reactors, is posing challenges to adopting the technology. From stress analysis point of view, one undesirable feature of PCHEs is their geometrical complexity, which is implied by their multilength-scale features. As a result, a full-scale model of a utility-scale exchanger cannot simply be solved on a computer because meshing such components results in a vast number of degrees-of-freedom. Here, this work seeks to address the challenge of stress analyses to PCHEs by presenting a method to simplify the geometry of PCHE designs. The models proposed by this work can be practically analyzed on a standard computer and provide a path for implementing ASME design rules. The analyses presented herein are divided into five separate investigations. Each is carried out to incrementally simplify the analyzed model by addressing features such as the shapes of the flow passages, the complex distribution of stress in large components, the three-dimensionality of the stress and strain, the thermal stresses caused by thermohydraulic operation observed experimentally and more.

42 ENGINEERING↗

DOE OSTI2020

Accelerating Large-Format Metal Additive Manufacturing: How Controls R&D is Driving Speed, Scale, and Efficiency

This article highlights work at Oak Ridge National Laboratory’s Manufacturing Demonstration Facility to develop closed-loop, feedback control for laser-wire based Directed Energy Deposition, a form of metal Big Area Additive Manufacturing (m-BAAM), a process being developed in partnership with GKN Aerospace specifically for the production of Ti-6Al-4V pre-forms for aerospace components. A large-scale structural demonstrator component is presented as a case-study in which not just control, but the entire 3D printing workflow for m-BAAM is discussed in detail, including design principles for large-format metal AM, toolpath generation, parameter development, process control, and system operation, as well as post-print net-shape geometric analysis and finish machining. In terms of control, a multi-sensor approach has been utilized to measure both layer height and melt pool size, and multiple modes of closed-loop control have been developed to manipulate process parameters (laser power, print speed, deposition rate) to control these variables. Layer height control and melt pool size control have yielded excellent local (intralayer) and global (component-level) geometry control, and the impact of melt pool size control in particular on thermal gradients and material properties is the subject of continuing research. Further, these modes of control have allowed the process to advance to higher deposition rates (exceeding 7.5 lb/hr), larger parts (1-meter scale), shorter build times, and higher overall efficiency. The control modes are examined individually, highlighting their development, demonstration, and lessons learned, and it is shown how they operate concurrently to enable the printing of a large-scale, near net shape Ti-6Al-4V component.

Gibson, Brian↗

DOE OSTI2022

Large-scale parametric modeling of spent nuclear fuel dynamics in the 30 cm package drop scenario

Packages used to transport spent nuclear fuel (SNF) are required by the U.S. Code of Federal Regulations 10 CFR 71.71 to demonstrate satisfactory performance during a drop scenario. While the CFR is meant to ensure safe package function, it does not evaluate survival of the SNF within. The U.S. Department of Energy Spent Fuel and Waste Science and Technology program is working on closing the knowledge gap related to the response of SNF to external mechanical loads, including the hypothetical 30 cm package drop scenario in the CFR. In support of this effort, LS-DYNA finite element simulations were developed by Pacific Northwest National Laboratory (PNNL) to model generic drop scenarios at both the package and fuel assembly level. The models were validated against one-third scale package and full scale fuel assembly drop test data and were exercised to predict fuel cladding strains in a narrow range of model configurations. This work describes a large-scale parametric study conducted by PNNL using the previously developed and validated PWR finite element model, with the addition of a new generic BWR assembly model. The motivation for the parametric study was to characterize the broad range of SNF responses in the 30 cm package drop scenario. This was accomplished by varying the drop orientation, fuel assembly type (17x17 PWR and 10x10 BWR), burnup, cladding temperature, spacer grid buckling load, package mass, impact limiter stiffness, and mechanical gap conditions within the basket. A MATLAB framework was developed to automate LS-DYNA model generation and execution on PNNL institutional computing resources. In total, over 2000 simulations were performed. For each simulation, the SNF response was quantified in terms of permanent grid deformation, fuel rod contact pressure, and strains within the fuel rods, guide tubes, and water rods. The results provide valuable insight into the range of responses that could be reasonably expected from SNF in the hypothetical drop scenario, as well as the sensitivity to each input parameter. The results of this parametric study are a key component of the testing and modeling strategy the Spent Fuel and Waste Science and Technology program is using to close the external loads knowledge gap.

Kadooka, Kevin↗

DOE OSTI2024

Exploring Failure of Adhesively-Bonded Joints with Different Void Sizes at the Same Void Volume Fraction through A Micro-scale Numerical Modeling

This paper studied the effects of void sizes on the failure behavior of adhesively-bonded materials under global shear via micro-scale computational modeling. The numerical results indicate that large void sizes with increased distances between them at the same void volume fraction can alleviate the reduced joint strength of a weaker adherend-adhesive interface caused by interfacial voids. However, this is not the case for an adherend-adhesive interface with the interfacial tensile strength being sufficiently higher than the adhesive strength, showing the negligible effect on the joint strength due to various void sizes and the amount of interfacial voids. This preliminary investigation provides insightful information for scaling up the void size in the macro-scale computational modeling of joints, and enhances the understanding of the micro-mechanical adhesion in adherend-adhesive interfaces with different levels of bonding.

Qiao, Yao↗

DOE OSTI2023

A mid-scale plasma-electrode Pockels cell for the FLUX laser

A mid-scale (270 x 270-mm aperture) plasma-electrode Pockels cell (PEPC) has been developed for the pump laser system in FLUX (the fourth-generation laser for ultrabroadband experiments). The FLUX mid-scale PEPC is adapted from the PEPC used in the OMEGA EP beamlines (410 x 410-mm aperture), with certain dimensions scaled to reduce capacitance while maintaining key plasma characteristics. After experimental optimization of operating parameters, particularly the plasma current, <70-ns switching time with excellent (>1000:1) contrast has been demonstrated. Dependence of optical switching performance on operating conditions is explained by a plasma model.

AMICA↗

DOE OSTI2024

High energy density picoliter-scale zinc-air microbatteries for colloidal robotics

The recent interest in microscopic autonomous systems, including microrobots, colloidal state machines, and smart dust, has created a need for microscale energy storage and harvesting. However, macroscopic materials for energy storage have noted incompatibilities with microfabrication techniques, creating substantial challenges to realizing microscale energy systems. Here, we photolithographically patterned a microscale zinc/platinum/SU-8 system to generate the highest energy density microbattery at the picoliter (10 −12 liter) scale. The device scavenges ambient or solution-dissolved oxygen for a zinc oxidation reaction, achieving an energy density ranging from 760 to 1070 watt-hours per liter at scales below 100 micrometers lateral and 2 micrometers thickness in size. The parallel nature of photolithography processes allows 10,000 devices per wafer to be released into solution as colloids with energy stored on board. Within a volume of only 2 picoliters each, these primary microbatteries can deliver open circuit voltages of 1.05 ± 0.12 volts, with total energies ranging from 5.5 ± 0.3 to 7.7 ± 1.0 microjoules and a maximum power near 2.7 nanowatts. We demonstrated that such systems can reliably power a micrometer-sized memristor circuit, providing access to nonvolatile memory. We also cycled power to drive the reversible bending of microscale bimorph actuators at 0.05 hertz for mechanical functions of colloidal robots. Additional capabilities, such as powering two distinct nanosensor types and a clock circuit, were also demonstrated. The high energy density, low volume, and simple configuration promise the mass fabrication and adoption of such picoliter zinc-air batteries for micrometer-scale, colloidal robotics with autonomous functions.

Robotics↗

DOE OSTI2021

Jet energy scale and resolution measured in proton–proton collisions at $\sqrt{s}=13$ TeV with the ATLAS detector

Jet energy scale and resolution measurements with their associated uncertainties are reported for jets using 36–81 fb -1 of proton–proton collision data with a centre-of-mass energy of √s=13 TeV collected by the ATLAS detector at the LHC. Jets are reconstructed using two different input types: topo-clusters formed from energy deposits in calorimeter cells, as well as an algorithmic combination of charged-particle tracks with those topo-clusters, referred to as the ATLAS particle-flow reconstruction method. The anti-k t jet algorithm with radius parameter R=0.4 is the primary jet definition used for both jet types. This result presents new jet energy scale and resolution measurements in the high pile-up conditions of late LHC Run 2 as well as a full calibration of particle-flow jets in ATLAS. Jets are initially calibrated using a sequence of simulation-based corrections. Next, several in situ techniques are employed to correct for differences between data and simulation and to measure the resolution of jets. The systematic uncertainties in the jet energy scale for central jets (|η|<1.2) vary from 1% for a wide range of high-p T jets (250 T <2000 GeV), to 5% at very low p T (20 GeV) and 3.5% at very high p T (>2.5 TeV). The relative jet energy resolution is measured and ranges from (24±1.5)% at 20 GeV to (6±0.5)% at 300 GeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

Beyond $\rho ^{2/3}$ scaling: microscopic origins and multimessengers of high-density nuclear symmetry energy

The nature and Equation of State (EOS) of dense neutron-rich matter are still very poorly known, while they have broad impacts on many interesting issues in both astrophysics and nuclear physics. In particular, the nuclear symmetry energy $E_{\textrm{sym}}(\rho )$ encoding the cost to make nuclear matter more neutron-rich has been the most uncertain component of the EOS of dense neutron-rich nucleonic matter. It significantly affects the radii, tidal deformations, cooling rates, and frequencies of various oscillation modes of isolated neutron stars as well as the strain amplitude and frequencies of gravitational waves from their mergers, besides its many effects on the structures of nuclei as well as the dynamics and observables of their collisions. Siemens (1970s) observed that $E_{\textrm{sym}}(\rho )$ scales as $(\rho /\rho _0)^{2/3}$ near the saturation density $\rho _0$ of nuclear matter, since both the kinetic part and the potential contribution (quadratic in momentum) exhibit this dependence. The scaling holds if: (1) the nucleon isoscalar potential is quadratic in momentum, and (2) the isovector interaction is weakly density-dependent. After examining many empirical evidences and understanding theoretical findings in the literature, we conclude that: (1) Siemens’ $\rho ^{2/3}$ scaling is robust and serves as a valuable benchmark for both nuclear theories and experiments up to $2\rho _0$ but breaks down at higher densities, (2) Experimental and theoretical findings about $E_{\textrm{sym}}(\rho )$ up to $2\rho _0$ are broadly consistent, but uncertainties remain large for its curvature $K_{\textrm{sym}}(\rho )$ and higher-order parameters, (3) Above $2\rho _0$ , uncertainties grow due to poorly constrained spin-isospin-dependent tensor and three-body forces as well as the resulting nucleon short-range correlations. Looking forward, combining signals from both observations of neutron stars and terrestrial heavy-ion reaction experiments is the most promising path to finally constraining the high-density $E_{\textrm{sym}}(\rho )$ and the EOS of supradense neutron-rich matter. Multiple examples of community efforts to further constrain the high-density $E_{\textrm{sym}}(\rho )$ using both real and mocked data of present and future high-precision observations of neutron stars, as well as heavy-ion collisions involving high-energy rare isotopes, are briefly reviewed.

Li, Bao-An [East Texas A & M Univ., Commerce, TX (↗

DOE OSTI2022

Machine Learning Assisted HPC Workload Trace Generation for Leadership Scale Storage Systems

Monitoring and analyzing a wide range of I/O activities in an HPC cluster is important in maintaining mission-critical performance in a large-scale, multi-user, parallel storage system. Center-wide I/O traces can provide high-level information and fine-grained activities per application or per user running in the system. Studying such large-scale traces can provide helpful insights into the system. It can be used to develop predictive methods for making predictive decisions, adjusting scheduling policies, or providing decisions for the design of next-generation systems. However, sharing real-world I/O traces to expedite such research efforts leaves a few concerns; i) the cost of sharing the large traces is expensive due to this large size, and ii) privacy concern is an issue.We address such issues by building an end-to-end machine learn- ing (ML) workflow that can generate I/O traces for large-scale HPC applications. We leverage ML based feature selection and gener- ative models for I/O trace generation. The generative models are trained on I/O traces collected by the darshan I/O characterization tool over a period of one year. We present a two-step generation process consisting of two deep-learning models, called the feature generator and the trace generator. The combination of two-step generative models provides robustness by reducing the bias of the model and accounting for the stochastic nature of the I/O traces across different runs of an application. We evaluate the performance of the generative models and show that the two-step model can generate time-series I/O traces with less than 20% root mean square error.

Paul, Arnab↗

DOE OSTI2025

Scaling Uintah on the Aurora Exascale System up to 122,880 Intel Ponte Vecchio Xe Stacks

The challenge of being able to scale application codes based on the Asynchronous Many-Task (AMT) Uintah framework on the Department of Energy (DOE) Aurora exascale system is addressed in this work by considering a challenging Reverse Monte Carlo Ray Tracing radiation benchmark calculation. This benchmark involves potentially global all-to-all communication and uses adaptive mesh refinement and ray tracing to achieve scalability. This benchmark has been used as part of previous scalability studies on a number of pre-exascale systems and on the DOE Frontier exascale system. This paper describes steps taken to enable this benchmark to run successfully on up to 10,240 nodes and 122,880 Intel® Ponte Vecchio Xe stacks on the DOE Aurora exascale system. This scalability was achieved through a limited number of experiments on Aurora, given machine loads and its uniqueness. These experiments constitute valuable lessons learned to achieve scalability at this level. The resulting scalability runs, while few in number, demonstrate relatively good strong-scaling characteristics. A detailed analysis of these results provides important indications about the path to scalability on Aurora for future work. Overall, these results continue the remarkable ability of this AMT approach to produce scalable solutions for challenging problems at extreme scale on heterogeneous architectures.

Garcia, Marta [Argonne National Laboratory (ANL)]↗

DOE OSTI2025

ORBIT-2: Scaling Exascale Vision Foundation Models for Weather and Climate Downscaling

Sparse observations and coarse-resolution climate models limit effective regional decision-making, underscoring the need for robust downscaling. However, existing AI methods struggle with generalization across variables and geographies and are constrained by the quadratic complexity of Vision Transformer (ViT) self-attention. We introduce ORBIT-2, a scalable foundation model for global, hyper-resolution climate downscaling. ORBIT-2 incorporates two key innovations: (1) Residual Slim ViT (Reslim), a lightweight architecture with residual learning and Bayesian regularization for efficient, robust prediction; and (2) TILES, a tile-wise sequence scaling algorithm that reduces self-attention complexity from quadratic to linear, enabling long-sequence processing and massive parallelism. ORBIT-2 scales to 10 billion parameters across 65,536 GPUs, achieving up to 4.1 ExaFLOPS sustained throughput and 74–98% strong scaling efficiency. It supports downscaling to 0.9 km global resolution and processes sequences up to 4.2 billion tokens. On 7 km resolution benchmarks, ORBIT-2 achieves high accuracy with R2 scores in range of 0.98–0.99 against observation data.

Wang, Xiao [ORNL] (ORCID:0000000165451943)↗

DOE OSTI2025

Data Readiness for Scientific AI at Scale

This paper examines how Data Readiness for AI (DRAI) principles apply to leadership-scale scientific datasets used to train foundation models. We analyze archetypal workflows across four representative domains—climate, nuclear fusion, bio/health, and materials—to identify common preprocessing patterns and domain-specific constraints. We introduce a two-dimensional readiness framework that combines canonical preprocessing patterns with a five-level operational readiness scale, both tailored to high-performance computing (HPC) environments. This framework helps outline key challenges in transforming large-scale scientific data into formats suitable for scalable AI training. Together, these dimensions form a conceptual maturity matrix that characterizes scientific data readiness and guides infrastructure development toward standardized, cross-domain support for scalable and reproducible AI for science.

Brewer, Wes [ORNL] (ORCID:0000000236393956)↗