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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

HPC resources for CMS offline computing: An integration and scalability challenge for the Submission Infrastructure

The computing resource needs of LHC experiments are expected to continue growing significantly during the Run 3 and into the HL-LHC era. The landscape of available resources will also evolve, as High Performance Computing (HPC) and Cloud resources will provide a comparable, or even dominant, fraction of the total compute capacity. The future years present a challenge for the experiments’ resource provisioning models, both in terms of scalability and increasing complexity. The CMS Submission Infrastructure (SI) provisions computing resources for CMS workflows. This infrastructure is built on a set of federated HTCondor pools, currently aggregating 400k CPU cores distributed worldwide and supporting the simultaneous execution of over 200k computing tasks. Incorporating HPC resources into CMS computing represents firstly an integration challenge, as HPC centers are much more diverse compared to Grid sites. Secondly, evolving the present SI, dimensioned to harness the current CMS computing capacity, to reach the resource scales required for the HLLHC phase, while maintaining global flexibility and efficiency, will represent an additional challenge for the SI. To preventively address future potential scalability limits, the SI team regularly runs tests to explore the maximum reach of our infrastructure. In this note, the integration of HPC resources into CMS offline computing is summarized, the potential concerns for the SI derived from the increased scale of operations are described, and the most recent results of scalability test on the CMS SI are reported.

Pérez-Calero Yzquierdo, Antonio↗
DOE OSTI2015

A freestream-preserving fourth-order finite-volume method in mapped coordinates with adaptive-mesh refinement

We present a fourth-order accurate finite-volume method for solving time-dependent hyperbolic systems of conservation laws on mapped grids that are adaptively refined in space and time. Some novel considerations for formulating the semi-discrete system of equations in computational space are combined with detailed mechanisms for accommodating the adapting grids. Furthermore, these considerations ensure that conservation is maintained and that the divergence of a constant vector field is always zero (freestream-preservation property). The solution in time is advanced with a fourth-order Runge-Kutta method. A series of tests verifies that the expected accuracy is achieved in smooth flows and the solution of a Mach reflection problem demonstrates the effectiveness of the algorithm in resolving strong discontinuities.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Two-level Clustering-based Target Detection Through Sensor Deployment and Data Fusion

Target detection is one fundamental problem in many sensor network-based applications, which is typically tackled in two separate stages for sensor deployment and data fusion. We propose an integrated solution, referred to as 2C-SSEM,which combines 2-level clustering-based sensor deployment and source strength estimate map-based data fusion for the detection of a single static or moving target. 2C-SSEM conducts the first level of clustering to determine a sensor deployment scheme and the second level of clustering to divide the deployed sensors into several subsets. For each sensor, the source strength is estimated at each grid point of the entire region based on a signal attenuation model, and for each subset of sensors, the target location is estimated using a distribution map-based statistical analysis method. A final detection decision is made by thresholding the clustering degree of the target location estimates computed by all subsets of sensors. Compared with traditional grid-based target detection methods, 2C-SSEM significantly reduces the computation complexity and improves the detection performance through an integrated optimization strategy. Extensive simulation results show the performance superiority of the proposed solution over several well-known methods for target detection.

Rao, Nageswara↗
DOE OSTI2024

SIERRA Low Mach Module: Fuego Verification Manual (V.5.22)

The SIERRA Low Mach Module: Fuego, henceforth referred to as Fuego, is the key element of the ASC fire environment simulation project. The fire environment simulation project is directed at characterizing both open large-scale pool fires and building enclosure fires. Fuego represents the turbulent, buoyantly-driven incompressible flow, heat transfer, mass transfer, combustion, soot, and absorption coefficient model portion of the simulation software. Sierra/PMR handles the participating-media thermal radiation mechanics. This project is an integral part of the SIERRA multi-mechanics software development project. Fuego depends heavily upon the core architecture developments provided by SIERRA for massively parallel computing, solution adaptivity, and mechanics coupling on unstructured grids.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

US ATLAS and US CMS HPC and Cloud Blueprint

The Large Hadron Collider (LHC) at CERN houses two general purpose detectors - ATLAS and CMS - which conduct physics programs over multi-year runs to generate increasingly precise and extensive datasets. The efforts of the CMS and ATLAS collaborations lead to the discovery of the Higgs boson, a fundamental particle that gives mass to other particles, representing a monumental achievement in the field of particle physics that was recognized with the awarding of the Nobel Prize in Physics in 2013 to François Englert and Peter Higgs. These collaborations continue to analyze data from the LHC and are preparing for the high luminosity data taking phase at the end of the decade. The computing models of these detectors rely on a distributed processing grid hosted by more than 150 associated universities and laboratories worldwide. However, such new data will require a significant expansion of the existing computing infrastructure. To address this, both collaborations have been working for years on integrating High Performance Computers (HPC) and commercial cloud resources into their infrastructure and continue to assess the potential role of such resources in order to cope with the demands of the new high luminosity era. US ATLAS and US CMS computing management have charged the authors to provide a blueprint document looking at current and possibly future use of HPC and Cloud resources, outlining integration models, possibilities, challenges and costs. The document will address key questions such as the optimal use of resources for the experiments and funding agencies, the main obstacles that need to be overcome for resource adoption, and areas that require more attention.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Climate Control Load Reduction Strategies for Electric Drive Vehicles in Cold Weather

When operated, the climate control system is the largest auxiliary load on a vehicle. This load has significant impact on fuel economy for conventional and hybrid vehicles, and it drastically reduces the driving range of all electric vehicles (EVs). Heating is even more detrimental to EV range than cooling because no engine waste heat is available. Reducing the thermal loads on the heating, ventilating, and air conditioning system will extend driving range and increase the market penetration of EVs. Researchers at the National Renewable Energy Laboratory have evaluated strategies for vehicle climate control load reduction with special attention toward grid connected electric vehicles. Outdoor vehicle thermal testing and computational modeling were used to assess potential strategies for improved thermal management and to evaluate the effectiveness of thermal load reduction technologies. A human physiology model was also used to evaluate the impact on occupant thermal comfort. Experimental evaluations of zonal heating strategies demonstrated a 5.5% to 28.5% reduction in cabin heating energy over a 20-minute warm-up. Vehicle simulations over various drive cycles show a 6.9% to 18.7% improvement in EV range over baseline heating using the most promising zonal heating strategy investigated. A national-level analysis was conducted to determine the overall national impact. If all vehicles used the best zonal strategy, the range would be improved by 7.1% over the baseline heating range. This is a 33% reduction in the range penalty for heating.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2018

Calibration of the E3SM Land Model Using Surrogate‐Based Global Optimization

Abstract Calibration of the Energy Exascale Earth System Model (E3SM), land model (ELMv0) is challenging because of its model complexity, strong model nonlinearity, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near‐optimal solution within an affordable time. The goal of this study is to calibrate some of the ELMv0 parameters to improve model projection of carbon fluxes. We propose a computationally efficient global optimization procedure using sparse‐grid based surrogates. We first use advanced sparse grid (SG) interpolation to construct a surrogate system of the ELMv0, and then calibrate the surrogate model in the optimization process. As the surrogate model is a polynomial whose evaluation is fast, it can be efficiently evaluated a sufficiently large number of times in the optimization, which facilitates the global search. We calibrate eight parameters against five years of net ecosystem exchange, total leaf area index, and latent heat flux data from the U.S. Missouri Ozark flux tower. The calibrated model is then used for predicting the three variables in the following 4 years. The results indicate that an accurate surrogate model can be created for the ELMv0 with a relatively small number of SG points, i.e., a few ELMv0 simulations that can be fully parallel. And, the application of the optimized parameters leads to a better model performance and a higher predictive capability than the default parameter values in the ELMv0.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Explosion-induced ignition and combustion of acetylene clouds

We investigate the explosion-induced ignition and combustion of an acetylene cloud in a rectangular chamber (10 cm × 10 cm × 39 cm). In the experiments, a 0.2-g PETN charge was located at $x = 9.7\,\hbox {cm}$ and a soap bubble (d=5cm) filled with pure acetylene was located at $x = 27\,\hbox {cm}$ as measured relative to the end wall. Detonation of the charge created a blast wave that crushed the soap bubble—inducing mixing with the air. After 0.55 ms, the mixture ignited, forming at turbulent combustion cloud. The flow was modeled using the compressible Navier–Stokes equations assuming unity Lewis number. Arrhenius-based kinetics were used to model ignition. Adaptive mesh refinement was used to capture turbulent mixing on the grid (the MILES approach of J. Boris). Computed pressures were found to be in agreement with measured pressure histories. Finite-rate kinetics were required to capture the ignition processes over the duration of the experiment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Magnetic Susceptibility and Neutron Scattering of Graphene in Antiferromagnetic State: a Tight-Binding Approach

We address here a tight-binding model study of the frequency-dependent antiferromagnetic spin susceptibility for the graphene systems. The Hamiltonian consists of electron hopping up to the third-nearest-neighbors, substrate and impurity effects in presence of electron-electron interactions at A and B sub-lattices. To calculate susceptibility, we evaluate the two-particle electron Green’s function by using Zubarev’s Green’s function technique. The frequency-dependent antiferromagnetic susceptibility of the system is computed numerically by taking 1000 X 1000 grid points of the electron momentum. The susceptibility displays a sharp peak at the neutron momentum transfer energy at low energies and another higher-energy peak associated with the substrate-induced gap. The evolution of these two peaks are investigated by varying neutron wave vector, Coulomb correlation energy, substrate-induced gap, electron hopping integrals and A- and B-site electron-doping concentrations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

FlowPM: Distributed TensorFlow implementation of the FastPM cosmological N-body solver

Here, we present FlowPM, a Particle-Mesh (PM) cosmological N-body code implemented in Mesh-TensorFlow for GPU-accelerated, distributed, and differentiable simulations. We implement and validate the accuracy of a novel multi-grid scheme based on multiresolution pyramids to compute large-scale forces efficiently on distributed platforms. We explore the scaling of the simulation on large-scale supercomputers and compare it with corresponding Python based PM code, finding on an average 10x speed-up in terms of wallclock time. We also demonstrate how this novel tool can be used for efficiently solving large scale cosmological inference problems, in particular reconstruction of cosmological fields in a forward model Bayesian framework with hybrid PM and neural network forward model. We provide skeleton code for these examples and the entire code is publicly available at https://github.com/modichirag/flowpm[Formula presented].

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Global field reconstruction from sparse sensors with Veronoi tessellation-assisted deep learning

Achieving accurate and robust global situational awareness of a complex time-evolving field from a limited number of sensors has been a longstanding challenge. This reconstruction problem is especially difficult when sensors are sparsely positioned in a seemingly random or unorganized manner, which is often encountered in a range of scientific and engineering problems. Moreover, these sensors can be in motion and can become online or offline over time. The key leverage in addressing this scientific issue is the wealth of data accumulated from the sensors. As a solution to this problem, we propose a data-driven spatial field recovery technique founded on a structured grid-based deep-learning approach for arbitrary positioned sensors of any numbers. It should be noted that the naïve use of machine learning becomes prohibitively expensive for global field reconstruction and is furthermore not adaptable to an arbitrary number of sensors. In the present work, we consider the use of Voronoi tessellation to obtain a structured-grid representation from sensor locations enabling the computationally tractable use of convolutional neural networks. One of the central features of the present method is its compatibility with deep-learning based super-resolution reconstruction techniques for structured sensor data that are established for image processing. The proposed reconstruction technique is demonstrated for unsteady wake flow, geophysical data, and three-dimensional turbulence. The current framework is able to handle an arbitrary number of moving sensors, and thereby overcomes a major limitation with existing reconstruction methods. The presented technique opens a new pathway towards the practical use of neural networks for real-time global field estimation.

Fukami, Kai↗
DOE OSTI2017

CMS readiness for multi-core workload scheduling

In the present run of the LHC, CMS data reconstruction and simulation algorithms benefit greatly from being executed as multiple threads running on several processor cores. The complexity of the Run 2 events requires parallelization of the code to reduce the memory-per- core footprint constraining serial execution programs, thus optimizing the exploitation of present multi-core processor architectures. The allocation of computing resources for multi-core tasks, however, becomes a complex problem in itself. The CMS workload submission infrastructure employs multi-slot partitionable pilots, built on HTCondor and GlideinWMS native features, to enable scheduling of single and multi-core jobs simultaneously. This provides a solution for the scheduling problem in a uniform way across grid sites running a diversity of gateways to compute resources and batch system technologies. This paper presents this strategy and the tools on which it has been implemented. The experience of managing multi-core resources at the Tier-0 and Tier-1 sites during 2015, along with the deployment phase to Tier-2 sites during early 2016 is reported. The process of performance monitoring and optimization to achieve efficient and flexible use of the resources is also described.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Gamma-Ray Point-Source Localization and Sparse Image Reconstruction using Poisson Likelihood

Gamma-ray imaging trys to reconstruct the spatial and intensity distribution of gamma-emitting radionuclides from a set of measurements. Wholly, this problem is solved by discretizing the spatial dimensions and employing the maximum likelihood expectation maximization (ML-EM) algorithm, with or without some form of regularization. While the generality of this formulation enables use in a wide variety of scenarios, it is susceptible to overfitting, limited by the discretization of spatial coordinates, and can be computationally expensive. We present a novel approach to 3D gamma-ray image reconstruction for scenarios where sparsity may be assumed, for example radiological source search. In this work we first formulate a point-source localization (PSL) approach as an optimization problem where both position and source intensity are continuous variables. We then extend and generalize this formulation to an iterative algorithm called additive point-source localization (APSL) for sparse parametric image reconstruction. A set of simulated source search scenarios using a single non-directional detector are considered, finding improved image accuracy and computational efficiency with APSL over traditional grid-based methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

A Pipeline for Large Data Processing Using Regular Sampling for Unstructured Grids

Large simulation data requires a lot of time and computational resources to compute, store, analyze, visualize, and run user studies. Today, the largest cost of a supercomputer is not hardware but maintenance, in particular energy consumption. Our goal is to balance energy consumption and cognitive value of visualizations of resulting data. This requires us to go through the entire processing pipeline, from simulation to user studies. To reduce the amount of resources, data can be sampled or compressed. While this adds more computation time, the computational overhead is negligible compared to the simulation time. We built a processing pipeline at the example of regular sampling. The reasons for this choice are two-fold: using a simple example reduces unnecessary complexity as we know what to expect from the results. Furthermore, it provides a good baseline for future, more elaborate sampling methods. We measured time and energy for each test we did, and we conducted user studies in Amazon Mechanical Turk (AMT) for a range of different results we produced through sampling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Powered By ERAD [Slides]

Energy Resilience Analysis for Distribution Power System (ERAD) is a free, open-source Python toolkit for estimating the energy and service impacts of hazards like earthquakes and flooding. It uses a graph-based approach to capture high resolution connectivity among the grid, critical services, and customers and rapidly compute household level metrics and aggregated statistics across large distribution systems. It uses asset fragility curves that relate hazard severity to survival probability for power system equipment including cables, transformers, substations, etc. The tool is designed to be modular and extensible, allowing it to interface with third-party hazard simulators and integrate into broader resilience analysis workflows. ERAD enables researchers, students, communities, distribution utilities, and other stakeholders to understand hazard impacts and evaluate the effectiveness of different programs to improve energy resilience. The webinar was hosted by NLR researcher Aadil Latif.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Parameter distribution mapping in a gas turbine engine

A profile map of parameter values in a region is determined based on average values along linear paths through the region. In one example, a temperature map of a region of a gas turbine engine is created based on time-of-flight measurements from acoustic transceiver pairs arranged circumferentially around the region. A speed of sound for each transceiver pair is determined based on the time-of-flight measurements. An average temperature along each path is estimated from the time of flight, and the profile map is computed from the average temperatures. The profile map may be computed using a polynomial approximation technique, a grid optimization technique or a basis function technique.

42 ENGINEERING↗
DOE OSTI2021

Physics-Based Feature Extraction from Bulk Time-Series PMU Datasets for Event Detection

In this work, two physics-based feature extraction techniques are developed for bulk time-series phasor measurement unit (PMU) datasets collected from the field to train the machine learning model for anomaly detection. Two approaches have been developed to extract useful features for different types of events. An admittance-based feature extraction technique is developed to detect events that involve line outages and system topology variations. The developed algorithm extracts the system equivalent admittance variation. Additionally, Fielder’s Theory is utilized to further reduce the potential computation burden by sectionalizing large-scale grids and datasets into smaller areas. Second, an oscillation-based feature extraction technique is developed to detect low-frequency oscillations in power grids. The dominant oscillation modes in the grids are extracted using energy-sorted Prony analysis. The extracted dominant oscillation modes by the developed work exhibit a high fitting resolution. Finally, the developed techniques have been validated using large-scale and real-world datasets.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Energy I-Corps Annual Report 2021

The U.S. Department of Energy invests millions of dollars every year into the national lab complex. This investment allows the national laboratories to tackle the critical scientific challenges of our time - from renewable energy to quantum computing to creating a more resilient energy grid. The discoveries and innovations being developed by the labs have an even greater impact when we invest in bringing these ideas to the market where they can benefit the nation and world. To better arm researchers to collaborate with industry and turn research and development into demonstration and deployment, DOE employs Energy I-Corps to help researchers gain industry insight to guide innovation. Energy I-Corps invites teams of researchers to participate in an intensive two-month training during which the researchers define technology value propositions, conduct customer discovery interviews, and explore viable market pathways for their technologies. Researchers return to their labs with a framework for industry engagement to guide future research and inform a culture of market engagement within the lab environment. In this way, Energy I-Corps ensures our investment in the national labs and maintains and strengthens long-term U.S. competitiveness.

cohort↗