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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3

An Unsteady State Gas Transport Model in Matrix Laboratory (MATLAB) for PWR Gas Management

Reactor Coolant System (RCS) hydrogen gas management can be a challenge at Pressurized Water Reactors (PWRs) during shutdown, startup, and steady state operation. Poor hydrogen control can result in higher general corrosion rates for RCS components and increases in critical path time for shutdown or startup. Also, performance indicator penalties may be accrued by entry into Electric Power Research Institute (EPRI) Action Level conditions or by accrual of Chemistry Effectiveness Index (CEI) penalties established by the Institute of Nuclear Power Operations (INPO). Furthermore, poor hydrogen control may increase industrial safety risk due to the creation of an explosive environment. Part of the hydrogen management difficulty arises from the interaction of several phenomena that require complex simulations to fully understand. While hydrogen gas transport models have been developed, the application of these models is limited by the computational intensity required to accurately model the dynamism inherent in plant shutdown or startup. Subsequently, a gas transport model is developed in a suitable computational program to meet these needs and provide input to an optimized RCS hydrogen management strategy for all operational modes. The gas transport model is used to model RCS hydrogen behavior for an example PWR during an End of Cycle (EOC) Forced Outage (FO) startup, a refueling outage (RFO) shutdown, and during nominal steady state operation. The computational software selected is Matrix Laboratory (MATLAB{sup R})

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Exploration of multifidelity UQ sampling strategies for computer network applications.

Network modeling is a powerful tool to enable rapid analysis of complex systems that can be challenging to study directly using physical testing. Here, two approaches are considered: emulation and simulation. The former runs real software on virtualized hardware, while the latter mimics the behavior of network components and their interactions in software. Although emulation provides an accurate representation of physical networks, this approach alone cannot guarantee the characterization of the system under realistic operative conditions. Operative conditions for physical networks are often characterized by intrinsic variability (payload size, packet latency, etc.) or a lack of precise knowledge regarding the network configuration (bandwidth, delays, etc.); therefore uncertainty quantification (UQ) strategies should be also employed. UQ strategies require multiple evaluations of the system with a number of evaluation instances that roughly increases with the problem dimensionality, i.e., the number of uncertain parameters. It follows that a typical UQ workflow for network modeling based on emulation can easily become unattainable due to its prohibitive computational cost. In this paper, a multifidelity sampling approach is discussed and applied to network modeling problems. The main idea is to optimally fuse information coming from simulations, which are a low-fidelity version of the emulation problem of interest, in order to decrease the estimator variance. By reducing the estimator variance in a sampling approach it is usually possible to obtain more reliable statistics and therefore a more reliable system characterization. Several network problems of increasing difficulty are presented. For each of them, the performance of the multifidelity estimator is compared with respect to the single fidelity counterpart, namely, Monte Carlo sampling. For all the test problems studied in this work, the multifidelity estimator demonstrated an increased efficiency with respect to MC.

97 MATHEMATICS AND COMPUTING↗

Computational Fluid Dynamics Modeling of the Fuel Reactor in NETL's 50 kW th Chemical Looping Facility

The National Energy Technology Laboratory (NETL) has explored chemical looping in its 50 kW th facility using a number of oxygen carriers. In this work, the results for methane conversion in the fuel reactor with a hematite iron ore as the oxygen carrier are analyzed. The experimental results are compared to predictions using CPFD's barracuda computational fluid dynamics (CFD) code with kinetics derived from the analysis of fixed bed data. It has been found through analytical techniques from thermal gravimetric analysis data as well as the same fixed bed data that the kinetics for the methane–hematite reaction follows a nucleation and growth or Johnson–Mehl–Avrami (JMA) reaction mechanism. barracuda does not accept nucleation and growth kinetics; however, there is enough sufficient variability of the solids dependence within the software such that the nucleation and growth behavior can be mimicked. This paper presents the method to develop the pseudo-JMA kinetics for barracuda extracted from the fixed bed data and then applies these values to the fuel reactor data to compare the computational results to experimental data obtained from 50 kW th unit for validation. Lastly, a fuel reactor design for near complete conversion is proposed.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Determination of anharmonic free energy contributions: Low temperature phases of the Lennard-Jones system

We investigate a general method to calculate the free energy of crystalline solids by considering the harmonic approximation and quasistatically switching the anharmonic contribution. The advantage of this method is that the harmonic approximation provides an already very accurate estimate of the free energy, and therefore the anharmonic term is numerically very small and can be determined to high accuracy. We further show that the anharmonic contribution to the free energy satisfies a number of exact inequalities that place constraints on its magnitude and allows approximate but fast and accurate estimates. The method is implemented into a readily available general software by combining the code HOODLT (Highly Optimized Object Oriented Dynamic Lattice Theory) for the harmonic part and the molecular dynamics (MD) simulation package HOOMD-blue for the anharmonic part. We use the method to calculate the low temperature phase diagram for Lennard-Jones particles. We demonstrate that hcp is the equilibrium phase at low temperature and pressure and obtain the coexistence curve with the fcc phase, which exhibits reentrant behavior. Furthermore, several implications of the method are discussed.

36 MATERIALS SCIENCE↗

MDDC Multi-Length Scale Data Architecture Contribution Report – PNNL, INL, ANL, LANL and ORNL

This report offers a comprehensive view of data streams currently generated at Pacific Northwest National Laboratory, Idaho National Laboratory, Argonne National Laboratory, Los Alamos National Laboratory, and Oak Ridge National Laboratory set to integrate into the evolving Multi-Dimensional Data Correlation framework at Oak Ridge National Laboratory. Developed by the Advanced Materials and Manufacturing Technologies program, the Multi-Dimensional Data Correlation framework serves as a cutting-edge software to manage data relevant to advanced manufacturing and material behavior in advanced reactors. The report defines data streams, highlights their generation methods and visualization methods both for experimental and computational aspects relevant to the Advanced Materials and Manufacturing Technologies project. A logical next step for this work is to integrate the MDDC framework into PNNL’s, INL’s, ANL’s, LANL’s and ORNL’s fabrication, experimentation, and modelling workflows. This would require setting up the MDDC framework at PNNL, INL, ANL, and LANL and integrating it into the data collection and storage for these different activities.

36 MATERIALS SCIENCE↗

Distributed Macroscopic Traffic Simulation with Open Traffic Models

This paper presents OTM-MPI, an extension of the Open Traffic Models platform (OTM) for running macroscopic traffic simulations in high-performance computing environments. OTM-MPI represents the first open-source, distributed-memory, macroscopic simulation model developed for modern high performance parallel machines and large networks. Macroscopic simulations are appropriate for studying regional traffic scenarios when aggregate trends are of interest, rather than individual vehicle traces. They are also appropriate for studying the routing behavior of classes of vehicles, such as app-informed vehicles. The network partitioning was performed with METIS. Inter-process communication was done with MPI (message-passing interface). Results are provided for two networks: one realistic network which was obtained from Open Street Maps for Chattanooga, TN, and another larger synthetic grid network. The software recorded a speedups of 198x using 256 cores for Chattanooga, and 475x with 1,024 cores for the synthetic network.

macro-scopic traffic simulation↗

Assessing VQLS for Fluid Dynamics on a Hybrid Quantum-HPC Stack

Recent advances in quantum linear solvers offer a promising direction for accelerating extreme scientific computations such as fluid dynamics. However, the deep and complex circuits required by many quantum algorithms limit their practical use on current quantum hardware. The Variational Quantum Linear Solver (VQLS) presents a viable alternative for near-term quantum devices (NISQ), and initial efforts have explored its application to select fluid dynamics problems. In this work, we evaluate the use of VQLS for canonical fluid dynamics problems, aiming to identify pathways for generalizing its implementation across a broader class of systems. We analyze the impact of various circuit ansatz and classical optimizers on solution quality and convergence behavior. Furthermore, we assess the algorithm's feasibility within a hybrid quantum–high-performance computing (HPC) framework by porting it to QFw, a state-of-the-art quantum-HPC software stack. 11This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the US DOE under Contract No. DE-AC05-00OR22725.

Gopalakrishnan Meena, Murali [ORNL] (ORCID:0000000↗

Preliminary Thermal-Hydraulic Analysis of the UTA–2 Subcritical Linear Accelerator–Driven System

The National Nuclear Security Administration’s mission includes establishing a reliable supply of 99 Mo without highly enriched uranium. Oak Ridge National Laboratory (ORNL) supports this objective through collaborative research and development with industrial partners. Niowave Inc., a current partner, is currently designing a subcritical linear accelerator-driven system (ADS) and preparing for the US Nuclear Regulatory Commission’s licensing process. Niowave’s system has the potential to efficiently supply medical radioisotopes. The technology includes a superconducting electron accelerator and a pile of both natural and low-enriched uranium (LEU) targets. The process fissions uranium and many valuable isotopes can then be extracted from the targets. Niowave is currently iterating through conceptual and detailed design processes for several system sizes. This report discusses UTA–2, which is at the demonstration stage. UTA–2 will validate numerical modeling results with experimental measurements before progressing to the detailed design of UTA–3, the commercial-sized ADS. The thermal-hydraulic behavior of the UTA–2 core design was numerically investigated using STAR-CCM+, as described in this report. STAR-CCM+, a state-of-the-art computational fluid dynamics (CFD) software that was commercially developed by Siemens, has an extensive user base and a set of validation studies. It is also compliant with the American Society of Mechanical Engineers’ Nuclear Quality Assurance 1 standard. Three cases are investigated in this report. Case 1 quantified the temperature field within the UTA–2 assembly and water tank using only conduction as the method of thermal energy transport. This simplified approach was overly conservative and yielded wetted cladding temperatures above the coolant saturation temperature. In this case, the maximum temperature of the wetted cladding surface of the highest power rod exceeded the saturation temperature of water by 63.8°C. Because of the overly conservative approach taken in Case 1 and its negative subcooled margin, buoyancy-driven natural circulation flow physics were implemented in Case 2. Adding coolant motion significantly distributed the thermal energy of the system through convective heat transfer. This relatively small amount of convective heat transfer significantly reduced system temperatures and increased the subcooled margin from –63.8 to 61.3°C. This margin confirmed that no boiling was expected during normal operation of UTA–2 at 230 W. Case 3 had no additional physics models but considered an overpower event during which the power of each LEU and natural uranium rod was at its respective peak values. This resulted in a study with the total assembly power equal to 176% of the nominal power of 230 W considered in Cases 1 and 2. The resulting natural circulation flows were slightly enhanced. The subcooled margin decreased slightly to 46.3°C, which is still a significant margin to local boiling of the water within the tank. This margin confirmed that no boiling was expected during an abnormal operation of UTA–2 at 406 W.

07 ISOTOPE AND RADIATION SOURCES↗

Numerical Investigation of Enhanced Dehumidification Processes By Using Dielectrophoresis Principles in Moist Airflows

Dispersed particle-laden flows are encountered in many building and industrial applications, such as flow in a fluidized bed, hydrocarbon transportation in pipelines, and the fouling of air-cooled heat exchangers (Kuruneru et al., 2016; Ray et al., 2019; Wang et al., 2019). Computational fluid dynamic (CFD) models have been developed in recent years to depict particle-fluid and particle-particle interactions in laminar or turbulent flows with increasing accuracy and stability. One particular particle-laden system of interest for moisture control is electrically-enhanced condensation in air and water droplet flows. Electrically-enhanced condensation consists of the use of highly charged water droplets injected in the moist air. The droplets become electric seeds that attract polar water vapor molecules to their surfaces and promote condensation. The nucleation and growth of the charged droplets deplete the vapor phase near a droplet, which is compensated for by the dielectrophoresis flow and diffusion. Dielectrophoresis flow involves surrounding vapor at a distance of about 10 to 100 nm for droplets charged by an electrospray compared to ~2 nm for a single electron charge in a droplet. As the vapor molecules collapse on the surface of the droplets, their initial electrical charge decreases with time due to the neutralization of the ions. While the physics of this phenomena is well known, engineering models for predicting the condensation rates are not available. This work computationally investigates dehumidification of moist airflow in a converging rectangular duct. The objective is to develop an engineering model that predicts water vapor condensation by employing dielectrophoresis principles. We construct a Computational Fluid Dynamics (CFD) model of the duct with electrically-enhanced condensation. The model is implemented in the open-source software OpenFOAM. We utilize the Multi-Phase Particle-In-Cell (MP-PIC) method coupled with a Population Balance Equation (PBE) approach to simulate the particle-laden system. This methodology is an Eulerian-Lagrangian approach used to simulate the droplets' behavior in the humid air. The MP-PIC approach (Andrews and O'Rourke, 1996) mitigates the computational cost by parceling several fundamental particles with similar properties (such as types, sizes, and temperature) into one computational particle. Thus, the billions of particles can be substituted by millions of computational particles without significant loss of information. The PBE was considered with the Lagrangian frame to combine the particle distribution function used in MP-PIC (Kim et al., 2020). This approach preserves mass and energy conservation between the phases in the Eulerian and Lagrangian structures. The PBE in this procedure was directly linked to the discrete parcels, making the simulation of the particle distribution computationally efficient and robust. The MP-PIC-PBE approach used in the present work was applied to the dehumidification of air. Water droplets were injected in the air stream and forced to grow according to experimentally derived correlation. The experiments were conducted on a converging duct with the same geometry and boundary conditions used to build the CFD model. This approach enabled us to approximate the effect of dielectrophoresis phenomena on the droplet and air interface. This presentation will discuss the details of the new CFD model built for the duct, the implementation of the model in OpenFOAM CFD programming language, and the experimental validation of the newly developed model. The results revealed a moderate yet measurable increase in droplet diameter due to water vapor condensation at the vapor-liquid interface of the electrically charged droplets' surface. The seed water droplet particles grew in size by capturing the water vapor in the surrounding air. The OpenFOAM model predicted reductions of humidity in the air from 5 to 10 percent.

Yel Mahi, Maliha↗

SU-F-I-43: A Software-Based Statistical Method to Compute Low Contrast Detectability in Computed Tomography Images

Purpose: The low contrast detectability (LCD) of a CT scanner is its ability to detect and display faint lesions. The current approach to quantify LCD is achieved using vendor-specific methods and phantoms, typically by subjectively observing the smallest size object at a contrast level above phantom background. However, this approach does not yield clinically applicable values for LCD. The current study proposes a statistical LCD metric using software tools to not only to assess scanner performance, but also to quantify the key factors affecting LCD. This approach was developed using uniform QC phantoms, and its applicability was then extended under simulated clinical conditions. Methods: MATLAB software was developed to compute LCD using a uniform image of a QC phantom. For a given virtual object size, the software randomly samples the image within a selected area, and uses statistical analysis based on Student’s t-distribution to compute the LCD as the minimal Hounsfield Unit’s that can be distinguished from the background at the 95% confidence level. Its validity was assessed by comparison with the behavior of a known QC phantom under various scan protocols and a tissue-mimicking phantom. The contributions of beam quality and scattered radiation upon the computed LCD were quantified by using various external beam-hardening filters and phantom lengths. Results: As expected, the LCD was inversely related to object size under all scan conditions. The type of image reconstruction kernel filter and tissue/organ type strongly influenced the background noise characteristics and therefore, the computed LCD for the associated image. Conclusion: The proposed metric and its associated software tools are vendor-independent and can be used to analyze any LCD scanner performance. Furthermore, the method employed can be used in conjunction with the relationships established in this study between LCD and tissue type to extend these concepts to patients’ clinical CT images.

60 APPLIED LIFE SCIENCES↗

DT-HYDRO

The software solves the time dependent, one dimensional (1D) coupled mass and momentum balance equations governing the elastic flow of water through the penstock, turbine and draft tube in a hydroelectric facility using a high order finite volume based method. The numerical method is based on the Kurganov-Tadmor central method paired with the Monotonic Upstream-centered Scheme for Conservation Laws (MUSCL). This solution method accurately resolves the fast transient behavior of the flow, including water hammer. Additionally, the software estimates the full 3D flow field within the turbine chamber in real time, a feat that is made possible by leveraging pre-computed CFD results by utilizing a reduced order modeling method based on an efficient randomized singular value decomposition (SVD) driven proper orthogonal decomposition (POD) with POD-mode weight regression. The reduced order model of the 3D flow is directly coupled to the 1D elastic flow model so the entire flow field through the penstock and turbine system is resolved quickly and with high fidelity.

Gurecky, William [Oak Ridge National Laboratory (O↗

GridOPTICS/GridPACK

GridPACK is a software framework consisting of a set of modules designed to simplify the development of programs that model the power grid and run on parallel, high performance computing platforms. It also contains several fully developed applications, including powerflow, dynamic simulation, state estimation, Kalman filter analysis (dynamic state estimation), contingency analysis and real time path rating. These applications can be used either standalone or as components in more complicated workflows that combine several different types of application together. The framework modules are available as a combination of libraries and software templates and consist of components for setting up and distributing power grid networks, support for modeling the behavior of individual buses and branches in the network, converting the network models to the corresponding algebraic equations, and parallel routines for manipulating and solving large algebraic systems. The framework also contains a module for distributing tasks evenly amongst computing resources, even if individual tasks vary widely in their execution times. Additional modules support input and output, basic statistical analysis of contingency based calculations, distributed data structures, as well as basic profiling and error management.

Palmer, Bruce↗

Streamlining the Design-to-Build Transition with Build-Optimization Software Tools

Scaling-up capabilities for the design, build, and test of synthetic biology constructs holds great promise for the development of new applications in fuels, chemical production, or cellular-behavior engineering. Construct design is an essential component in this process; however, not every designed DNA sequence can be readily manufactured, even using state-of-the-art DNA synthesis methods. Current biological computer-aided design and manufacture tools (bioCAD/CAM) do not adequately consider the limitations of DNA synthesis technologies when generating their outputs. Designed sequences that violate DNA synthesis constraints may require substantial sequence redesign or lead to price-premiums and temporal delays, which adversely impact the efficiency of the DNA manufacturing process. In this paper, we have developed a suite of build-optimization software tools (BOOST) to streamline the design-build transition in synthetic biology engineering workflows. BOOST incorporates knowledge of DNA synthesis success determinants into the design process to output ready-to-build sequences, preempting the need for sequence redesign. The BOOST web application is available at https://boost.jgi.doe.gov and its Application Program Interfaces (API) enable integration into automated, customized DNA design processes. Finally, the herein presented results highlight the effectiveness of BOOST in reducing DNA synthesis costs and timelines.

59 BASIC BIOLOGICAL SCIENCES↗

INTEGRATED WORKFLOW MANAGEMENT FOR PARTICLE ACCELERATOR SIMULATION

Supercomputing systems are used for a wide range of computationally demanding tasks in many fields of science and engineering. They play a key role in numerical simulation, in which mathematical models are computed in order to simulate the behavior of physical systems. Scientists and engineers that use supercomputers for numerical simulation often have their productivity limited by the need to manually organize and manage extremely large amounts of data that are often produced and consumed by the software programs run on these systems. Recognizing these limitations, Kitware Inc. (Clifton Park, NY) and SLAC National Accelerator Laboratory (Menlo Park, CA) are developing an advanced software platform that can reduce the cognitive overhead required by knowledge workers when using supercomputers for numerical simulation. Phase I of the project is complete and includes the development of new capabilities for organizing simulation project files, improvements to the user interface and overall usability, and deployment of a “middle tier” server to sit between user desktop machines and supercomputers to offload much of the data management workload. The project also developed prototype software for executing sequences of numerical simulations, and a prototype for migrating supercomputing software to cloud-based computing systems to provide a potential alternative to supercomputers with different logistical and price-to-performance tradeoffs.

Tourtellott, John↗

Grain2Mesh: Mesh Generation for Grain-Scale Nonlinear Elasticity Modeling

The nonlinear hysteretic behavior of rocks under cyclic loading is a crucial area of study in geomechanics. The macroscopic response of a variety of materials has been found to be contingent upon the behavior of the micro-scale structure. This project aims to develop a functional and maintainable software package for generating a multi-phase numerical mesh and accompanying simulation files for finite element modeling used in computational mechanics solvers. Meshes generated from images often lack key preprocessing that reduces noise and prevents mesh element distortion that can increase computational cost. By incorporating user feedback throughout, grain2mesh ensures a high-fidelity mesh that can be used to model grain-scale interactions such as shearing, crack propagation, and interfacial material contrast. Scientific applications of this software include material fracturing, stress-strain analysis for natural and engineered materials, and nonlinear meso-scale analysis.

54 ENVIRONMENTAL SCIENCES↗

Barrier elision for production parallel programs

Large scientific code bases are often composed of several layers of runtime libraries, implemented in multiple programming languages. In such situation, programmers often choose conservative synchronization patterns leading to suboptimal performance. In this paper, we present context-sensitive dynamic optimizations that elide barriers redundant during the program execution. In our technique, we perform data race detection alongside the program to identify redundant barriers in their calling contexts; after an initial learning, we start eliding all future instances of barriers occurring in the same calling context. We present an automatic on-the-fly optimization and a multi-pass guided optimization. We apply our techniques to NWChem - a 6 million line computational chemistry code written in C/C++/Fortran that uses several runtime libraries such as Global Arrays, ComEx, DMAPP, and MPI. Our technique elides a surprisingly high fraction of barriers (as many as 63%) in production runs. This redundancy elimination translates to application speedups as high as 14% on 2048 cores. Our techniques also provided valuable insight about the application behavior, later used by NWChem developers. Overall, we demonstrate the value of holistic context-sensitive analyses that consider the domain science in conjunction with the associated runtime software stack.

97 MATHEMATICS AND COMPUTING↗

Mass Spectrometer Transient Analysis

This software implements a complete preprocessing pipeline for transient mass spectrometry (MS) data collected during TAP (Temporal Analysis of Products) experiments. It is designed to extract chemically meaningful fluxes from overlapping ion signals by applying a calibrated defragmentation matrix and solving the resulting linear system using non-negative least squares (NNLS) regression. The core script, preprocess_mass_spec.py, performs the following operations: Gain correction: Applies amplifier gain scalars derived from inert-packed calibration pulses to normalize signal intensities across AMUs and acquisition settings. Background subtraction: Removes experiment baselines using user-defined time windows, ensuring compatibility with slow-diffusing species and preventing negative values that would interfere with NNLS. Options to subtract before and after defragmentation. Defragmentation: Constructs a fragmentation matrix A from zeroth moments of calibration pulses (equal molar gas:inert mixtures) and solves Ax=b at each time point, where b is the raw MS signal and x is the estimated species flux. The matrix is normalized to inert signals and accounts for instrument-specific fragmentation behavior. Pulse-mode handling: Supports both averaged and individual pulse modes, enabling statistical treatment of fluxes and calculation of standard deviations. Integration and output: Computes zeroth moments (integrated fluxes) and exports time-resolved and integrated data in CSV format, suitable for downstream kinetic modeling. The software is validated using both virtual TAP simulations (VTAP) and experimental data from propane dehydrogenation (PDH) on CrOx/Al2O3 catalysts. It preserves temporal resolution by applying NNLS point-by-point across the pulse duration (typically 6,000+ time slices per pulse), leveraging the linear superposition principle to reconstruct full flux profiles. The defragmented outputs are compatible with kinetic extraction methods such as the G and Y procedures, which are used to derive rate–concentration relationships from TAP data. The details of these validations are discussed in detail in the supporting manuscript and supporting information. Example data and output files are also included. The methodology is robust to experimental noise and drift, with calibration protocols that account for pulse size effects, MS aging, and inert gas normalization. The software is modular, reproducible, and tailored for high-throughput TAP-MS workflows in catalysis research.

Kristy, Stephen [Idaho National Laboratory (INL), ↗

Understanding the Impact of Dynamic Power Capping on Application Progress

Electrical power has become an important design constraint in high-performance computing (HPC) systems. On future HPC machines, power is likely to be a budgeted resource and thus managed dynamically. Power management software needs to reliably measure application performance at runtime in order to respond effectively to changes in application behavior. Execution time tells us little about how the science in the application is progressing toward an application-defined end goal. To the best of our knowledge, no study has defined or categorized online application progress in the context of power management. Based on semi-structured interviews with HPC application-specialists, we define an online notion of progress—an application-specific metric that can be monitored at runtime to provide a sense of the rate at which application science is being performed. Using instrumentation, we characterize and categorize the progress of various production scientific applications and benchmarks. We propose a model of the impact of dynamic power capping on application progress. By experimental evaluation, we show that our model accurately captures the general behavior of the progress of different classes of applications under a power cap. We believe that such a model is an important first step toward the design of more dynamic power management policies for HPC systems.

application progress↗