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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

PPT-GPU: Scalable GPU Performance Modeling

Performance modeling is a challenging problem due to the complexities of hardware architectures. In this paper, we present PPT-GPU, a scalable and accurate simulation framework that enables GPU code developers and architects to predict the performance of applications in a fast, and accurate manner on different GPU architectures. PPT-GPU is part of the open source project, Performance Prediction Toolkit (PPT) developed at the Los Alamos National Laboratory. We extend the old GPU model in PPT that predict the runtimes of computational physics codes to offer better prediction accuracy, for which, we add models for different memory hierarchies found in GPUs and latencies for different instructions. To further show the utility of PPT-GPU, we compare our model against real GPU device(s) and the widely used cycle-accurate simulator, GPGPU-Sim using different workloads from RODINIA and Parboil benchmarks. The results indicate that the predicted performance of PPT-GPU is within a 10% error compared to the real device(s). In addition, PPT-GPU is highly scalable, where it is up to 450x faster than GPGPU-Sim with more accurate results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Performance Modeling of In Situ Rendering

With the push to exascale, in situ visualization and analysis will continue to play an important role in high performance computing. Tightly coupling in situ visualization with simulations constrains resources for both, and these constraints force a complex balance of trade-offs. A performance model that provides an a priori answer for the cost of using an in situ approach for a given task would assist in managing the trade-offs between simulation and visualization resources. In this work, we present new statistical performance models, based on algorithmic complexity, that accurately predict the run-time cost of a set of representative rendering algorithms, an essential in situ visualization task. To train and validate the models, we conduct a performance study of an MPI+X rendering infrastructure used in situ with three HPC simulation applications. We then explore feasibility issues using the model for selected in situ rendering questions.

Larsen, Matthew↗
DOE OSTI2016

Development and validation of chemistry agnostic flow battery cost performance model and application to nonaqueous electrolyte systems: Chemistry agnostic flow battery cost performance model

A chemistry agnostic cost performance model is described for a nonaqueous flow battery. The model predicts flow battery performance by estimating the active reaction zone thickness at each electrode as a function of current density, state of charge, and flow rate using measured data for electrode kinetics, electrolyte conductivity, and electrode-specific surface area. Validation of the model is conducted using a 4kW stack data at various current densities and flow rates. This model is used to estimate the performance of a nonaqueous flow battery with electrode and electrolyte properties used from the literature. The optimized cost for this system is estimated for various power and energy levels using component costs provided by vendors. The model allows optimization of design parameters such as electrode thickness, area, flow path design, and operating parameters such as power density, flow rate, and operating SOC range for various application duty cycles. A parametric analysis is done to identify components and electrode/electrolyte properties with the highest impact on system cost for various application durations. A pathway to 100$kWh -1 for the storage system is identified.

25 ENERGY STORAGE↗
DOE OSTI2017

PV Performance Modeling Methods and Practices: Results from the 4th PV Performance Modeling Collaborative Workshop.

In 2014, the IEA PVPS Task 13 added the PVPMC as a formal activity to its technical work plan for 2014-2017. The goal of this activity is to expand the reach of the PVPMC to a broader international audience and help to reduce PV performance modeling uncertainties worldwide. One of the main deliverables of this activity is to host one or more PVPMC workshops outside the US to foster more international participation within this collaborative group. This report reviews the results of the first in a series of these joint IEA PVPS Task 13/PVPMC workshops. The 4th PV Performance Modeling Collaborative Workshop was held in Cologne, Germany at the headquarters of TÜV Rheinland on October 22-23, 2015.

14 SOLAR ENERGY↗
DOE OSTI2016

A Performance Model for Allocating the Parallelism in a Multigrid-in-Time Solver

The traditional way to numerically solve time-dependent problems is to sequentially march through time, solving for one time step and then the next. The parallelism in this approach is limited to the spatial dimension, which is quickly exhausted, causing the gain from using more processors to solve a problem to diminish. One approach to overcome this barrier is to use methods that are parallel in time. These methods have the potential to achieve dramatically better performance compared to time-stepping approaches, but achieving this performance requires carefully choosing the amount of parallelism devoted to space versus the amount devoted to time. Here, we present a performance model that, for a multigrid-in-time solver, makes the decision on when to switch to parallel-in-time and on how much parallelism to devote to space vs. time. In our experiments, the model selects the best parallel configuration in most of our test cases and a configuration close to the best one in all other cases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Adaptive Learning for Concept Drift in Application Performance Modeling

Supervised learning is a promising approach for modeling the performance of applications running on large HPC systems. A key assumption in supervised learning is that the training and testing data are obtained under the same conditions. However, in production HPC systems these conditions might not hold because the conditions of the platform can change over time as a result of hardware degradation, hardware replacement, software upgrade, and configuration updates. These changes could alter the data distribution in a way that affects the accuracy of the predictive performance models and render them less useful; this phenomenon is referred to as concept drift. Ignoring concept drift can lead to suboptimal resource usage and decreased efficiency when those performance models are deployed for tuning and job scheduling in production systems. To address this issue, we propose a concept-drift-aware predictive modeling approach that comprises two components: (1) an online Bayesian changepoint detection method that can automatically identify the location of events that lead to concept drift in near-real time and (2) a moment-matching transformation inspired by transfer learning that converts the training data collected before the drift to be useful for retraining. We use application input/output performance data collected on Cori, a production supercomputing system at the National Energy Research Scientific Computing Center, to demonstrate the effectiveness of our approach. The results show that concept-drift-aware models obtain significant improvement in accuracy; the median absolute error of the best-performing Gaussian process regression improved by 58.8% when the proposed approaches were used.

adaptive learning↗
DOE OSTI2024

Fuel Performance Modeling Status Update and Potential Model Improvements

Fuel performance modeling status update and potential model improvements overview of TRISO fuel performance modeling codes PARFUME/BISON, AGR experiment support, potential modeling improvements, BISON smeared cracking model, BISON fission product source term, and AGR-3/4 reirradiation heating test.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Design-time performance modeling of compositional parallel programs

Performance models are powerful instruments for understanding the performance of parallel systems and uncovering their bottlenecks. Already during system design, performance models can help ponder alternative development options. However, creating a performance model – whether theoretically or empirically – for an entire application that does not exist yet is challenging. In this paper, we propose to generate performance models of full programs from performance models of their components using formal composition operators derived from parallel design patterns. As long as the design of the overall system follows such a pattern, its performance model can be predicted with reasonable accuracy without an actual implementation. In conclusion, we demonstrate our approach with design patterns of varying complexity, including pipeline, task pool, and eventually MapReduce, which is representative of a broad class of data-analytics applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

TRISO Fuel Performance Modeling

Overview of TRISO fuel performance modeling TRISO fuel performance code PARFUME TRISO fuel performance modeling with BISON PARFUME and BISON comparison

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Data-driven Mapping of the Mouse Connectome: The utility of transfer learning to improve the performance of deep learning models performing axon segmentation on light-sheet microscopy images

Light sheet microscopy has made possible the high temporal and spatial 3D imaging of both fixed and live biological tissue, with samples as large as the entire mouse brain. However, segmentation and quantification of that data remains a time-consuming manual process. Machine learning methods promise the possibility of automating this process. This study seeks to advance the performance of prior models through the application of refinements such as transfer learning.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

PV Performance Modeling - Data and Resources

The Photovoltaic (PV) Performance Modeling Collaborative (PVPMC) organized a blind PV performance modeling intercomparison to allow PV modelers to blindly test their models and modeling ability against real system data. Measured weather and irradiance data were provided along with detailed descriptions of PV systems from two locations (Albuquerque, New Mexico, USA and Roskilde, Denmark). Participants were asked to simulate the plane-of-array irradiance, module temperature, and DC power output from six systems and submit their results to Sandia for processing. This dataset includes seven MS-Excel sheets with instructions, notes and all necessary data (weather, irradiance, temperature, power) used for the data analysis of the blind modeling comparison. The hourly data represent six different systems from Albuquerque, NM and Roskilde, Denmark over a period of one year. These data are useful for PV performance model validation studies.

14 SOLAR ENERGY↗
DOE OSTI2021

Small Scale WEC Performance Modeling Data

Small Scale WEC Performance Modeling Data is performance data from downscaled models of common WEC devices and their calculated performance outputs. This data is used by the Small WEC interactive modeling tool hosted by PRIMRE. The devices include a point absorber, a two-body point absorber (RM3), an oscillating surge device (OSWEC), and an attenuator type device (McCabe Wave Pump). One of the primary use cases for this work is to give an easy way to compare power output for a variety of WECs and model sizes.

16 TIDAL AND WAVE POWER↗
DOE OSTI2016

Meso-Scale Fuel Performance Modeling using the MARMOT Code

The in-pile behavior of light-water-reactor (LWR) fuels is governed by the physics from various time and spatial scales. Accordingly, multi-scale modeling and simulation are needed to accurately predict the fuel performance at the engineering scale. To improve the traditional fuel performance modeling, the Department of Energy (DOE) Nuclear Energy Advanced Modeling and Simulation (NEAMS) program targets at developing multi-scale, multiphysics modeling tools for in-pile fuel behavior prediction under various operating or accident scenarios. In traditional fuel performance modeling, the history of fuel is represented by a single parameter, fuel burn-up. Burn-up measures the energy that has been extracted from fuels. However, it is not an adequate measure of fuel history as it ignores the flux effect, which is critical for the irradiation-induced microstructural evolution in fuels. Moreover, the traditional fuel performance models are usually fitted to experimental data. Their capabilities of being extrapolated to new operating conditions and being extended to new types of fuels are strongly limited by the empirical or semi-empirical nature. To improve the burn-up based models, a microstructure based fuel approach, the MOOSE-BISON-MARMOT (MBM) tool-kit, is being developed under the fuel product line (FPL) of the NEAMS program for predictive fuel performance modeling. In the MBM approach, the evolution of fuel microstructure and changes in fuel properties are described by the MARMOT code. The transient fuel properties obtained from MARMOT are then fed into the BISON code for engineering scale fuel performance modeling. Both BISON and MARMOT are developed within the MOOSE framework, which was developed at Idaho National Laboratory. In this summary, the development and the capabilities of the MARMOT code will be introduced. In the past few years, several materials models have been developed including: (1) a grain growth model that describes the grain size evolution as a function of time, temperature and fuel porosity, considering the effects of impurity and porosity dragging and anisotropic grain boundary energy, (2) a fission gas release model that describes the fission gas diffusion in grain interior, fission gas bubble formation in grain interior and at grain boundaries, and fission gas release to the fuel-cladding gap, (3) a fracture model that describes the fracture initiation and propagation in grain interior and along grain boundaries, and (4) a thermal conductivity model that describes the change in thermal conductivity due to grain growth, gas bubble formation, fracture and radiation damage. Several other models are also being developed in MARMOT for hydride formation in Zr-based cladding, radiation damage and grain size subdivision. In most cases, these materials models are coupled with each other. For instance, to accurate predict the transient thermal conductivity, information on grain size, fission gas density, porosity and fracture is required. Further development is on going to improve all these models. These models are developed primarily for UO{sub 2} fuels used in LWRs. They can be extended to other fuels with possibly new development. All models in MARMOT will be rigorously verified and validated before being applied for fuel performance modeling. A version of MARMOT was recently released in September 2015 with an assessment report. (authors)

36 MATERIALS SCIENCE↗