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At least 109 records · Page 6

All sources NASA NTRS DOE OSTI

DOE OSTI2019

Software Resilience using Kokkos Ecosystem

Due to the cost of hardware failures within mission critical and scientific applications, it is necessary for software to provide a mechanism to prevent or recover from interruptions. The Kokkos ecosystem is a programming environment that provides performance and portability to many applications that run on DOE supercomputers as well as smaller scale systems. These applications require a higher level of service due to the cost associated with each simulation or the critical nature of the mission. Software resilience enables an application of manage hardware failures reducing the cost of an interruption. Two different resilience methodologies have been added to the Kokkos ecosystem: checkpointing has been added for restart capabilities and a resilient execution model has been added to account for failures in compute devices. The design and implementation of each of these additions are described, and appropriate examples are included for end users.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

(U) Radiation Hydrodynamics in the Ristra Project’s Symphony code: Initial release

One of the recent focuses in the next-generation code (NGC/Ristra)project at LANL is to develop a cell-centered Lagrangian radiation-hydrodynamics code under FleCSI framework. The FleCSI frame-work provides a unified data and execution model in order to insulate production code development from the uncertainty of evolving runtimeand architectures. We have developed the code, Symphony, which in-corporates the hydrodynamics code, FleCSALE, and the radiation transport code, Puno.This memo is intended to describe the coupling of radiation and hydrodynamics within the Ristra Project’s code Symphony.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

User-Defined Types as Task Parameters and Arguments in FleCSI

FleCSI is a C++ framework for writing multi-physics simulations. FleCSI abstracts away complicated backends, enabling high performance while eliminating the need for users to directly manage memory and execution. This is done via FleCSI’s data and execution model.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

The Athena++ Adaptive Mesh Refinement Framework: Design and Magnetohydrodynamic Solvers

The design and implementation of a new framework for adaptive mesh refinement calculations are described. It is intended primarily for applications in astrophysical fluid dynamics, but its flexible and modular design enables its use for a wide variety of physics. The framework works with both uniform and nonuniform grids in Cartesian and curvilinear coordinate systems. It adopts a dynamic execution model based on a simple design called a "task list" that improves parallel performance by overlapping communication and computation, simplifies the inclusion of a diverse range of physics, and even enables multiphysics models involving different physics in different regions of the calculation. We describe physics modules implemented in this framework for both nonrelativistic and relativistic magnetohydrodynamics (MHD). These modules adopt mature and robust algorithms originally developed for the Athena MHD code and incorporate new extensions: support for curvilinear coordinates, higher-order time integrators, more realistic physics such as a general equation of state, and diffusion terms that can be integrated with super-time-stepping algorithms. The modules show excellent performance and scaling, with well over 80% parallel efficiency on over half a million threads. The source code has been made publicly available.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2013

A Framework to Survey the Energy Efficiency of Installed Motor Systems

While motors are ubiquitous throughout the globe, there is insufficient data to properly assess their level of energy efficiency across regional boundaries. Furthermore, many of the existing data sets focus on motor efficiency and neglect the connected drive and system. Without a comprehensive survey of the installed motor system base, a baseline energy efficiency of a country or region’s motor systems cannot be developed. The lack of data impedes government agencies, utilities, manufacturers, distributers, and energy managers when identifying where to invest resources to capture potential energy savings, creating programs aimed at reducing electrical energy consumption, or quantifying the impacts of such programs. This paper will outline a data collection framework for use when conducting a survey under a variety of execution models to characterize motor system energy efficiency within a country or region. The framework is intended to standardize the data collected ensuring consistency across independently conducted surveys. Consistency allows for the surveys to be leveraged against each other enabling comparisons to motor system energy efficiencies from other regions. In creating the framework, an analysis of various motor driven systems, including compressed air, pumping, and fan systems, was conducted and relevant parameters characterizing the efficiency of these systems were identified. A database using the framework will enable policymakers and industry to better assess the improvement potential of their installed motor system base particularly with respect to other regions, assisting in efforts to promote improvements to the energy efficiency of motor driven systems.

Rao, Prakash↗

DOE OSTI2022

DUNE Software and High Performance Computing

DUNE, like other HEP experiments, faces a challenge related to matching execution patterns of our production simulation and data processing software to the limitations imposed by modern high-performance computing facilities. In order to efficiently exploit these new architectures, particularly those with high CPU core counts and GPU accelerators, our existing software execution models require adaptation. In addition, the large size of individual units of raw data from the far detector modules pose an additional challenge somewhat unique to DUNE. Here we describe some of these problems and how we begin to solve them today with existing software frameworks and toolkits. We also describe ways we may leverage these existing software architectures to attack remaining problems going forward. This whitepaper is a contribution to the Computational Frontier of Snowmass21.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Comparing LLC-Memory Traffic between CPU and GPU Architectures

The cache hierarchy in modern CPUs and GPUs is becoming increasingly complex, which makes understanding the handshake between the memory access patterns and the cache hierarchy difficult. Moreover, the details of different cache policies are not publicly available. Therefore, the research community relies on observation to understand the relationship between memory access patterns and cache hierarchy. Our previous studies delved into the different microarchitectures of Intel CPUs. In this study, GPUs from NVIDIA and AMD are considered. Even though the execution models in CPUs and GPUs are distinct, this study attempts to correlate the behavior of the cache hierarchy of CPUs and GPUs. Using the knowledge gathered from studying Intel CPUs, the similarities and dissimilarities between CPUs and GPUs are identified. Through model evaluation, this study provides a proof of concept that traffic between last-level cache and memory can be predicted for sequential streaming and strided access patterns on GPUs.

Monil, M. A. H.↗

DOE OSTI2018

Simulated execution of hybrid quantum computing systems

We use modeling and simulation to study the behavior and performance of hybrid quantum computing systems. Our approach is based on a layered design with abstract machine models, which identify the key components and interfaces for quantum processing units, quantum programming models, and hybrid execution models. We use discrete-event simulating to track the dynamical state of hierarchical abstract machine models while executing test programs, and we collect statistics on time and energy consumption to forecast the resources required by quantum processors for future scientific computation.

Humble, Travis↗

DOE OSTI2017

SimApp 1.8.x

System for converting DESIRE (Direct Executing Simulation in Real Time) simulation models to executable code models (C# / C++). These converted models are used as the basis for 2 products, one of which is the focus of this review: an engineering design interface for Fissile Solution Systems. ModelConverter.exe (C#) converts DESIRE model inputs to a C++ plug-in subclass. SimApp.exe (C# / C++) has a C++ dll that is a numerical integration engine that the Converted models plug into. The engineering design interface is a C# GUI that calls on the C++ dll to execute a particular converted model. The GUI allows the user to specify the length of a simulation, run the simulation, view data plots and data tables. Stability plots may also be produced and manipulated. The engineering design interface will be released as source code and executable; the model converter source is also intended for release.

Determan, JohnC.↗

DOE OSTI2017

SimApp 1.8.x

Determan, John C.↗

DOE OSTI2016

Performance Models for Split-execution Computing Systems

Split-execution computing leverages the capabilities of multiple computational models to solve problems, but splitting program execution across different computational models incurs costs associated with the translation between domains. We analyze the performance of a split-execution computing system developed from conventional and quantum processing units (QPUs) by using behavioral models that track resource usage. We focus on asymmetric processing models built using conventional CPUs and a family of special-purpose QPUs that employ quantum computing principles. Our performance models account for the translation of a classical optimization problem into the physical representation required by the quantum processor while also accounting for hardware limitations and conventional processor speed and memory. We conclude that the bottleneck in this split-execution computing system lies at the quantum-classical interface and that the primary time cost is independent of quantum processor behavior.

multiprocessor↗

DOE OSTI2024

Groundwater and Surface Water Flow (GSFLOW) model files to explore bedrock circulation depth and porosity in Copper Creek, Colorado

This data package contains integrated hydrological model input and output files for Copper Creek, Colorado (24 km2), a tributary of the East River located in the headwaters of the Upper Colorado River Basin. The model code is the U.S. Geological Survey (USGS) Groundwater and Surface Water Flow (GSFLOW) model. The model contains a 100-m grid resolution and a daily timestep. The land surface model is dynamically linked to a three-dimensional groundwater flow model that allows for streamflow gaining and losing conditions. The groundwater model contains 12 model layers and extends 400 m below land surface. The original Copper Creek model was modified to contain geologic layers representing saprolite, shallow bedrock, and deep bedrock. Endmember depth versus hydraulic conductivity relationships and porosity values for fractured crystalline rock are simulated. For the shallow case, median flow depths occur in the shallow saprolite at depths <8 m, while the deep case promotes a median groundwater flow depth of 100 m. With this modeling framework we compare streamflow response to a plausible worst-case drought lasting up to five years. Streamflow metrics of analysis include average streamflow, fraction of stream network that is dry, no-flow duration, average groundwater flow to streams and time to recovery following the drought. Results and implications are presented in a paper submitted to Geophysical Research Letters titled, "The role of bedrock circulation depth and porosity in mountain streamflow response to prolonged drought" by Rosemary WH. Carroll, Andrew H. Manning and Kenneth H Williams. A Readme.txt file provides instructions on how to download all model files and execute each model scenario. In addition to the GSFLOW output/prms/copper_drought.csv file containing daily basin water stores and fluxes (refer to GSFLOW manual) and the output/prms/copper_drought_statvar.dat file with output defined in the gsflow3.control file (refer to GSFLOW Manual), output files also include spatially distributed daily values of total evapotranspiration, canopy evaporation, precipitation, snowfall, infiltration, snow water equivalent, potential evapotranspiration, recharge, sublimation, soil moisture, contributing interflow, water table elevations, changes in groundwater storage, groundwater evapotranspiration, interbasin groundwater flow (limited to the alluvium below the stream outlet), and surface-groundwater exchanges within the river system.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

ML-PSA

The computer code uses a parallel simulated annealing framework with embedded machine learning components to solve multi-constrained optimization problems. The software automatically balances the execution of low and high fidelity physics models within the optimization procedure. The low fidelity model is used to rapidly explore the design space while the high fidelity physics model is executed sparingly to account for complex design constraints that are not resolved by the quickly executing low fidelity model.

Gurecky, William↗

DOE OSTI2023

Model certainty in cellular network-driven processes with missing data

Mathematical models are often used to explore network-driven cellular processes from a systems perspective. However, a dearth of quantitative data suitable for model calibration leads to models with parameter unidentifiability and questionable predictive power. Here we introduce a combined Bayesian and Machine Learning Measurement Model approach to explore how quantitative and non-quantitative data constrain models of apoptosis execution within a missing data context. We find model prediction accuracy and certainty strongly depend on rigorous data-driven formulations of the measurement, and the size and make-up of the datasets. For instance, two orders of magnitude more ordinal (e.g., immunoblot) data are necessary to achieve accuracy comparable to quantitative (e.g., fluorescence) data for calibration of an apoptosis execution model. Notably, ordinal and nominal (e.g., cell fate observations) non-quantitative data synergize to reduce model uncertainty and improve accuracy. Finally, we demonstrate the potential of a data-driven Measurement Model approach to identify model features that could lead to informative experimental measurements and improve model predictive power.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

Fast estimation of dense gas dispersion from multiple continuous CO 2 surface leakage sources for risk assessment

Surface leakage of CO 2 , and associated potential impacts on health, safety, and the environment (HSE) are considered hazards of geologic carbon sequestration (GCS). There are two challenges associated with impact assessment of CO 2 surface dispersion. First, the fact that CO 2 is a dense gas makes its dispersion in air a complex process. Rigorous numerical solutions for modeling concentration distributions are relatively time-consuming. Second, impact assessment requires consideration of uncertainty, e.g., quantification of how much uncertainty is propagated through input parameters to model outputs by carrying out large numbers of model runs. In order to assess the potential consequences of surface leakage of CO 2 , it is useful to have a model that executes very quickly for repeated model calculations (e.g., in Monte Carlo mode) of the atmospheric dispersion of CO 2 (concentrations as a function of space and time). In addition, the model should be able to handle multiple surface leakage sources. In this work, we have extended the nomograph approach of Britter and McQuaid (1988) for estimating dense gas plume length from single leakage source to multiple leakage sources. The method is very fast and therefore amenable to general system-level GCS risk assessment including uncertainty quantification within the framework of the National Risk Assessment Partnership (NRAP) Integrated Assessment Model (IAM). The method is conservative in that it assumes the wind could be from any direction, and it handles multiple sources by a simple superposition approach. The method produces results in reasonable agreement with a sophisticated computational fluid dynamics (CFD) code, but runs in a small fraction of the time.

58 GEOSCIENCES↗

DOE OSTI2023

Adaptive, Active Learning, and Multifidelity Monte Carlo Methods in the MOOSE Stochastic Tools Module

MOOSE is an open-source computational platform for constructing multi-physics models and executing them in a massively parallel fashion. It has a stochastic tools module (STM) for forward/inverse uncertainty quantification (UQ) and surrogate modeling. This presentation details some recent developments to the STM with respect to the implementation of adaptive, active learning, and multifidelity Monte Carlo methods for forward UQ of computational models. Specifically, the adaptive Monte Carlo methods include Markov Chain Monte Carlo (MCMC)-driven algorithms like adaptive importance sampling and parallelized subset simulation for statistical QoI estimation, rare events analysis, and stochastic gradient-free optimization. The active learning methods include Gaussian Process (GP) surrogates and their training via Adam optimization, design of acquisition functions, and integration with samplers like Monte Carlo, adaptive importance, and parallelized subset simulation. These active learning methods are also designed to work in a batch mode, wherein, the required calls to the full computational model are executed in parallel whenever a user-specified batch size is met. The multifidelity methods in STM are broadly divided into two categories: hierarchical, where a defined hierarchy exists among the low-fidelity models, and peer, where all the low-fidelity models are treated equally. A GP surrogate is used to learn the differences between the low- and high-fidelity models in both multifidelity categories, and acquisition functions from the active learning classes are used to decide whether to rely on a low-fidelity model or call the expensive high-fidelity model. Alongside the software description and usage, applications are also presented to nuclear engineering computational models including a TRISO nuclear fuel particle, a reactor pressure vessel, and a heat-pipe microreactor.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Sensitivity and Uncertainty Workflow of Full System SIERRA Models Supporting High Consequence Applications (Executive Summary)

The overall goal of this work was to advance the integrated workflow capabilities to provide the weapon system analysts the tools to construct, communicate and robustly execute end-to-end computational simulation models and analysis. Specifically, this includes developing and running automated, CompSim workflows that map model inputs to responses from large-scale studies executed on a distributed, heterogenous computing environment (e.g., CAD applications on Windows laptop to Sierra FEM codes on Trinity within a single workflow). The workflows developed enable ensemble calculations in support of ND program decisions, including sensitivity analysis, optimization and uncertainty quantification.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Extensions to the SENSEI In situ Framework for Heterogeneous Architectures

The proliferation of GPUs and accelerators in recent supercomputing systems, so called heterogeneous architectures, has led to increased complexity in execution environments and programming models as well as to deeper memory hierarchies on these systems. In this work, we discuss challenges that arise in in situ code coupling on these heterogeneous architectures. In particular, we present data and execution model extensions to the SENSEI in situ framework that are targeted at the effective use of systems with heterogeneous architectures. We then use these new data and execution model extensions to investigate several in situ placement and execution configurations and to analyze the impact these choices have on overall performance.

Loring, Burlen↗