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At least 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Development and Demonstration of Material Properties Database and Software for the Simulation of Flow Properties in Cementitious Materials

This report describes work performed by the Savannah River National Laboratory (SRNL) in fiscal year 2014 to develop a new Cementitious Barriers Project (CBP) software module designated as FLOExcel. FLOExcel incorporates a uniform database to capture material characterization data and a GoldSim model to define flow properties for both intact and fractured cementitious materials and estimate Darcy velocity based on specified hydraulic head gradient and matric tension. The software module includes hydraulic parameters for intact cementitious and granular materials in the database and a standalone GoldSim framework to manipulate the data. The database will be updated with new data as it comes available. The software module will later be integrated into the next release of the CBP Toolbox, Version 3.0. This report documents the development efforts for this software module. The FY14 activities described in this report focused on the following two items that form the FLOExcel package; 1) Development of a uniform database to capture CBP data for cementitious materials. In particular, the inclusion and use of hydraulic properties of the materials are emphasized; and 2) Development of algorithms and a GoldSim User Interface to calculate hydraulic flow properties of degraded and fractured cementitious materials. Hydraulic properties are required in a simulation of flow through cementitious materials such as Saltstone, waste tank fill grout, and concrete barriers. At SRNL these simulations have been performed using the PORFLOW code as part of Performance Assessments for salt waste disposal and waste tank closure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

eXascale PRogramming Environment and System Software (XPRESS)

Exascale systems, with a thousand times the compute capacity of today’s leading edge petascale computers, are expected to emerge during the next decade. Their software systems will need to facilitate the exploitation of exceptional amounts of concurrency in applications, and ensure that jobs continue to run despite the occurrence of system failures and other kinds of hard and soft errors. Adapting computations at runtime to cope with changes in the execution environment, as well as to improve power and performance characteristics, is likely to become the norm. As a result, considerable innovation is required to develop system support to meet the needs of future computing platforms. The XPRESS project aims to develop and prototype a revolutionary software system for extreme-­scale computing for both exascale and strong­scaled problems. The XPRESS collaborative research project will advance the state-­of-­the-­art in high performance computing and enable exascale computing for current and future DOE mission-­critical applications and supporting systems. The goals of the XPRESS research project are to: A. enable exascale performance capability for DOE applications, both current and future, B. develop and deliver a practical computing system software X-­stack, OpenX, for future practical DOE exascale computing systems, and C. provide programming methods and environments for effective means of expressing application and system software for portable exascale system execution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Optimizing the Performance of Radionuclide Identification Software in the Hunt for Nuclear Security Threats

The Radionuclide Analysis Kit (RNAK), my team’s most recent nuclide identification software, is entering the testing phase. A question arises: will removing rare nuclides from the software’s library improve its overall performance? An affirmative response indicates fundamental errors in the software’s framework, while a negative response confirms the effectiveness of the software’s key machine learning algorithms. After thorough testing, I found that the performance of RNAK cannot be improved with the library choice effect, thus verifying the effectiveness of RNAK’s algorithms—multiple linear regression, Bayesian network using the Viterbi algorithm, and branch and bound search.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Summary of Documentation for DYNA3D-ParaDyn's Software Quality Assurance Regression Test Problems

The Software Quality Assurance (SQA) regression test suite for DYNA3D (Zywicz and Lin, 2015) and ParaDyn (DeGroot, et al., 2015) currently contains approximately 600 problems divided into 21 suites, and is a required component of ParaDyn’s SQA plan (Ferencz and Oliver, 2013). The regression suite allows developers to ensure that software modifications do not unintentionally alter the code response. The entire regression suite is run prior to permanently incorporating any software modification or addition. When code modifications alter test problem results, the specific cause must be determined and fully understood before the software changes and revised test answers can be incorporated. The regression suite is executed on LLNL platforms using a Python script and an associated data file. The user specifies the DYNA3D or ParaDyn executable, number of processors to use, test problems to run, and other options to the script. The data file details how each problem and its answer extraction scripts are executed. For each problem in the regression suite there exists an input deck, an eight-processor partition file, an answer file, and various extraction scripts. These scripts assemble a temporary answer file in a specific format from the simulation results. The temporary and stored answer files are compared to a specific level of numerical precision, and when differences are detected the test problem is flagged as failed. Presently, numerical results are stored and compared to 16 digits. At this accuracy level different processor types, compilers, number of partitions, etc. impact the results to various degrees. Thus, for consistency purposes the regression suite is run with ParaDyn using 8 processors on machines with a specific processor type (currently the Intel Xeon E5530 processor). For non-parallel regression problems, i.e., the two XFEM problems, DYNA3D is used instead. When environments or platforms change, executables using the current source code and the new resource are created and the regression suite is run. If differences in answers arise, the new answers are retained provided that the differences are inconsequential. This bootstrap approach allows the test suite answers to evolve in a controlled manner with a high level of confidence. Developers also run the entire regression suite with (serial) DYNA3D. While these results normally differ from the stored (parallel) answers, abnormal termination or wildly different values are strong indicators of potential issues.

42 ENGINEERING↗
DOE OSTI2017

Simulation-based Testing of Control Software

It is impossible to adequately test complex software by examining its operation in a physical prototype of the system monitored. Adequate test coverage can require millions of test cases, and the cost of equipment prototypes combined with the real-time constraints of testing with them makes it infeasible to sample more than a small number of these tests. Model based testing seeks to avoid this problem by allowing for large numbers of relatively inexpensive virtual prototypes that operate in simulation time at a speed limited only by the available computing resources. In this report, we describe how a computer system emulator can be used as part of a model based testing environment; specifically, we show that a complete software stack including operating system and application software - can be deployed within a simulated environment, and that these simulations can proceed as fast as possible. To illustrate this approach to model based testing, we describe how it is being used to test several building control systems that act to coordinate air conditioning loads for the purpose of reducing peak demand. These tests involve the use of ADEVS (A Discrete Event System Simulator) and QEMU (Quick Emulator) to host the operational software within the simulation, and a building model developed with the MODELICA programming language using Buildings Library and packaged as an FMU (Functional Mock-up Unit) that serves as the virtual test environment.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

PIPER: Performance Insight for Programmers and Exascale Runtimes: Guiding the Development of the Exascale Software Stack

The PIPER project set out to develop methodologies and software for measurement, analysis, attribution, and presentation of performance data for extreme-scale systems. Goals of the project were to support analysis of massive multi-scale parallelism, heterogeneous architectures, multi-faceted performance concerns, and to support both post-mortem performance analysis to identify program features that contribute to problematic performance and on-line performance analysis to drive adaptation. This final report summarizes the research and development activity at Rice University as part of the PIPER project. Producing a complete suite of performance tools for exascale platforms during the course of this project was impossible since both hardware and software for exascale systems is still a moving target. For that reason, the project focused broadly on the development of new techniques for measurement and analysis of performance on modern parallel architectures, enhancements to HPCToolkit’s software infrastructure to support our research goals or use on sophisticated applications, engaging developers of multithreaded runtimes to explore how support for tools should be integrated into their designs, engaging operating system developers with feature requests for enhanced monitoring support, engaging vendors with requests that they add hardware measure- ment capabilities and software interfaces needed by tools as they design new components of HPC platforms including processors, accelerators and networks, and finally collaborations with partners interested in using HPCToolkit to analyze and tune scalable parallel applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Agile Software Components and Libraries for Large-Scale Reservoir Simulations

The objective of this project was for Sandia National Laboratories (SNL) and ExxonMobil Research Company (URC) to collaborate on the development and integration of numerical algorithms and software components for the solution of partial differential equations (PDEs) that address the high fidelity simulation of reservoir flows. SNL’s role was to consult and advise URC on the best practices and use of SNL opensource software components as part of URC's work to incorporate these components into their PDE-based URC reservoir simulation code. A desired outcome was for URC to leverage SNL’s open-source software components as within their own codes, rather than to have to develop from scratch those same capabilities themselves, and for SNL to harden and mature those software components based on usage and feedback from URC.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

XPRESS: eXascale PRogramming Environment and System Software

The XPRESS project is the research and development of a complete software architecture for Exascale computing. The four major R&D thrusts include: 1) an Exascale lightweight kernel operating system (LXK), based on the Kitten operating system (OS), to manage billion way hardware parallelism, management of faults and power, management of global virtual name space, and other features of future system architectures; 2) a runtime system (HPX-5), co-designed with LXK, based on the ParalleX execution model that supports dynamic resource management and task scheduling; 3) system interfaces for interoperability between the runtime system and both the OS and the runtime system (RIOS); 4) compilation strategies and systems to translate MPI and OpenMP legacy codes to a form that can be run with performance at least as good as a native code implementation. The software architecture development has been driven by performance evaluation of mini-applications developed at Department of Energy Co-Design Centers as well as ancillary in-house developed applications and benchmarks. Research on the ParalleX execution model has been driven by the development of formal semantics and model checkers for the execution model. The major components of the software architecture are: ParalleX execution model – guiding principles for co-design of components of the system stack with advances in locality management and task prioritization through introspection; HPX runtime system – support of application dynamic adaptive resource management, task scheduling, and introspective control policies; LXK operating system – lightweight kernel operating system for order-constant scalability and low/no noise to manage resources; RIOS – a realm of Exascale system operation unique in the XPRESS project that includes the relationship between the new generation of lightweight kernel operating systems and runtime system software; XPI advanced programming model – intermediate form and low-level (readable) programming interface reflecting the ParalleX model, providing a target for source-to- source high level parallel language translation, and supporting early direct programming experimentation and measurement; Performance models & metrics – providing parameters and their mutual sensitivities to guide co-design and quantify operational behavior; Legacy migration – ensuring seamless transition of legacy codes and programming methods to the future generation of ParalleX-based Exascale systems; . Experiments and evaluation – critical to determining degree of effectiveness and likelihood of ultimate success as well as guiding corrective design changes to achieve DOE objectives; and Applications – collaborations with Co-Design Centers and other mission critical codes. The exemplar implementation of the ParalleX execution model, HPX-5, has been released along with five fully ported applications (LULESH, HPCG, SSSP, WAMR, and DASHMM) and multiple microbenchmarks. Using these applications, the system architecture demonstrated dynamic load balancing through active migration, legacy code migration, and data-driven approaches to for graph-like applications. Crucially for Exascale and billion way parallelism, the needs and patterns of irregular applications in HPX-5 were addressed using local flow control rather than relying on globally synchronized data structures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

CRADA: Data Acquisition and Tomographic Software Development

Dr. Raymond R. Raylman, Professor and Vice Chair for Research, Radiology Department at WVU was the Principal Investigator for the project. The Jefferson Lab partner on this project was the Radiation Detector and Imaging Group in the Physics Division, led by Dr. Drew Weisenberger who was the coordinator of the project execution and the chief contact with WVU. The responsibility of Jefferson Lab was to facilitate optimization of data acquisition software, conversion of JLab Fortran iterative positron emission mammography computer code to C++ and to act as a computer code repository. During the two year project interactions and exchange of information was directed by Dr. Raylman and Dr. Wesienberger. WVU provided the expertise that allowed JLab to continue development of the current JLab data acquisition software referred to as JDAQ. JDAQ is a highly flexible Java language based data acquisition tool that helps to minimize software induced dead-time while maintaining the highest possible data rates from a JLab built FPGA based analog-to-digital convertor (ADC). JDAQ interfaces directly with the FPGA based multi-channel ADCs. Using JDAQ it is possible process data from a number of different detector types such as the PET systems at WVU. Because of the inherent flexibility of our processing software, we are also able adjust detector readout parameters during operation to provide the best possible data presentation and calibration parameters. To accommodate the high rate nature of PET detector systems JDAQ was designed to be scalable across an Ethernet network as well as for multi-cpu computer systems, and it also has the capability to synchronize many ADCs connected to multiple computers. During the project WVU will provided iterative reconstruction algorithm expertise and Fortran programming assistance to facilitate the conversion to C++ of a JLab developed Fortran positron emission mammography (PEM) reconstruction code. Also WVU provided acceptance testing of the new C++ based PEM reconstruction code. The PEM code is a maximization (MLEM) image reconstruction program that created images from the list-mode data acquired from the PET system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Workshop on Software Productivity for Extreme-scale Science, January 13-14, 2014, Hilton Hotel, Rockville, MD

While emerging extreme-scale computing systems provide unprecedented resources for scientific discovery, DOE’s scientific communities now face a crisis in extreme-scale scientific software productivity due to a powerful confluence of factors: ongoing disruptive changes in computer architectures and new frontiers in modeling, simulation, and analysis of complex multiscale and multiphysics phenomena. Research in mission-critical science and energy problems has become increasingly dependent on high-performance computing, but productivity in extreme-scale scientific application development has not kept pace. The principal issues are (1) a significant lag between extreme-scale hardware and algorithmic innovations and their effective use in applications, leading to poorly scaling codes; (2) obstacles in the combined use of independently developed software components; (3) lack of agile yet rigorous software engineering practices for high-performance computing; and (4) failure to consider the entire lifecycle of large scientific software efforts, leading to fragile, complicated application codes that are increasingly difficult to enhance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

A Software Defined Storage Approach to Exascale Storage Services (Final Technical Report)

The Mochi project began in 2015 in recognition of a set of changes that were set to alter the landscape of storage and I/O for DOE science. On the one hand, an influx of new classes of applications including an initial set of data intensive and machine learning applications were operating with new types of data and in new ways, as compared to traditional computational science simulation codes. Simultaneously, new memory, storage, and networking technologies were driving a need to adapt software designs to a rapidly changing design space. The goal of the three-year Mochi project was to develop and demonstrate an approach for composing storage services which provides new levels of functionality, performance, and reliability for science applications at extreme scale. The team, including a mix of Laboratory, university, and industry collaborators, focused on systems and applications of interest to DOE Office of Science, tracking developments at NERSC and the LCFs as well as application trends within ECP and SciDAC. Technical work focused on three themes: composition (understand the building blocks needed to provide storage services that meet DOE mission needs and how these components are flexibly composed), application interfacing (what service compositions are most effective for common DOE application use cases), and consistency and synchronization (taking advantage of knowledge about, and from, the system and scientific applications to provide better service than would otherwise be possible). The project was highly successful, producing 13 publications and tens of thousands of lines of open source software that is now being used by multiple ECP teams, HPC software vendors, and international collaborators. The uptake of Mochi technologies speaks to both the value of the service composition model and the quality of architecture and software developed by the team. Through continued funding under ASCR and ECP, the Mochi team and its technologies are on track to further contribute to DOE science successes into the exascale timeframe.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Joint Analysis of Program Data Representations using Machine Learning for Improved Software Assurance and Development Capabilities

We explore the use of multiple deep learning models for detecting flaws in software programs. Current, standard approaches for flaw detection rely on a single representation of a software program (e.g., source code or a program binary). We illustrate that, by using techniques from multimodal deep learning, we can simultaneously leverage multiple representations of software programs to improve flaw detection over single representation analyses. Specifically, we adapt three deep learning models from the multimodal learning literature for use in flaw detection and demonstrate how these models outperform traditional deep learning models. We present results on detecting software flaws using the Juliet Test Suite and Linux Kernel.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

TChem v2.0 - A Software Toolkit for the Analysis of Complex Kinetic Models

TChem is an open source software library for solving complex computational chemistry problems and analyzing detailed chemical kinetic models. The software provides support for: complex kinetic models for gas-phase and surface chemistry; thermodynamic properties based on NASA polynomials; species production/consumption rates; stable time integrator for solving stiff time ordinary differential equations; and, reactor models such as homogenous gas-phase ignition (with analytical Jacobian matrices), continuously stirred tank reactor, plug-flow reactor. This toolkit builds upon earlier versions that were written in C and featured tools for gas-phase chemistry only. The current version of the software was completely refactored in C++, uses an object-oriented programming model, and adopts Kokkos as its portability layer to make it ready for the next generation computing architectures i.e., multi/many core computing platforms with GPU accelerators. We have expanded the range of kinetic models to include surface chemistry and have added examples pertaining to Continuously Stirred Tank Reactors (CSTR) and Plug Flow Reactor (PFR) models to complement the homogenous ignition examples present in the earlier versions. To exploit the massive parallelism available from modern computing platforms, the current software interface is designed to evaluate samples in parallel, which enables large scale parametric studies, e.g. for sensitivity analysis and model calibration.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

The ERSDT Software Quality Assurance Plan Practices and Development Environment Setup for RASCAL 5

This document includes the information pertaining to the practices of the Emergency Response Software Development Team (ERSDT) for the RASCAL software project. The content includes information regarding: 1) Software Quality Assurance. 2) Project Management. 3) Configuration Management. 4) Development Standards. 5) Third-Party Software. 6) Verification and Validation. The information contained in this report is considered living documentation. The information contained in this report was assembled from multiple documents and content management systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

An Evaluation of the ASM2000 software

The ASM2000 software is based on the NetCAM3 software (currently installed at RLUOB) with the main difference being enhanced multi-CAM head support. Therefore, as the NetCAM3 software has been previously tested, the current evaluation concentrated on performance related to multi-head operation. In addition, bug fixes and improvements to prior versions of the NetCAM3 code were incorporated into the ASM2000 V2.0.0 software package.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Uncertainty Analysis and Software Verification

Uncertainty analyses are an important part of calibrations and testing. They allow researchers insight on how to reduce and mitigate errors in testing. Each device used in testing introduces error in a system, as well as other sources such as environmental conditions, electronics, analog to digital, and random errors. Each source is carefully examined to identify how much error it introduces to a system. These sources are then combined using various methods of uncertainty calculations. To verify and validate software, a manual calculation is required to ensure the software is performing as intended. Using Excel to verify the calculations, we can identify discrepancies within the software. The root of the sum of the squares uncertainty (RSS) is used to find the combined uncertainty of a device at one and two standard deviations of the mean. Calibrations on accelerometers are performed using a vibration system along with a back-toback reference accelerometer. The vibration system takes a reference point at 100Hz frequency at 10g amplitude. The sensitivities are collected at each dialed in frequency. The sensitivity of the device represents the electrical output of the UUT (mV, pC, etc.) per unit of acceleration (g). The full history of sensitivities of selected accelerometers are used to find the averaged, standard deviation, and uncertainty of the device at each frequency tested. The uncertainty calculations from excel and the software are then compared.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Seismic Source Modeling Software Enhancements (FY21)

Seismic source modeling allows researchers both to simulate how a source that induces seismic waves interacts with the Earth to produce observed seismograms and, inversely, to infer what the time histories, sizes, and force distributions were for a seismic source given observed seismograms. In this report, we discuss improvements made in FY21 to our software as applies to both the forward and inverse seismic source modeling problems. For the forward portion of the problem, we have added the ability to use full 3-D nonlinear simulations by implementing 3-D time varying boundary conditions within Sandia’s linear seismic code Parelasti. Secondly, on the inverse source modeling side, we have developed software that allows us to invert seismic gradiometer-derived observations in conjunction with standard translational motion seismic data to infer properties of the source that may improve characterization in certain circumstances. First, we describe the basic theory behind each software enhancement and then demonstrate the software in action with some simple examples.

58 GEOSCIENCES↗
DOE OSTI2019

Performance tuning of CEED software and 1st and 2nd wave apps

The goal of this milestone was the performance tuning of the CEED software, as well as the use and tuning of CEED to accelerate the first and second wave of targeted ECP applications. In this milestone, the CEED team developed optimization techniques and tuned for performance the CEED software to accelerate the first and second wave target ECP applications. Specifically, the focus was on the following: (1) Efficient use of the memory sub-system for managing data motion and interactions among different physics packages and libraries; this included integration of the new developments in a libCEED 0.5 software release; (2) Enhancing the CEED libraries with GPU and AMD GPU support in close interaction with vendors; (3) Optimal data locality and motion, and enhanced scalability and parallelism; this included vendors interactions for improvements in data motion and making strong scaling easier and more efficient; and (4) Continue boosting performance for the first-wave and second-wave of ECP target applications, including ExaSMR, ExaWind, Urban, MARBL, E3SM, and ExaAM. A main part of this milestone was the performance tuning of the CEED first-wave and second-wave ECP applications. This included the ExaSMR application – Coupled Monte Carlo Neutronics and Fluid Flow Simulation of Small Modular Reactors (ORNL), the MARBL application – Next-Gen Multi-physics Simulation Code (LLNL), and the ExaAM ExaConstit – a miniapp for the Transforming Additive Manufacturing through Exascale Simulation applications project (ExaAM), as well as ExaWind, Urban, and E3SM. The artifacts delivered include performance improvements in CEED’s 1st and 2nd wave of applications, and tuned CEED software for various architectures through a number of backends, freely available in the CEED’s repository on GitHub. See the CEED website, http://ceed.exascaleproject.org and the CEED GitHub organization, http://github.com/ceed for more details. In addition to details and results from the above R&D efforts, in this document we are also reporting on other project-wide activities performed in Q4 of FY19 including: CEED’s third annual meeting, MFEM GPU kernels scaling improvements, libCEED 0.5 and NekRS releases, an overview paper covering latest results on a set of bake-off high-order finite element problems (BPs), and other outreach efforts.

97 MATHEMATICS AND COMPUTING↗