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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Performance model-directed data sieving for high-performance I/O

Many scientific computing applications and engineering simulations exhibit noncontiguous I/O access patterns. Data sieving is an important technique to improve the performance of noncontiguous I/O accesses by combining small and noncontiguous requests into a large and contiguous request. It has been proven effective even though more data are potentially accessed than demanded. In this study, we propose a new data sieving approach namely performance model-directed data sieving, or PMD data sieving in short. It improves the existing data sieving approach from two aspects: (1) dynamically determines when it is beneficial to perform data sieving; and (2) dynamically determines how to perform data sieving if beneficial. It improves the performance of the existing data sieving approach considerably and reduces the memory consumption as verified by both theoretical analysis and experimental results. Given the importance of supporting noncontiguous accesses effectively and reducing the memory pressure in a large-scale system, the proposed PMD data sieving approach in this research holds a great promise and will have an impact on high-performance I/O systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Lightweight Provenance Service for High-Performance Computing

Provenance describes detailed information about the history of a piece of data, containing the relationships among elements such as users, processes, jobs, and workflows that contribute to the existence of data. Provenance is key to supporting many data management functionalities that are increasingly important in operations such as identifying data sources, parameters, or assumptions behind a given result; auditing data usage; or understanding details about how inputs are transformed into outputs. Despite its importance, however, provenance support is largely underdeveloped in highly parallel architectures and systems. One major challenge is the demanding requirements of providing provenance service in situ. The need to remain lightweight and to be always on often conflicts with the need to be transparent and offer an accurate catalog of details regarding the applications and systems. To tackle this challenge, we introduce a lightweight provenance service, called LPS, for high-performance computing (HPC) systems. LPS leverages a kernel instrument mechanism to achieve transparency and introduces representative execution and flexible granularity to capture comprehensive provenance with controllable overhead. Extensive evaluations and use cases have confirmed its efficiency and usability. We believe that LPS can be integrated into current and future HPC systems to support a variety of data management needs.

Dai, Dong↗
DOE OSTI2014

Calculation of exact vibrational spectra for P{sub 2}O and CH{sub 2}NH using a phase space wavelet basis

‘‘Exact” quantum dynamics calculations of vibrational spectra are performed for two molecular systems of widely varying dimensionality (P{sub 2}O and CH{sub 2}NH), using a momentum-symmetrized Gaussian basis. This basis has been previously shown to defeat exponential scaling of computational cost with system dimensionality. The calculations were performed using the new “SWITCHBLADE” black-box code, which utilizes both dimensionally independent algorithms and massive parallelization to compute very large numbers of eigenstates for any fourth-order force field potential, in a single calculation. For both molecules considered here, many thousands of vibrationally excited states were computed, to at least an “intermediate” level of accuracy (tens of wavenumbers). Future modifications to increase the accuracy to “spectroscopic” levels, along with other potential future improvements of the new code, are also discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Neural network–based pore flow field prediction in porous media using super resolution

Direct pore-scale simulations of fluid flow through porous media are computationally expensive to perform for realistic systems. Previous works have demonstrated using the geometry of the microstructure of porous media to predict the velocity fields therein based on neural networks. However, such trained neural networks do not perform well for unseen porous media with a large degree of heterogeneity. In this study we propose that incorporating a coarse velocity field in the input of neural networks is an effective way to improve the prediction performance. The coarse velocity field can be simulated with a low computational cost and provides global information to regularize the ill-posedness of the learning problem, which is usually caused by the use of local geometries due to the computational resource constraints. We show that incorporating the coarse-mesh velocity field significantly improves the prediction accuracy of the fine-mesh velocity field by comparison to the prediction that relies on geometric information alone, especially for the porous medium with a large interior vuggy pore space. We also show the flexibility of training the network in using coarse velocity fields with various resolutions. The results suggest that even using coarse velocity field with a very low resolution, the predictions are still enhanced and close to the ground truths. The feasibility of the method is further demonstrated by testing the trained network on real rocks. This study highlights the merits of incorporating a coarse-mesh velocity field into the input for neural networks, which provides global, physics-based information for the model, thereby improving the model's generalization capability.

42 ENGINEERING↗
DOE OSTI2023

COLLABORATIVE DEVELOPMENT PROJECTS - PHOTONIC MEMORY CONTROLLER MODULE (P-MCM)

As computational density for high-performance computing and big-data services continues to scale, performance scalability of next generation computing systems is becoming increasingly constrained by limitations in memory access, power dissipation and chip packaging. The processor-memory communication bottleneck, a major challenge in current multicore processors due to limited pin-out and power budget, presents a detrimental scaling barrier to data-intensive computing. A consortium team of small businesses and leading researchers that includes experts from photonics processor-memory architecture, III/V photonic laser design/fabrication, silicon photonics design/fabrication, photonics packaging and assembly, and FPGA-based high-performance memory controller IP development – to collaboratively develop a commercialization path for a Photonic Memory Controller Module (P-MCM).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

The CLAS12 Data Acquisition System

The CLAS12 Data Acquisition System was designed and built as part of the CLAS12 detector project in Hall B at Jefferson Laboratory. This article contains a full description of the system, including requirements, design, hardware, and software descriptions, as well as the achieved performance. The associated computing, network, and slow controls systems are also described.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Toward improving drought monitoring using the remotely sensed soil moisture assimilation: A parallel particle filtering framework

Drought is the costliest hazard among all natural disasters. Despite the significant improvements in drought modeling over the last decade, accurate provisions of drought conditions in a timely manner is still one of the major research challenges. In order to improve the current drought monitoring skills, this study presents a land data assimilation system by merging the remotely sensed surface soil moisture with the use of recently developed particle Markov chain Monte Carlo (PMCMC) method. To cope with the computational complexity, a modular parallel particle filtering framework (PPFF) is developed which allows a large ensemble size in PMCMC applications. The implementation of the proposed system is demonstrated with the 2012 summer flash drought case study over the Contiguous United States (CONUS). Results from both synthetic and real case studies suggest that the land data assimilation system significantly improves the soil moisture predictions and the drought monitoring skills. Compared with the U.S. Drought Monitoring (USDM), the land data assimilation can better capture the drought onset on May 2012 and the drought severity in June and July 2012. Finally, this study recommends that the proposed land data assimilation system under a high-performance computing (HPC) infrastructure can better facilitate the drought preparation and response actions.

42 ENGINEERING↗
DOE OSTI2020

Software Framework for Federated Science Instruments

There is an unprecedented promise of enhanced capabilities for federations of leadership computing systems and experimental science facilities by leveraging software technologies for fast and efficient operations. These federations seek to unify different science instruments, both computing and experimental, to effectively support science users and operators to execute complex workflows. The FedScI project addresses the software challenges associated with the formation and operation of federated environments by leveraging recent advances in containerization of software and softwarization of hardware. We propose a software framework to streamline the federation usage by science users and it’s provisioning and operations by facility providers. A distinguishing element of our work is the support for improved interaction between experimental devices, such as beam-line instruments, and more traditional high-performance computing resources, including compute, network, storage systems. We present guiding principles for the software framework and highlight portions of a current prototype implementation. We describe our science use case involving neutron imaging beam-lines (SNAP/BL-3, Imaging/CG-1D) at the Spallation Neutron Source and High Flux Isotope Reactor facilities at Oak Ridge National Laboratory. Additionally, we detail plans for a more direct instrument interaction within a federated environment, which could enable more advanced workflows with feedback loops to shorten the time to science.

Naughton III, Thomas↗
DOE OSTI2017

High-Throughput Computing on High-Performance Platforms: A Case Study

The computing systems used by LHC experiments has historically consisted of the federation of hundreds to thousands of distributed resources, ranging from small to mid-size resource. In spite of the impressive scale of the existing distributed computing solutions, the federation of small to mid-size resources will be insufficient to meet projected future demands. This paper is a case study of how the ATLAS experiment has embraced Titan -- a DOE leadership facility in conjunction with traditional distributed high- throughput computing to reach sustained production scales of approximately 52M core-hours a years. The three main contributions of this paper are: (i) a critical evaluation of design and operational considerations to support the sustained, scalable and production usage of Titan; (ii) a preliminary characterization of a next generation executor for PanDA to support new workloads and advanced execution modes; and (iii) early lessons for how current and future experimental and observational systems can be integrated with production supercomputers and other platforms in a general and extensible manner.

Oleynik, D↗
DOE OSTI2019

A parallel workflow implementation for PEST version 13.6 in high-performance computing for WRF-Hydro version 5.0: a case study over the midwestern United States

The Weather Research and Forecasting Hydrological (WRF-Hydro) system is a state-of-the-art numerical model that models the entire hydrological cycle based on physical principles. As with other hydrological models, WRF-Hydro parameterizes many physical processes. Hence, WRF-Hydro needs to be calibrated to optimize its output with respect to observations for the application region. When applied to a relatively large domain, both WRF-Hydro simulations and calibrations require intensive computing resources and are best performed on multimode, multicore high-performance computing (HPC) systems. Typically, each physics-based model requires a calibration process that works specifically with that model and is not transferrable to a different process or model. The parameter estimation tool (PEST) is a flexible and generic calibration tool that can be used in principle to calibrate any of these models. In its existing configuration, however, PEST is not designed to work on the current generation of massively parallel HPC clusters. To address this issue, we ported the parallel PEST to HPCs and adapted it to work with WRF-Hydro. The porting involved writing scripts to modify the workflow for different workload managers and job schedulers, as well as to connect the parallel PEST to WRF-Hydro. To test the operational feasibility and the computational benefits of this first-of-its-kind HPC-enabled parallel PEST, we developed a case study using a flood in the midwestern United States in 2013. Results on a problem involving the calibration of 22 parameters show that on the same computing resources used for parallel WRF-Hydro, the HPC-enabled parallel PEST can speed up the calibration process by a factor of up to 15 compared with commonly used PEST in sequential mode. The speedup factor is expected to be greater with a larger calibration problem (e.g., more parameters to be calibrated or a larger size of study area).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Application configuration selection for energy-efficient execution on multicore systems

Balanced performance and energy consumption are incorporated in the design of modern computer systems. Several runtime factors, such as concurrency levels, thread mapping strategies, and dynamic voltage and frequency scaling (DVFS) should be considered in order to achieve optimal energy efficiency fora workload. Selecting appropriate run-time factors, however, is one of the most challenging tasks because the run-time factors are architecture-specific and workload-specific. And while most existing works concentrate on either static analysis of the workload or run-time prediction results, we present a hybrid two-step method that utilizes concurrency levels and DVFS settings to achieve the energy efficiency configuration for a worldoad. The experimental results based on a Xeon E5620 server with NPB and PARSEC benchmark suites show that the model is able to predict the energy efficient configuration accurately. On average, an additional 10% EDP (Energy Delay Product) saving is obtained by using run-time DVFS for the entire system. An off-line optimal solution is used to compare with the proposed scheme. Finally, the experimental results show that the average extra EDP saved by the optimal solution is within 5% on selective parallel benchmarks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Extreme Heterogeneity 2018 - Productive Computational Science in the Era of Extreme Heterogeneity: Report for DOE ASCR Workshop on Extreme Heterogeneity

The 2018 Basic Research Needs Workshop on Extreme Heterogeneity identified five Priority Research Directions for realizing the capabilities needed to address the challenges posed in this era of rapid technological change. This report captures the outcomes of that workshop. In the context of extreme heterogeneity, it defines basic research needs and opportunities in computer science research to develop smart and trainable operating and runtime systems, programming environments, and predictive tools that will make future systems easier to adapt to scientists’ computing needs and easier for facilities to deploy securely.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Simulating long-time evolution of driven many-body systems with next generation quantum computers (Final Report)

This project is focused on performing the best science possible on current generation quantum computers. As such, we have focused on developing techniques that involve robust algorithms that are either insensitive to noise, or are extremely low depth. We also have worked on determining topological properties and employing the robustness of topology to noise to allow for robust quantum computation. The ultimate goal is to be able to do new science on quantum computers that cannot be achieved on classical computers. The main effort of the work we have completed is on time-evolution of quantum systems and robust calculation of properties of systems enabled by being able to perform time evolution.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Toward A High-Performance Emulation Platform for Brain-Inspired Intelligent Systems Exploring Dataflow-Based Execution Model and Beyond

Brain-inspired computing is a novel computing technology based on neural morphological engineering, which draws lessons from methods of human brain information processing and storage. Combining with the high-performance computing (HPC) platform, they constitute the foundation of general artificial intelligence. However, current brain HPC platforms generally suffer from slow speed, poor scalability, and high energy consumption, which severely restrain its potential and circumscribe the development of general artificial intelligence. The dataflow model was first proposed in the 1970s, providing a novel idea for the development of HPC. In addition, the dataflow model shares similar information processing mechanisms with human's neural system, which makes dataflow models naturally suit the emulation of brain-inspired computing. Based on the contemporary progress of the dataflow model, the Codelet model was proposed. Through a fine-grained asynchronous program execution and resource allocation, the Codelet model successfully realized the distributed computing on the heterogeneous system, effectively improved the computing power and speed, and open up a new path to overcome the shortcomings of the existing high-performance computing technology. We propose a dataflow-based emulation platform, aiming at providing high-performance computing technology support for general brain-inspired intelligent system, as well as using characteristics of dataflow models to fully explore the potential of brain-inspired intelligence. As an example, we will select a convolutional neural network (LeNet5) that already has a spectacular user base to initially verify the superiority and feasibility of our proposal.

brain-inspired intelligent system↗
DOE OSTI2021

DFTTK: Density Functional Theory ToolKit for high-throughput lattice dynamics calculations

In this work, we present a software package in Python for high-throughput first-principles calculations of thermodynamic properties at finite temperatures, which we refer to as DFTTK (Density Functional Theory ToolKit). DFTTK is based on the atomate package and integrates our experiences in the last decades on the development of theoretical methods and computational softwares. It includes task submissions on all major operating systems and task executions on high-performance computing environments. Furthermore, the distribution of the DFTTK package comes with examples of calculations of phonon density of states, heat capacity, entropy, enthalpy, and free energy under the quasi-harmonic phonon scheme for the stoichiometric phases of Al, Ni, Al 3 Ni, AlNi, AlNi 3 , Al 3 Ni 4 , and Al 3 Ni 5 , and the fcc solution phases treated using the special quasirandom structures at the compositions of Al 3 Ni, AlNi, and AlNi 3 .

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Effective Exploration of New 760°C-Capability Steels for Coal Energy

Cost effective and high performance alloys that are capable of operating at 760 °C or higher for extended periods of time under a very aggressive environment are critically required for the design and development of advanced ultrasupercritical (AUSC) boilers and steam turbines. Finely dispersed Laves phase precipitates have been shown by Takeyama and co-workers to be a viable strengthening mechanism in high temperature austenitic steels. There is currently no straightforward theory that can predict what other intermetallic phases can serve as potent precipitation-strengthening phases for steels; thus we employed a highly effective dual-anneal diffusion multiple (DADM) approach to screen for viable strengthening precipitates over a wide range of compositions. From the Fe-Co-Cr-Ni-Mo DADMs, the Fe-Cr-Mo based Chi phase was identified as a new strengthening phase for high temperature ferritic steels; and from the Fe-Mn-Cr-Nb-Ni-Mo-FeAl DADMs, the Laves phase was identified as a viable strengthening precipitate in Fe-Mn and Fe-Ni based austenitic steels. After identification of viable strengthening phases from the DADMs that covered compositions in the basic ternary and quaternary systems, we employed computation thermodynamics to perform multicomponent alloy design and optimization. For the new the Chi-phase strengthened steels, we performed thermodynamic calculations to vary the volume fraction of the Chi phase and introduced Nb and carbon to promote the formation of stable carbides for grain size control during solution heat treatment. For the Fe-Ni-Mn based austenitic steels, we performed extensive parametric optimization of compositions in order to reduce the expensive Ni content, add Cr and Al for oxidation resistance, and balance the alloying contents (Ni, Mn, Cr, Al, Mo) to suppress the ferritic phase and promote the austenitic matrix phase. Four steels (two ferritic + two austenitic) were designed and tested. The two Chi-phase strengthened ferritic steels exhibited excellent oxidation resistance and good creep-rupture strength at moderate temperatures, considering their ferritic matrix that usually results in lower creep resistance than austenitic steels. These steels showed brittleness and sample-to-sample variability in ductility. The low ductility might be due to the macro segregation during solidification or the significant grain growth during the solution heat treatments. We believe there is no inherent brittleness based on the chemistry of the steels. The creep-rupture performance of the steels is comparable to the 9Cr steels. Due to their ferritic matrix, the new Chi-phase strengthened ferritic steels may not be suited for the 760 °C AUSC applications, but they are very good candidates for intermediate temperature applications due to their outstanding oxidation resistance and high strength. Further study is required to find the source of low and highly variable ductility. We believe the compositions of the Chi-phase strengthened steels are not inherently brittle. The Chi-phase strengthened ferritic steels may also be excellent candidates for intermediate-temperature and room-temperature cast stainless steels, thus we highly recommend further investigations. The two Mn-containing austenitic steels based on the Laves phase showed good ductility, excellent oxidation resistance (slightly inferior to the two ferritic steels) at high temperatures and moderate creep strength. The creep-strength of the two austenitic steels based on the Larson-Miller parameters is higher than that of the traditional 316 stainless steels, but lower than the alumina-forming alloys (AFAs) developed at Oak Ridge National Laboratories. We do not recommend high priority in further studying these compositions unless higher Cr alloys are required for hot-corrosion resistance.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2014

Parallel computation safety analysis irradiation targets fission product molybdenum in neutronic aspect using the successive over-relaxation algorithm

One of the research activities in support of commercial radioisotope production program is a safety research on target FPM (Fission Product Molybdenum) irradiation. FPM targets form a tube made of stainless steel which contains nuclear-grade high-enrichment uranium. The FPM irradiation tube is intended to obtain fission products. Fission materials such as Mo{sup 99} used widely the form of kits in the medical world. The neutronics problem is solved using first-order perturbation theory derived from the diffusion equation for four groups. In contrast, Mo isotopes have longer half-lives, about 3 days (66 hours), so the delivery of radioisotopes to consumer centers and storage is possible though still limited. The production of this isotope potentially gives significant economic value. The criticality and flux in multigroup diffusion model was calculated for various irradiation positions and uranium contents. This model involves complex computation, with large and sparse matrix system. Several parallel algorithms have been developed for the sparse and large matrix solution. In this paper, a successive over-relaxation (SOR) algorithm was implemented for the calculation of reactivity coefficients which can be done in parallel. Previous works performed reactivity calculations serially with Gauss-Seidel iteratives. The parallel method can be used to solve multigroup diffusion equation system and calculate the criticality and reactivity coefficients. In this research a computer code was developed to exploit parallel processing to perform reactivity calculations which were to be used in safety analysis. The parallel processing in the multicore computer system allows the calculation to be performed more quickly. This code was applied for the safety limits calculation of irradiated FPM targets containing highly enriched uranium. The results of calculations neutron show that for uranium contents of 1.7676 g and 6.1866 g (× 10{sup 6} cm{sup −1}) in a tube, their delta reactivities are the still within safety limits; however, for 7.9542 g and 8.838 g (× 10{sup 6} cm{sup −1}) the limits were exceeded.

07 ISOTOPE AND RADIATION SOURCES↗