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At least 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
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↗
NASA NTRS1994

Equation solvers for distributed-memory computers

A large number of scientific and engineering problems require the rapid solution of large systems of simultaneous equations. The performance of parallel computers in this area now dwarfs traditional vector computers by nearly an order of magnitude. This talk describes the major issues involved in parallel equation solvers with particular emphasis on the Intel Paragon, IBM SP-1 and SP-2 processors.

Storaasli, Olaf O.↗
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↗
NASA NTRS1989

Response of gravity level fluctuations on the Gravity Probe-B spacecraft propellant system

Time-dependent computations have been performed to investigate the dynamical behaviors of fluid under a microgravity environment. A computer algorithm is introduced which can be used to simulate the fluid behavior in that environment, in particular the excitation of sloshing waves due to different gravity environments and rotation speeds. A suggestion on the proper handling and managing of cryogenic fluid propellant to be used in the Gravity Probe-B spacecraft propulsion is made.

Hung, R. J.↗
NASA NTRS1987

Spacecraft attitude determination using a second-order nonlinear filter

The stringent attitude determination accuracy and faster slew maneuver requirements demanded by present-day spacecraft control systems motivate the development of recursive nonlinear filters for attitude estimation. This paper presents the second-order filter development for the estimation of attitude quaternion using three-axis gyro and star tracker measurement data. Performance comparisons have been made by computer simulation of system models and filter mechanization. It is shown that the second-order filter consistently performs better than the extended Kalman filter when the performance index of the root sum square estimation error of the quaternion vector is compared. The second-order filter identifies the gyro drift rates faster than the extended Kalman filter. The uniqueness of this algorithm is the online generation of the time-varying process and measurement noise covariance matrices, derived as a function or the process and measurement nonlinearity, respectively.

Vathsal, S.↗
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↗
NASA NTRS1990

ISIS and META projects

The ISIS project has developed a new methodology, virtual synchony, for writing robust distributed software. High performance multicast, large scale applications, and wide area networks are the focus of interest. Several interesting applications that exploit the strengths of ISIS, including an NFS-compatible replicated file system, are being developed. The META project is distributed control in a soft real-time environment incorporating feedback. This domain encompasses examples as diverse as monitoring inventory and consumption on a factory floor, and performing load-balancing on a distributed computing system. One of the first uses of META is for distributed application management: the tasks of configuring a distributed program, dynamically adapting to failures, and monitoring its performance. Recent progress and current plans are reported.

Birman, Kenneth↗
DOE OSTI2021

Performance Analysis of PIConGPU: Particle-in-Cell on GPUs using NVIDIA’s NSight Systems and NSight Compute

PIConGPU, Particle In Cell on GPUs, is an open source simulations framework for plasma and laser-plasma physics used to develop advanced particle accelerators for radiation therapy of cancer, high energy physics and photon science. While PIConGPU has been optimized for at least 5 years to run well on NVIDIA GPU-based clusters, there has been limited exploration by the development team of potential scalability bottlenecks using recently updated and new tools including NVIDIA’s NVProf tool and the brand-new NVIDIA NSight Suite (Systems and Compute) tools. PIConGPU is a highly optimized application that runs production jobs at scale on a system Oak Ridge Leadership Facility’s (OLCF) Summit supercomputer (using the full machine at 4600 nodes; at 98% of GPU utilization on all ~28000 NVIDIA Volta GPUs). PIConGPU has been selected as one of the the eight applications for OLCF’s coveted Center for Accelerated Application Readiness (CAAR) program aimed at the facility’s Frontier supercomputer (OLCF’s first exascale system to launch in 2021), to partner with our vendors (primary vendors: AMD and Cray/HPE) ensuring that Frontier will be able to perform large-scale science when it opens to users in 2022. To this effect, performance engineers on the PIConGPU team wanted to dive deep into the application to understand at the finest granularity, which portions of the code could be further optimized to exploit the hardware on Summit at it’s maximum potential and also to elucidate which key kernels should be tracked and optimized for the CAAR effort to port this code to Frontier. Any bottlenecks that are observed via performance profiling on Summit are likely to also impact scalability on the Frontier-dev system and the Frontier Early Access (EA) system. Additionally, the engineers wanted to take a closer look at the newest NVIDIA profiling tools which allows us to identify the most useful features on these tools and will provide an opportunity to compare it to new AMD and Cray’s performance analysis tool releases and provide feedback to our vendor partners on what features are most important and mission critical for CAAR efforts. The primary goal of this report is to focus on the evaluation of PIConGPU’s most time-intensive kernels using NVProf and NSight Suite. Three kernels, Current Deposition (also known as Compute Current), Particle Push (Move and Mark), and Shift Particles are known to be some of the most time-consuming kernels in PIConGPU. The Current xi Deposition kernel and Particle Push kernel both set up the particle attributes for running any physics simulation with PIConGPU, so it is crucial to improve the performance of these two kernels. In this report, we measure single GPU metrics for the three kernels, offer high level takeaways from the conducted analysis, and compare the profiling data from NSight Compute to that of NVProf. This analysis was performed using a grid size of 240 x 272 x 224, and 10 time steps with the Mid-November Figure of Merit (FOM) run setup. The Traveling Wave Electron Acceleration (TWEAC) science case used in this run is a representative science case for PIConGPU. This execution can also be used for baseline analysis on AMD MI50/ MI60 systems. As of the time of writing, the PIConGPU application has limited use for features of NSight Systems, so this report will mainly focus on insights garnered from NSight Compute. For this analysis, we run the “full” metric set available in NSight Compute version 2020.1.2 and use NSight Systems version 2020.3.1 to generate the application timeline.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗