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DOE OSTI2017

SNS Cryogenic Test Facility Kinney Vacuum Pump Commissioning and Operation at 2 K

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL) has built and commissioned an independent Cryogenic Test Facility (CTF) in support of testing in the Radio-frequency Test Facility (RFTF). Superconducting Radio-frequency Cavity (SRF) testing was initially conducted with the CTF cold box at 4.5 K. A Kinney vacuum pump skid consisting of a roots blower with a liquid ring backing pump was recently added to the CTF system to provide testing capabilities at 2 K. System design, pump refurbishment and installation of the Kinney pump will be presented. During the commissioning and initial testing period with the Kinney pump, several barriers to achieve reliable operation were experienced. Details of these lessons learned and improvements to skid operations will be presented. Pump capacity data will also be presented.

Degraff, Brian D.↗

DOE OSTI2017

SNS Central Helium Liquefier spare Carbon Bed installation and commissioning

The Spallation Neutron Source (SNS) Central Helium Liquefier (CHL) at Oak Ridge National Laboratory (ORNL) has been without major operations downtime since operations were started back in 2006. This system utilizes a vessel filled with activated carbon as the final major component to remove oil vapor from the compressed helium circuit prior to insertion into the system's cryogenic cold box. The need for a spare carbon bed at SNS due to the variability of carbon media lifetime calculation to adsorption efficiency will be discussed. The fabrication, installation and commissioning of this spare carbon vessel will be presented. The novel plan for connecting the spare carbon vessel piping to the existing infrastructure will be presented.

Degraff, Brian D.↗

DOE OSTI2016

Cosmic Dawn (CoDa): the first radiation-hydrodynamics simulation of reionization and galaxy formation in the Local Universe

Cosmic reionization by starlight from early galaxies affected their evolution, thereby impacting reionization itself. Star formation suppression, for example, may explain the observed underabundance of Local Group dwarfs relative to N-body predictions for cold dark matter. Reionization modelling requires simulating volumes large enough [~ (100 Mpc) 3 ] to sample reionization ‘patchiness’, while resolving millions of galaxy sources above ~10 8 M ⊙ combining gravitational and gas dynamics with radiative transfer. Modelling the Local Group requires initial cosmological density fluctuations pre-selected to form the well-known structures of the Local Universe today. Cosmic Dawn (‘CoDa’) is the first such fully coupled, radiation-hydrodynamics simulation of reionization of the Local Universe. Our new hybrid CPU-GPU code, RAMSES-CUDATON, performs hundreds of radiative transfer and ionization rate-solver timesteps on the GPUs for each hydro-gravity timestep on the CPUs. CoDa simulated (91Mpc) 3 with 4096 3 particles and cells, to redshift 4.23, on ORNL supercomputer Titan, utilizing 8192 cores and 8192 GPUs. Global reionization ended slightly later than observed. However, a simple temporal rescaling which brings the evolution of ionized fraction into agreement with observations also reconciles ionizing flux density, cosmic star formation history, CMB electron scattering optical depth and galaxy UV luminosity function with their observed values. Photoionization heating suppressed the star formation of haloes below ~2 × 109 M ⊙ , decreasing the abundance of faint galaxies around M AB1600 = [-10, -12]. For most of reionization, star formation was dominated by haloes between 10 10 –10 11 M ⊙ , so low-mass halo suppression was not reflected by a distinct feature in the global star formation history. Intergalactic filaments display sheathed structures, with hot envelopes surrounding cooler cores, but do not self-shield, unlike regions denser than 100 $\langle$ρ$\rangle$.

Astronomy & Astrophysics↗

DOE OSTI2017

Comparison of Monoenergetic Photon Organ Dose Rate Coefficients for the Female Stylized and Voxel Phantoms Submerged in Air

Dose rate coefficients computed using the International Commission on Radiological Protection (ICRP) reference adult female voxel phantom were compared with values computed using the Oak Ridge National Laboratory (ORNL) adult female stylized phantom in an air submersion exposure geometry. This is a continuation of previous work comparing monoenergetic organ dose rate coefficients for the male adult phantoms. With both the male and female data computed, effective dose rate as defined by ICRP Publication 103 was compared for both phantoms. Organ dose rate coefficients for the female phantom and ratios of organ dose rates for the voxel and stylized phantoms are provided in the energy range from 30 to 5 MeV. Analysis of the contribution of the organs to effective dose is also provided. Lastly, comparison of effective dose rates between the voxel and stylized phantoms was within 8% at 100 keV and is <5% between 200 and 5000 keV.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2018

Dynamics of Organophosphate‐Induced Structural Changes in Acetylcholinesterase Revealed by Time‐Resolved Small‐Angle X‐Ray Scattering and Inelastic Neutron Scattering

Organophosphates (OPs), the active compounds in nerve agents and insecticides such as paraoxon (POX), exert their toxic effects by irreversibly inhibiting acetylcholinesterase (AChE). Currently available antidotes for OP toxicity can reactivate AChE, but reactivation rates are slow which can lead to irreversible nervous system damage or death. We hypothesize that better antidotes with accelerated reactivation rates can be developed by understanding the structural dynamics of OP‐AChE conjugates. Previous steady‐state small‐angle X‐ray scattering (SAXS) experiments showed that POX and certain other OPs induce quaternary structural changes in human AChE (hAChE) that were reversible by oxime antidotes. To characterize the dynamics of these OP‐induced structural changes we have used time‐resolved SAXS (TR‐SAXS) and inelastic neutron scattering (INS). Stopped‐flow TR‐SAXS experiments were performed at BL‐4 at the Stanford Synchrotron Radiation Lightsource by mixing a molar excess of POX (500 μM) with hAChE (50 μM). Scattering data were collected every 20 msec during the first 1000 msec after mixing. Changes in the solution structure of hAChE during this period occurred with k obs = 0.13(±0.01) s −1 . This rate of structural change is comparable to the rate of hAChE inactivation observed using biochemical assays. INS data were collected at the Cold Neutron Chopper Spectrometer at the Spallation Neutron Source at Oakridge National Laboratory using 100 mg samples of hAChE and POX‐hAChE that were lyophilized and rehydrated in D 2 O. INS data collected at 120 K (to prevent translational motions) were used to compare the vibrational density states of native and POX‐conjugated hAChE. The POX‐conjugated hAChE exhibited an increased number of vibrational states, particularly those associated with regional motions such as loop dynamics, whereas global collective motions remained unchanged. Molecular dynamics simulations were used to simulate the vibrational spectra and identify the region(s) in hAChE that contributed to the differences in the INS experimental data. In aggregate, these studies provide experimental evidence for OP conjugate‐induced changes in hAChE dynamics that has to be considered in the design of antidotes for OP poisoning that have improved efficacy and patient outcomes. Support or Funding Information This work was supported by the CounterACT Program, National Institutes of Health Office of the Director, Grant 1U01NS083451 from NINDS, and by grants to SSRL and ORNL from the U.S. Department of Energy, and the National Institutes of Health. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Blumenthal, Donald K.↗

DOE OSTI2022

β decay of neutron-rich 76 Cu and the structure of 76 Zn

In this work, the β decay of 76 Cu to the levels of 76 Zn was studied at the Holifield Radioactive Ion Beam Facility (HRIBF) at Oak Ridge National Lab (ORNL). A purified 76 Cu beam was developed and data were recorded for the decay of the A=76 decay chain using four high-purity germanium (HPGe) clover detectors at the Low-energy Radioactive Ion Beam Spectroscopy Station (LeRIBSS). In this measurement, data on γ-ray emission following β decay, including βγ and γγ coincidences, were collected and γγ spectra were analyzed to identify the statistically significant coincidences. From this analysis, we propose a level scheme for 76 Zn which contains a total of 59 energy levels up to 6.0 MeV containing 105 γ rays. We have identified an additional 53 γ rays associated with this decay which could not be place in the decay scheme due to insufficient coincidence information or no energy match to identified levels. No γ rays from states in 75 Zn fed in the delayed-neutron branch were observed even though other γ rays in the A=75 decay chain were observed. Spin and parity assignments are proposed for some levels based on comparison to systematics and shell model calculations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

A 20-kW DC Oak Ridge Converter with Integrated AC & DC Sources for Grid Services and Energy Storage Systems

This paper proposes a wireless power transfer (WPT) platform with integrated energy conversion that has the capability for 1) recharging the energy storage systems (ESSs) from the grid systems, including renewable energy sources such as wind, solar, etc., 2) off grid systems recharging the ESSs from dc grid systems, 3) grid recharging of electric vehicles (EVs), and 4) off grid recharging of EVs from ESSs. The unique aspect of the method is the use of multi-interface power electronic converter for the grid and ESSs and EVs that can support a range of applications with ac / dc and dc / dc energy conversion ability in a single converter system. The key enabling technology to achieve these functionalities is Oak Ridge Converter (ORC) with polyphase coupler coil system both developed at ORNL. This new technology enables higher power density WPT systems while allowing the coils to interface from ac grid at 60 Hz frequency or dc source directly merging with 85 kHz operating frequency of switching component. The experimental results of the proposed system are presented for 20 kW output power with the system overall efficiency around 95.4% from dc source and 93% overall efficiency from ac grid achieving 9-10% current total harmonic distortion (THD) and 0.98-0.99 power factor (PF).

Asa, Erdem↗

DOE OSTI2015

Use of a metadata documentation and search tool for large data volumes: The NGEE arctic example

The Online Metadata Editor (OME) is a web-based tool to help document scientific data in a well-structured, popular scientific metadata format. In this paper, we will discuss the newest tool that Oak Ridge National Laboratory (ORNL) has developed to generate, edit, and manage metadata and how it is helping data-intensive science centers and projects, such as the U.S. Department of Energy’s Next Generation Ecosystem Experiments (NGEE) in the Arctic to prepare metadata and make their big data produce big science and lead to new discoveries.

Devarakonda, Ranjeet↗

DOE OSTI2016

Distributed and cloud-based multi-model analytics experiments on large volumes of climate change data in the earth system grid federation eco-system

A case study on climate models intercomparison data analysis addressing several classes of multi-model experiments is being implemented in the context of the EU H2020 INDIGO-DataCloud project. Such experiments require the availability of large amount of data (multi-terabyte order) related to the output of several climate models simulations as well as the exploitation of scientific data management tools for large-scale data analytics. More specifically, the paper discusses in detail a use case on precipitation trend analysis in terms of requirements, architectural design solution, and infrastructural implementation. The experiment has been tested and validated on CMIP5 datasets, in the context of a large scale distributed testbed across EU and US involving three ESGF sites (LLNL, ORNL, and CMCC) and one central orchestrator site (PSNC).

Fiore, S.↗

DOE OSTI2016

Landrush: Rethinking In-Situ Analysis for GPGPU Workflows

In-situ analysis on the output data of scientific simulations has been made necessary by ever-growing output data volumes and increasing costs of data movement as supercomputing is moving towards exascale. With hardware accelerators like GPUs becoming increasingly common in high end machines, new opportunities arise to co-locate scientific simulations and online analysis performed on the scientific data generated by the simulations. However, the asynchronous nature of GPGPU programming models and the limited context-switching capabilities on the GPU pose challenges to co-locating the scientific simulation and analysis on the same GPU. This paper dives deeper into these challenges to understand how best to co-locate analysis with scientific simulations on the GPUs in HPC clusters. Specifically, our 'Landrush' approach to GPU sharing proposes a solution that utilizes idle cycles on the GPU to provide an improved time-to-answer, that is, the total time to run the scientific simulation and analysis of the generated data. Landrush is demonstrated with experimental results obtained from leadership high-end applications on ORNL's Titan supercomputer, which show that (i) GPU-based scientific simulations have varying degrees of idle cycles to afford useful analysis task co-location, and (ii) the inability to context switch on the GPU at instruction granularity can be overcome by careful control of the analysis kernel launches and software-controlled early completion of analysis kernel executions. Results show that Landrush is superior in terms of time-to-answer compared to serially running simulations followed by analysis or by relying on the GPU driver and hardwired thread dispatcher to run analysis concurrently on a single GPU.

Computer Science↗

DOE OSTI2016

DISP: Optimizations towards Scalable MPI Startup

Despite the popularity of MPI for high performance computing, the startup of MPI programs faces a scalability challenge as both the execution time and memory consumption increase drastically at scale. We have examined this problem using the collective modules of Cheetah and Tuned in Open MPI as representative implementations. Previous improvements for collectives have focused on algorithmic advances and hardware off-load. In this paper, we examine the startup cost of the collective module within a communicator and explore various techniques to improve its efficiency and scalability. Accordingly, we have developed a new scalable startup scheme with three internal techniques, namely Delayed Initialization, Module Sharing and Prediction-based Topology Setup (DISP). Our DISP scheme greatly benefits the collective initialization of the Cheetah module. At the same time, it helps boost the performance of non-collective initialization in the Tuned module. We evaluate the performance of our implementation on Titan supercomputer at ORNL with up to 4096 processes. The results show that our delayed initialization can speed up the startup of Tuned and Cheetah by an average of 32.0% and 29.2%, respectively, our module sharing can reduce the memory consumption of Tuned and Cheetah by up to 24.1% and 83.5%, respectively, and our prediction-based topology setup can speed up the startup of Cheetah by up to 80%.

Fu, Huansong↗

DOE OSTI2018

Detecting outliers in streaming time series data from ARM distributed sensors

The Atmospheric Radiation Measurement (ARM) Data Center at ORNL collects data from a number of permanent and mobile facilities around the globe. The data is then ingested to create high level scientific products. High frequency streaming measurements from sensors and radar instruments at ARM sites require high degree of accuracy to enable rigorous study of atmospheric processes. Outliers in collected data are common due to instrument failure or extreme weather events. Thus, it is critical to identify and flag them. We employed multiple univariate, multivariate and time series techniques for outlier detection methods and studied their effectiveness. First, we examined Pearson correlation coefficient which is used to measure the pairwise correlations between variables. Singular Spectrum Analysis (SSA) was applied to detect outliers by removing the anticipated annual and seasonal cycles from the signal to accentuate anomalies. K-means was applied for multivariate examination of data from collection of sensors to identify any deviation from expected and known patterns and identify abnormal observation. The Pearson correlation coefficient, SSA and K-means methods were later combined together in a framework to detect outliers through a range of checks. We applied the developed method to data from meteorological sensors at ARM Southern Great Plains site and validated against existing database of known data quality issues.

Lu, Yuping↗

DOE OSTI2016

Evaluating Online Global Recovery with Fenix Using Application-Aware In-Memory Checkpointing Techniques

Exascale systems promise the potential for computation at unprecedented scales and resolutions, but achieving exascale by the end of this decade presents significant challenges. A key challenge is due to the very large number of cores and components and the resulting mean time between failures (MTBF) in the order of hours or minutes. Since the typical run times of target scientific applications are longer than this MTBF, fault tolerance techniques will be essential. An important class of failures that must be addressed is process or node failures. While checkpoint/restart (C/R) is currently the most widely accepted technique for addressing processor failures, coordinated, stable-storage-based global C/R might be unfeasible at exascale when the time to checkpoint exceeds the expected MTBF. This paper explores transparent recovery via implicitly coordinated, diskless, application-driven checkpointing as a way to tolerate process failures in MPI applications at exascale. The discussed approach leverages User Level Failure Mitigation (ULFM), which is being proposed as an MPI extension to allow applications to create policies for tolerating process failures. Specifically, this paper demonstrates how different implementations of application-driven in-memory checkpoint storage and recovery compare in terms of performance and scalability. We also experimentally evaluate the effectiveness and scalability of the Fenix online global recovery framework on a production system -the Titan Cray XK7 at ORNL- and demonstrate the ability of Fenix to tolerate dynamically injected failures using the execution of four benchmarks and mini-applications with different behaviors.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

TOWARDS RAPID RESPONSE UPDATES OF POPULATIONS AT RISK

Understanding population at risks has been a focus of the LandScan program through its development of population estimates. With advancements in computer vision, deep learning technologies and access to High Performance Computing (HPC) and high resolution imagery, population estimates are now modeled at the building level. However, when those patterns are disrupted, rapid updates to population distribution estimates are needed to support humanitarian aid and response. Oak Ridge National Laboratory (ORNL) recently adapted an existing deep learning building footprint extraction model in development of a scalable approach to Building Damage Assessments (BDA). This new opportunity opens the possibility of automating BDA to support rapid population distribution estimate updates for geographic areas involved in geopolitical conflicts or natural events for humanitarian aid and response or where to focus recovery efforts. In addition, incorporate social surveys to further model human behavior under conflict or other scenarios that disrupt normal patterns of life.

Urban, Marie↗

DOE OSTI2018

Tartan: Evaluating Modern GPU Interconnect via a Multi-GPU Benchmark Suite

In this paper, we fill the gap by proposing a multi-GPU benchmark suite named Tartan, which contains microbenchmarks, scale-up and scale-out applications. We then apply Tartan to evaluate the four latest types of modern GPU interconnects, i.e., PCI- e, NVLink-V1, NVLink-V2 and InfiniBand with GPUDirect- RDMA from two recently released NVIDIA super AI platforms as well as ORNL’s exascale prototype system. Based on empirical evaluation, we observe four new types of NUMA effects: three types are triggered by NVLink’s topology, connectivity and routing, while one type is caused by PCI-e (i.e., anti-locality). They are very important for performance tuning in multi-GPU environment. Our evaluation results show that, unless the current CPU-GPU master-slave programming model can be replaced, it is difficult for scale-up multi-GPU applications to really benefit from faster intra-node interconnects such as NVLinks; while for inter-node scale-out applications, although interconnect is more crucial to the overall performance, GPUDirect-RDMA appears to be not always the optimal choice.

HPC↗

DOE OSTI2016

Immersive Visual Analytics for Transformative Neutron Scattering Science

The ORNL Spallation Neutron Source (SNS) provides the most intense pulsed neutron beams in the world for scientific research and development across a broad range of disciplines. SNS experiments produce large volumes of complex data that are analyzed by scientists with varying degrees of experience using 3D visualization and analysis systems. However, it is notoriously difficult to achieve proficiency with 3D visualizations. Because 3D representations are key to understanding the neutron scattering data, scientists are unable to analyze their data in a timely fashion resulting in inefficient use of the limited and expensive SNS beam time. We believe a more intuitive interface for exploring neutron scattering data can be created by combining immersive virtual reality technology with high performance data analytics and human interaction. In this paper, we present our initial investigations of immersive visualization concepts as well as our vision for an immersive visual analytics framework that could lower the barriers to 3D exploratory data analysis of neutron scattering data at the SNS.

data visualization↗

DOE OSTI2020

Optimizing High Performance Markov Clustering for Pre-Exascale Architectures

HipMCL is a high-performance distributed memory implementation of the popular Markov Cluster Algorithm (MCL) and can cluster large-scale networks within hours using a few thousand CPU-equipped nodes. It relies on sparse matrix computations and heavily makes use of the sparse matrix-sparse matrix multiplication kernel (SpGEMM). The existing parallel algorithms in HipMCL are not scalable to Exascale architectures, both due to their communication costs dominating the runtime at large concurrencies and also due to their inability to take advantage of accelerators that are increasingly popular. In this work, we systematically remove scalability and performance bottlenecks of HipMCL. We enable GPUs by performing the expensive expansion phase of the MCL algorithm on GPU. Additionally, we propose a CPU-GPU joint distributed SpGEMM algorithm called pipelined Sparse SUMMA and integrate a probabilistic memory requirement estimator that is fast and accurate. Furthermore, we develop a new merging algorithm for the incremental processing of partial results produced by the GPUs, which improves the overlap efficiency and the peak memory usage. We also integrate a recent and faster algorithm for performing SpGEMM on CPUs. We validate our new algorithms and optimizations with extensive evaluations. With the enabling of the GPUs and integration of new algorithms, HipMCL is up to 12.4x faster, being able to cluster a network with 70 million proteins and 68 billion connections just under 15 minutes using 1024 nodes of ORNL's Summit supercomputer.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Learning to Scale the Summit: AI for Science on a Leadership Supercomputer

The Summit system at Oak Ridge National Lab-oratory (ORNL) has been the world's top AI for science su-percomputer for several years, ranked world's fastest computer at its 2018 launch and currently top system in the US and #2 on the TOP5OO list. Summit's purposeful design to handle both conventional modeling and simulation science and emerging AI workloads has made it a leading destination for AI-powered computational science. We report here on AI for science usage on Summit near the midpoint of its lifespan. We review AI usage across the many science projects that have used Summit. We then examine in detail a set of applications scaling AI to full system as well as projects implementing AI-coordinated science discovery workflows on Summit. Finally, we offer some observations regarding the future of advancing scientific knowledge and understanding via AI, especially in the context of leadership-class scientific computing.

Joubert, Wayne↗