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DOE OSTI2020

Solving Coupled Cluster Equations by the Newton Krylov Method

We describe using the Newton Krylov method to solve the coupled cluster equation. The method uses a Krylov iterative method to compute the Newton correction to the approximate coupled cluster amplitude. The multiplication of the Jacobian with a vector, which is required in each step of a Krylov iterative method such as the Generalized Minimum Residual (GMRES) method, is carried out through a finite difference approximation, and requires an additional residual evaluation. The overall cost of the method is determined by the sum of the inner Krylov and outer Newton iterations. We discuss the termination criterion used for the inner iteration and show how to apply pre-conditioners to accelerate convergence. We will also examine the use of regularization technique to improve the stability of convergence and compare the method with the widely used direct inversion of iterative subspace (DIIS) methods through numerical examples.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Criticality Evaluation of Low Density Product Oxides for Air Transport for the ES-3100 Package

This document presents the criticality safety evaluation of the Y-12 National Security Complex (Y-12) Model ES-3100 package for the addition of lower bulk density limits for HEU product oxides transported by air. The current authorized oxide density range for air transport is 2.0 to 6.54 g/cm 3 . The analysis in this report looks at lower densities, specifically in the range of 1.0 to 2.0 g/cm 3 . For reasons presented in this report, it was decided to present the entire range of oxide densities (1.0 to 6.54 g/cm 3 ) as a series of sub-ranges. The lowest density in each range was evaluated. Thus calculations were performed for the following oxide densities: 5.0 g/cm 3 , 4.0 g/cm 3 , 3.0 g/cm 3 , 2.0 g/cm 3 , 1.5 g/cm 3 and 1.0 g/cm 3 . Calculations presented herein were performed using a third generation computing platform; the Microway Parallel Linux Cluster ENGHPC workstation using the Monte Carlo Criticality Safety Analysis Sequence (CSAS5) criticality code sequence and 238-group ENDF/B-VII cross-section library of Standardized Computer Analysis for Licensing Evaluation (SCALE) 6.1.3 (ORNL/TM-2005/39, SCALE: A Comprehensive Modeling and Simulation Suite for Nuclear Safety Analysis and Design).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network

Abstract Single-cell RNA sequencing (scRNA-seq) permits researchers to study the complex mechanisms of cell heterogeneity and diversity. Unsupervised clustering is of central importance for the analysis of the scRNA-seq data, as it can be used to identify putative cell types. However, due to noise impacts, high dimensionality and pervasive dropout events, clustering analysis of scRNA-seq data remains a computational challenge. Here, we propose a new deep structural clustering method for scRNA-seq data, named scDSC, which integrate the structural information into deep clustering of single cells. The proposed scDSC consists of a Zero-Inflated Negative Binomial (ZINB) model-based autoencoder, a graph neural network (GNN) module and a mutual-supervised module. To learn the data representation from the sparse and zero-inflated scRNA-seq data, we add a ZINB model to the basic autoencoder. The GNN module is introduced to capture the structural information among cells. By joining the ZINB-based autoencoder with the GNN module, the model transfers the data representation learned by autoencoder to the corresponding GNN layer. Furthermore, we adopt a mutual supervised strategy to unify these two different deep neural architectures and to guide the clustering task. Extensive experimental results on six real scRNA-seq datasets demonstrate that scDSC outperforms state-of-the-art methods in terms of clustering accuracy and scalability. Our method scDSC is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/DHUDBlab/scDSC.

Gan, Yanglan↗

DOE OSTI2023

Coupled-cluster theory for strong entanglement in nuclei

Atomic nuclei can exhibit shape coexistence and multireference physics that enters in their ground states, and accurately capturing the ensuing correlations and entanglement is challenging. Here, we address this problem by applying single-reference coupled-cluster theory based on spherical and deformed reference states and the tailored coupled-cluster method. The latter combines configuration interactions to capture static correlations with coupled-cluster theory for dynamic correlations. We compute the atomic nuclei 12 C, 28 Si, and 56 Ni and find that the tailored coupled-cluster method and the single-reference approach based on a deformed Hartree-Fock state yield the most accurate results.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2019

Evaluating the Effects of Missing Values and Mixed Data Types on Social Sequence Clustering Using t-SNE Visualization

© 2019 Association for Computing Machinery. The goal of this work is to investigate the impact of missing values in clustering joint categorical social sequences. Identifying patterns in sociodemographic longitudinal data is important in a number of social science settings. However, performing analytical operations, such as clustering on life course trajectories, is challenging due to the categorical and multidimensional nature of the data, their mixed data types, and corruption by missing and inconsistent values. Data quality issues were investigated previously on single variable sequences. To understand their effects on multivariate sequence analysis, we employ a dataset of mixed data types and missing values, a dissimilarity measure designed for joint categorical sequence data, together with dimensionality reduction methodologies in a systematic design of sequence clustering experiments. Given the categorical nature of our data, we employ an “edit” distance using optimal matching. Because each data record has multiple variables of different types, we investigate the impact of mixing these variables in a single dissimilarity measure. Between variables with binary values and those with multiple nominal values, we find that the ability to overcome missing data problems is more difficult in the nominal domain than in the binary domain. Additionally, alignment of leading missing values can result in systematic biases in dissimilarity matrices and subsequently introduce both artificial clusters and unrealistic interpretations of associated data domains. We demonstrate the usage of t-distributed stochastic neighborhood embedding to visually guide mitigation of such biases by tuning the missing value substitution cost parameter or determining an optimal sequence span.

Lazar, Alina↗

DOE OSTI2014

Solution of relativistic quantum optics problems using clusters of graphical processing units

Numerical solution of relativistic quantum optics problems requires high performance computing due to the rapid oscillations in a relativistic wavefunction. Clusters of graphical processing units are used to accelerate the computation of a time dependent relativistic wavefunction in an arbitrary external potential. The stationary states in a Coulomb potential and uniform magnetic field are determined analytically and numerically, so that they can used as initial conditions in fully time dependent calculations. Relativistic energy levels in extreme magnetic fields are recovered as a means of validation. The relativistic ionization rate is computed for an ion illuminated by a laser field near the usual barrier suppression threshold, and the ionizing wavefunction is displayed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2017

XRootD popularity on hadoop clusters

Performance data and metadata of the computing operations at the CMS experiment are collected through a distributed monitoring infrastructure, currently relying on a traditional Oracle database system. This paper shows how to harness Big Data architectures in order to improve the throughput and the efficiency of such monitoring. A large set of operational data - user activities, job submissions, resources, file transfers, site efficiencies, software releases, network traffic, machine logs - is being injected into a readily available Hadoop cluster, via several data streamers. The collected metadata is further organized running fast arbitrary queries; this offers the ability to test several Map&Reduce-based frameworks and measure the system speed-up when compared to the original database infrastructure. By leveraging a quality Hadoop data store and enabling an analytics framework on top, it is possible to design a mining platform to predict dataset popularity and discover patterns and correlations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Quantum computation solves a half-century-old enigma: Elusive vibrational states of magnesium dimer found

The high-lying vibrational states of the magnesium dimer (Mg 2 ), which has been recognized as an important system in studies of ultracold and collisional phenomena, have eluded experimental characterization for half a century. Until now, only the first 14 vibrational states of Mg 2 have been experimentally resolved, although it has been suggested that the ground-state potential may support five additional levels. Here, we present highly accurate ab initio potential energy curves based on state-of-the-art coupled-cluster and full configuration interaction computations for the ground and excited electronic states involved in the experimental investigations of Mg 2 . Our ground-state potential unambiguously confirms the existence of 19 vibrational levels, with ~1 cm -1 root mean square deviation between the calculated rovibrational term values and the available experimental and experimentally derived data. Our computations reproduce the latest laser-induced fluorescence spectrum and provide guidance for the experimental detection of the previously unresolved vibrational levels.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2014

Power API Prototype

The software serves two purposes. The first purpose of the software is to prototype the Sandia High Performance Computing Power Application Programming Interface Specification effort. The specification can be found at http://powerapi.sandia.gov . Prototypes of the specification were developed in parallel with the development of the specification. Release of the prototype will be instructive to anyone who intends to implement the specification. More specifically, our vendor collaborators will benefit from the availability of the prototype. The second is in direct support of the PowerInsight power measurement device, which was co-developed with Penguin Computing. The software provides a cluster wide measurement capability enabled by the PowerInsight device. The software can be used by anyone who purchases a PowerInsight device. The software will allow the user to easily collect power and energy information of a node that is instrumented with PowerInsight. The software can also be used as an example prototype implementation of the High Performance Computing Power Application Programming Interface Specification.

Laros III, James↗

DOE OSTI2014

Power API Prototype

Source record↗

DOE OSTI2015

Accurate transport properties for H–CO and H–CO{sub 2}

Transport properties for collisions of hydrogen atoms with CO and CO{sub 2} have been computed by means of quantum scattering calculations. The carbon oxides are important species in hydrocarbon combustion. The following potential energy surfaces (PES’s) for the interaction of the molecule fixed in its equilibrium geometry were employed: for H–CO, the PES was taken from the work of Song et al. [J. Phys. Chem. A 117, 7571 (2013)], while the PES for H–CO{sub 2} was computed in this study by a restricted coupled cluster method that included single, double, and (perturbatively) triple excitations. The computed transport properties were found to be significantly different from those computed by the conventional approach that employs isotropic Lennard-Jones (12-6) potentials. The effect of using the presently computed accurate transport properties in 1-dimensional combustion simulations of methane-air flames was investigated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Structure and Synthesizability of Iron–Sulfur Metal–Organic Frameworks

Sulfur-based metal–organic frameworks (MOFs) and coordination polymers (CPs) are an emerging class of hybrid materials that have received growing attention due to their magnetic, conductive, and catalytic properties with potential applications in electrocatalysis and energy storage. In this work, we report a high-throughput virtual screening protocol to predict the synthesizability of candidate metal–sulfur MOFs/CPs by computing the thermodynamically stable structures resulting from a particular combination of metal cluster, linker, cation, and synthetic conditions. Free energies are computed by using all-atom classical mechanical thermodynamic integration. Low-free-energy structures are refined using ab initio density functional theory, and pair distribution functions and powder X-ray diffraction patterns are calculated to complement and guide experimental structure determination. We validate the computational approach by retrospective predictions of the stable structure produced by experimental syntheses, and a subsequent screen predicts Fe 4 S 4 -BDT–TPP as a new thermodynamically stable one-dimensional (1D) CP comprising a redox-active Fe 4 S 4 cluster, a 1,4-benzenedithiolate (BDT) linker, and a tetraphenylphosphonium (TPP) countercation. Furthermore, this material is experimentally synthesized, and the 1D chain structure of the crystal is confirmed using microcrystal electron diffraction. The computational screening pipeline is generically transferable to neutral and ionic MOFs/CPs comprising arbitrary metal clusters, linkers, cations, and synthetic conditions, and we make it freely available as an open source tool to guide and accelerate the discovery and engineering of novel porous materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2014

Interactions of mobile helium clusters with surfaces and grain boundaries of plasma-exposed tungsten

We report results of atomistic computations for the interactions of small mobile helium clusters (He{sub n}) with free surfaces and grain boundaries (GBs) in tungsten toward development of continuum drift-diffusion-reaction models for the dynamics of mobile helium clusters in plasma-exposed tungsten. Molecular-statics (MS) simulations based on reliable many-body interatomic potentials are carried out for He{sub n} (1 ≤ n ≤ 7) clusters near sinks to obtain the potential energy profiles of the He{sub n} clusters as a function of the clusters' center-of-mass distance from a sink. Sinks investigated include surfaces, GBs, and regions in the vicinity of junctions where GBs intersect free surfaces. Elastic interaction potentials based on elastic inclusion theory provide an excellent description of the MS results for the cluster-sink interactions. The key parameter in the elastic models is the sink segregation strength, which is found to increase with increasing cluster size. Such cluster-sink interactions are responsible for the migration of small helium clusters by drift and for helium segregation on surfaces and grain boundaries in tungsten. Such helium segregation on sinks is observed in large-scale molecular-dynamics simulations of helium aggregation in model polycrystalline tungsten at 933 K upon helium implantation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2015

Understanding GPU errors on large-scale HPC systems and the implications for system design and operation

Increase in graphics hardware performance and improvements in programmability has enabled GPUs to evolve from a graphics-specific accelerator to a general-purpose computing device. Titan, the world's second fastest supercomputer for open science in 2014, consists of more dum 18,000 GPUs that scientists from various domains such as astrophysics, fusion, climate, and combustion use routinely to run large-scale simulations. Unfortunately, while the performance efficiency of GPUs is well understood, their resilience characteristics in a large-scale computing system have not been fully evaluated. We present a detailed study to provide a thorough understanding of GPU errors on a large-scale GPU-enabled system. Our data was collected from the Titan supercomputer at the Oak Ridge Leadership Computing Facility and a GPU cluster at the Los Alamos National Laboratory. We also present results from our extensive neutron-beam tests, conducted at Los Alamos Neutron Science Center (LANSCE) and at ISIS (Rutherford Appleron Laboratories, UK), to measure the resilience of different generations of GPUs. We present several findings from our field data and neutron-beam experiments, and discuss the implications of our results for future GPU architects, current and future HPC computing facilities, and researchers focusing on GPU resilience.

Tiwari, Devesh↗

DOE OSTI2024

Performance Analysis of Data Processing in Distributed File Systems with Near Data Processing

In the era of big data, the escalating volume and velocity of data generation pose significant challenges in data processing. Traditional systems like Spark and Hadoop manage the increasing amount and velocity of data by improving data placement and processing speeds. However, they face inherent limitations due to the essential data movement required for processing. In this paper, we explore the Skyhook framework, a novel extension of the Ceph distributed system, which significantly reduces the need for data movement. We present an extensive case study using the Skyhook framework, applying it with the TPC-H and K-means clustering algorithms. More specifically, we leverage the TPC-H benchmark to distinguish between CPU-intensive and I/O-intensive tasks. We explore the integration of K-means clustering into SQL, coupled with a near-data processing system to offload the computational burden of the K-means clustering algorithm to storage nodes. We conduct a comprehensive performance evaluation of distributed data processing applications across three processing approaches: traditional layout (baseline), optimized layout, and near-data processing. Additionally, we introduce the use of the FIO tool to simulate real-world system workloads, enabling the measurement of performance metrics such as average latency and CPU utilization. Our research is a significant advance in understanding how to optimize data processing systems to meet the demands of the modern data landscape.

Hou, Shiyue↗

DOE OSTI2025

Uncertainty estimation of bifurcated solutions in the Rayleigh–Bénard problem for advanced nuclear reactors applications

Multiphysics models of nuclear reactors frequently comprise nonlinear systems of equations. The nonlinear nature of these models could lead to solution bifurcations, where a small change in a certain parameter, e.g., the thermophysical properties of the coolant, can lead to a sudden change in the system’s behavior. At the point in parameter space where this happens, called a critical point, the Jacobian matrix of the model’s nonlinear operator becomes singular potentially permitting multiple solutions to coexist. In this paper, we perform uncertainty estimation (UE) in a parameter range that includes bifurcated solutions within the context of Rayleigh–Bénard problem. We perform this analysis assuming uncertain temperature difference, and tilt angle for the iterative solution algorithm with a unit Prandtl number (Pr = 1). Also, we perform this analysis under uncertain thermophysical properties for both FLiBe molten salt and liquid sodium as working fluid. We deploy two approaches to compute statistical moments for the resulting distributions of selected flow-field variables. The first approach is the blind computation of the mean and the standard deviation without any consideration of solution bifurcation, while the second approach utilizes k-means clustering to cluster each branch’s solutions together and compute separate statistical moments for each branch. The statistical distributions are obtained by perturbing the selected parameters about nominal values that correspond to a solution on one of the valid branches, and that solution is used as initial guess for the iterative solution algorithm. We found that perturbation of any parameter when its nominal value is close to its critical point always leads to branch jumping, i.e., the iterations converge to a solution on a branch different from the branch of the initial guess. This produces a statistical ensemble comprised of fundamentally different solutions leading to wrong mean values and uncertainty estimates, whereas clustering provides an efficient way to deal with this type of computation. This work is important for developing Gen IV nuclear systems because many of these systems rely on natural convection for cooling especially in accident conditions.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Nowcasting Earthquakes in Southern California With Machine Learning: Bursts, Swarms, and Aftershocks May Be Related to Levels of Regional Tectonic Stress

Abstract Seismic bursts in Southern California are sequences of small earthquakes strongly clustered in space and time and include seismic swarms and aftershock sequences. A readily observable property of these events, the radius of gyration ( R G ), allows us to connect the bursts to the temporal occurrence of the largest M ≥ 7 earthquakes in California since 1984. In the Southern California earthquake catalog, we identify hundreds of these potentially coherent space‐time structures in a region defined by a circle of radius 600 km around Los Angeles. We compute R G for each cluster then filter them to identify those bursts with large numbers of events closely clustered in space, which we call “compact” bursts. Our basic assumption is that these compact bursts reflect the dynamics associated with large earthquakes. Once we have filtered the burst catalog, we apply an exponential moving average to construct a time series for the Southern California region. We observe that the R G of these bursts systematically decreases prior to large earthquakes, in a process that we might term “radial localization.” The R G then rapidly increases during an aftershock sequence, and a new cycle of “radial localization” then begins. These time series display cycles of recharge and discharge reminiscent of seismic stress accumulation and release in the elastic rebound process. The complex burst dynamics we observe are evidently a property of the region as a whole, rather than being associated with individual faults. This new method allows us to improve earthquake nowcasting, which is a technique to evaluate the current state of hazard in a seismically active region.

58 GEOSCIENCES↗

DOE OSTI2013

Final Report: Super Instruction Architecture for Scalable Parallel Computations

The most advanced methods for reliable and accurate computation of the electronic structure of molecular and nano systems are the coupled-cluster techniques. These high-accuracy methods help us to understand, for example, how biological enzymes operate and contribute to the design of new organic explosives. The ACES III software provides a modern, high-performance implementation of these methods optimized for high performance parallel computer systems, ranging from small clusters typical in individual research groups, through larger clusters available in campus and regional computer centers, all the way to high-end petascale systems at national labs, including exploiting GPUs if available. This project enhanced the ACESIII software package and used it to study interesting scientific problems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗