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DOE OSTI2015

Diverging polygon-based modeling (DPBM) of concentrated solar flux distributions

This paper presents an efficient and robust methodology for modeling concentrated solar flux distributions. Compared to ray tracing methods, which provide high accuracy but can be computationally intensive, this approach makes a number of simplifying assumptions in order to reduce complexity by modeling incident and reflected flux as a series of simple geometric diverging polygons, then applying shading and blocking effects. A reduction in processing time (as compared to ray tracing) allows for evaluating and visualizing numerous combinations of engineering and operational variables (easily exceeding 106 unique iterations) to ascertain instantaneous, transient, and annual system performance. The method is demonstrated on a linear Fresnel reflector array and a number of variable iteration examples presented. While some precision is sacrificed for computational speed, flux distributions were compared to ray tracing (SolTrace) and average concentration ratio generally found to agree within ~3%. Furthermore, this method presents a quick and very flexible coarse adjust method for concentrated solar power (CSP) field design, and can be used to both rapidly gain an understanding of system performance as well as to narrow variable constraint windows for follow-on high accuracy system optimization.

14 SOLAR ENERGY↗

DOE OSTI2022

System, method, and computer-accessible medium for remote sensing of the electrical distribution grid with hypertemporal imaging

An exemplary system, method, and computer-accessible medium for determining a property(ies) regarding an electrical grid(s) can be provided, which can include, for example, receiving a video(s) of the electrical grid(s), determining a flicker(s) in the electrical grid(s) based on the video(s), and determining the property(ies) based on the flicker(s). The flicker(s) can be a 120 Hertz flicker. The flicker(s) can be a flicker in a light(s) recorded in the video(s). A frequency and a phase of the flicker(s) can be determined.

Bianco, Federica B.↗

DOE OSTI2011

ON THE PERIOD DISTRIBUTION OF CLUSTER RR LYRAE STARS TO CONSTRAIN THEIR HELIUM CONTENT: THE CASE OF {omega} CENTAURI

We present new sets of nonlinear, time-dependent convective hydrodynamical models of RR Lyrae stars assuming two metal (Z = 0.0005, Z = 0.001) and three helium abundances (Y = 0.24, 0.30, 0.38). For each chemical composition, we constructed a grid of fundamental and first overtone models covering a broad range of stellar masses and luminosities. To constrain the impact of the helium content on RR Lyrae properties, we adopted two observables-period distribution and luminosity amplitudes-that are independent of distance and reddening. The current predictions confirm that the helium content has a marginal effect on the pulsation properties. The key parameter causing the difference between canonical and He-enhanced observables is the luminosity. We compared current predictions with the sample of 189 RR Lyrae stars in {omega} Cen and found that the period range of He-enhanced models is systematically longer than observed. These findings apply to metal-poor and metal-intermediate He-enhanced models. To further constrain the impact of He-enhanced structures on the period distribution, we also computed a series of synthetic horizontal branch (HB) models and found that the predicted period distribution, based on a Gaussian sampling in mass, agrees quite well with observations. This applies not only to the minimum fundamentalized period of RR Lyrae stars (0.39 versus 0.34 days) but also to the fraction of Type II Cepheids (2% versus 3%). We also computed a series of synthetic HB models assuming a mixed HB population in which the 80% is made of canonical HB structures, while the 20% is made of He-enhanced (Y = 0.30) HB structures. We found that the fraction of Type II Cepheids predicted by these models is almost a factor of two larger than observed (5% versus 3%). This indicates that the fraction of He-enhanced structures in {omega} Cen cannot be larger than 20%.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Use of DAGMan in CRAB3 to improve the splitting of CMS user jobs

CRAB3 is a workload management tool used by CMS physicists to analyze data acquired by the Compact Muon Solenoid (CMS) detector at the CERN Large Hadron Collider (LHC). Research in high energy physics often requires the analysis of large collections of files, referred to as datasets. The task is divided into jobs that are distributed among a large collection of worker nodes throughout the Worldwide LHC Computing Grid (WLCG). Splitting a large analysis task into optimally sized jobs is critical to efficient use of distributed computing resources. Jobs that are too big will have excessive runtimes and will not distribute the work across all of the available nodes. However, splitting the project into a large number of very small jobs is also inefficient, as each job creates additional overhead which increases load on infrastructure resources. Currently this splitting is done manually, using parameters provided by the user. However the resources needed for each job are difficult to predict because of frequent variations in the performance of the user code and the content of the input dataset. As a result, dividing a task into jobs by hand is difficult and often suboptimal. In this work we present a new feature called “automatic splitting” which removes the need for users to manually specify job splitting parameters. We discuss how HTCondor DAGMan can be used to build dynamic Directed Acyclic Graphs (DAGs) to optimize the performance of large CMS analysis jobs on the Grid. We use DAGMan to dynamically generate interconnected DAGs that estimate the processing time the user code will require to analyze each event. This is used to calculate an estimate of the total processing time per job, and a set of analysis jobs are run using this estimate as a specified time limit. Some jobs may not finish within the alloted time; they are terminated at the time limit, and the unfinished data is regrouped into smaller jobs and resubmitted.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2015

WE-AB-303-07: Computational Monitoring Framework of Four-Dimensional Dose Distributions During Treatment Time Based On a 2D/3D Registration with Adaptive Transformation Parameters in Lung Stereotactic Body Radiotherapy

Purpose: The aim of this study was to develop a computational framework for monitoring four-dimensional (4D) dose distributions during treatment time based on a 2D/3D registration with adaptive transformation parameters (ATPs) in lung stereotactic body radiotherapy (SBRT). Methods: The 4D dose distributions during the SBRT were calculated by applying a pencil beam convolution (PBC) algorithm to simulated 4D-computed tomography (CT) images during treatment time (hereinafter referred to as “treatment” 4D-CT images). The “treatment” 4D-CT images were derived by deforming 3D planning CT images by using transformation parameters so that 2D planning portal dose images (PDIs) can resemble 2D dynamic clinical PDIs in each frame, which were derived from electronic portal imaging device (EPID) images. The transformation parameters were optimized by the Levenberg- Marquardt (LM) algorithm. The optimized transformation parameters in a certain frame were adaptively employed as initial transformation parameters for optimization of the parameters in the consecutive frame. Gamma pass rates (3mm/3%) between dynamic clinical PDIs and dynamic “treatment” PDIs derived from the “treatment” 4D-CT images were measured for comparing the proposed frameworks without and with the ATPs. These proposed frameworks were applied to the EPID dynamic images (40 frames) of two patients with lung cancer, who underwent the SBRT. Results: Gamma pass rates without and with the ATPs were 83.01±4.42% and 89.31±3.18% on average, respectively (p<0.05). Moreover, percentage errors between prescribed doses and estimated doses at an isocenter by the proposed frameworks without and with the ATPs were 10.67±5.66% and 6.34±0.47% on average, respectively (p<0.05). Conclusion: The proposed framework with ATPs could be useful to ensure the quality of the SBRT by monitoring 4D dose distributions during treatment time.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

Provably secure and high-rate quantum key distribution with time-bin qudits

The security of conventional cryptography systems is threatened in the forthcoming era of quantum computers. Quantum key distribution (QKD) features fundamentally proven security and offers a promising option for quantum-proof cryptography solution. Although prototype QKD systems over optical fiber have been demonstrated over the years, the key generation rates remain several orders of magnitude lower than current classical communication systems. In an effort toward a commercially viable QKD system with improved key generation rates, we developed a discrete-variable QKD system based on time-bin quantum photonic states that can generate provably secure cryptographic keys at megabit-per-second rates over metropolitan distances. We use high-dimensional quantum states that transmit more than one secret bit per received photon, alleviating detector saturation effects in the superconducting nanowire single-photon detectors used in our system that feature very high detection efficiency (of more than 70%) and low timing jitter (of less than 40 ps). Our system is constructed using commercial off-the-shelf components, and the adopted protocol can be readily extended to free-space quantum channels. In conclusion, the security analysis adopted to distill the keys ensures that the demonstrated protocol is robust against coherent attacks, finite-size effects, and a broad class of experimental imperfections identified in our system.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2017

Singularity: Scientific containers for mobility of compute

Here we present Singularity, software developed to bring containers and reproducibility to scientific computing. Using Singularity containers, developers can work in reproducible environments of their choosing and design, and these complete environments can easily be copied and executed on other platforms. Singularity is an open source initiative that harnesses the expertise of system and software engineers and researchers alike, and integrates seamlessly into common workflows for both of these groups. As its primary use case, Singularity brings mobility of computing to both users and HPC centers, providing a secure means to capture and distribute software and compute environments. This ability to create and deploy reproducible environments across these centers, a previously unmet need, makes Singularity a game changing development for computational science.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Future computing platforms for science in a power constrained era

Power consumption will be a key constraint on the future growth of Distributed High Throughput Computing (DHTC) as used by High Energy Physics (HEP). This makes performance-per-watt a crucial metric for selecting cost-efficient computing solutions. For this paper, we have done a wide survey of current and emerging architectures becoming available on the market including x86-64 variants, ARMv7 32-bit, ARMv8 64-bit, Many-Core and GPU solutions, as well as newer System-on-Chip (SoC) solutions. We compare performance and energy efficiency using an evolving set of standardized HEP-related benchmarks and power measurement techniques we have been developing. In conclusion, we evaluate the potential for use of such computing solutions in the context of DHTC systems, such as the Worldwide LHC Computing Grid (WLCG).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

SMALE: Enhancing Scalability of Machine Learning Algorithms on Extreme-Scale Computing Platforms

Deployment and execution of machine learning tasks on extreme-scale computing platforms face several significant technical challenges: 1) High computing cost incurred by dense networks – The computing workload of deep networks with densely-connected topology increases rapidly with the network size, imposing a non-scalable computing model of extreme-scale computing platforms; 2) Non-optimized workload distribution – Many advanced deep learning algorithms, e.g., sparsification and irregular net-work topology, produce very unbalanced workload distribution on extreme-scale computing platforms. The computation efficiency is greatly hindered by the incurred data and computation redundancies as well as long tails of the node with extensive workload; 3) Constraints in data movement and I/O bottle-neck – Inter-node data movement in extreme-scale computing platforms are associated with high energy and latency costs, and subject to the constraints of I/O bandwidth; and 4) Generalization of algorithm realization and acceleration on computing platforms – The large varieties of machine learning algorithms and structures of extreme-scale computing platforms make the derivation of a generalized algorithm realization and acceleration method very challenging, which, however, is the requirement by domain scientists and interested users. We call the above challenges Smale’s Problems in Machine Learning and Understanding for High-Performance Computing Scientific Discovery. The objective of our three-year research project is to develop a holistic innovation set at structure, assembly, and acceleration layers of machine learning algorithms to address the above challenges in algorithm deployment and execution. Three tasks are particularly performed, including: At the algorithm structure level, we investigate the techniques that can structurally sparsify on the topology of deep networks for computing workload reduction. We also study clustering and pruning techniques that can optimize the workload distributions over the extreme-scale computing platforms; At the algorithm assembly level, we derive a unified learning framework for unsupervised transfer learning and dynamic growing capabilities. Novel training methods are also exploited to enhance the training efficiency of the proposed framework; At the algorithm acceleration level, we will develop a series of techniques that can accelerate the computation of sparse matrix operations, which are one of the core executions in deep learning and optimize memory access of the concerned platforms. Our proposed techniques attack the fundamental problems in machine learning algorithms running on extreme-scale computing platforms by vertically integrating the solutions at three closely entangled layers, paving the long-term scaling path of machine learning applications under DOE context. Three tasks corresponding to the above respective research orientations are performed during the three-year project period with our collaborators at ORNL. The outcome of the proposed project is anticipated to form a holistic solution set of novel algorithms and network topologies, efficient training techniques, and fast acceleration methods to promote the computing scalability of the machine learning applications of particular interest to DOE.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Proton Isovector Helicity Distribution on the Lattice at Physical Pion Mass

We present a state-of-the-art calculation of the isovector quark helicity Bjorken-x distribution in the proton using lattice-QCD ensembles at the physical pion mass. We compute quasi-distributions at proton momenta P z ϵ {2.2; 2.6; 3.0} GeV on the lattice, and match them systematically to the physical parton distribution using large-momentum effective theory (LaMET).We reach an unprecedented precision through high statistics in simulations, large-momentum proton matrix elements, and control of excited-state contamination. The resulting distribution with combined statistical and systematic errors is in agreement with the latest phenomenological analysis of the spin-dependent experimental data; in particular, Δ$\bar{u}$(x) > Δ$\bar{d}$(x).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2018

Scalable deep text comprehension for Cancer surveillance on high-performance computing

Background: Deep Learning (DL) has advanced the state-of-the-art capabilities in bioinformatics applications which has resulted in trends of increasingly sophisticated and computationally demanding models trained by larger and larger data sets. This vastly increased computational demand challenges the feasibility of conducting cutting-edge research. One solution is to distribute the vast computational workload across multiple computing cluster nodes with data parallelism algorithms. In this study, we used a High-Performance Computing environment and implemented the Downpour Stochastic Gradient Descent algorithm for data parallelism to train a Convolutional Neural Network (CNN) for the natural language processing task of information extraction from a massive dataset of cancer pathology reports. We evaluated the scalability improvements using data parallelism training and the Titan supercomputer at Oak Ridge Leadership Computing Facility. To evaluate scalability, we used different numbers of worker nodes and performed a set of experiments comparing the effects of different training batch sizes and optimizer functions.Results: We found that Adadelta would consistently converge at a lower validation loss, though requiring over twice as many training epochs as the fastest converging optimizer, RMSProp. The Adam optimizer consistently achieved a close 2nd place minimum validation loss significantly faster; using a batch size of 16 and 32 allowed the network to converge in only 4.5 training epochs.Conclusions: We demonstrated that the networked training process is scalable across multiple compute nodes communicating with message passing interface while achieving higher classification accuracy compared to a traditional machine learning algorithm.

Qiu, John X.↗

DOE OSTI2019

A Case Study of MPI Over Long Distance Connections

Scientific workflows are increasingly being distributed across wide-area networks, and their code executions are expected to span across geographically dispersed computing systems. MPI has been extensively used to support communications for distributed computations, typically, over compute clusters and high-performance systems within a single facility. We present a case study of performance of MPI basic operations over long distance connections, wherein TCP is used for the underlying transport. We present measurements of execution times of MPI codes that utilize MPI Sendrecv operations over emulated 10Gbps connections with 0-366ms round-trip times, including the longest one spanning the globe. They demonstrate that basic MPI codes can be sustained over long distance connections under external packet loss rates up to 10%. They also highlight the qualitative effects of losses which manifest as increased execution times as a consequence of TCP’s loss recovery process.

Rao, Nageswara S.↗

DOE OSTI2020

Scalable Knowledge Graph Analytics at 136 Petaflop/s

We are motivated by newly proposed methods for data mining large-scale corpora of scholarly publications, such as the full biomedical literature, which may consist of tens of millions of papers spanning decades of research. In this setting, analysts seek to discover how concepts relate to one another. They construct graph representations from annotated text databases and then formulate the relationship-mining problem as one of computing all-pairs shortest paths (APSP), which becomes a significant bottleneck. In this context, we present a new high-performance algorithm and implementation of the Floyd-Warshall algorithm for distributed-memory parallel computers accelerated by GPUs, which we call DSNAPSHOT (Distributed Accelerated Semiring All-Pairs Shortest Path). For our largest experiments, we ran DSNAPSHOT on a connected input graph with millions of vertices using 4, 096nodes (24,576GPUs) of the Oak Ridge National Laboratory's Summit supercomputer system. We find DSNAPSHOT achieves a sustained performance of 136×1015 floating-point operations per second (136petaflop/s) at a parallel efficiency of 90% under weak scaling and, in absolute speed, 70% of the best possible performance given our computation (in the single-precision tropical semiring or “min-plus” algebra). Looking forward, we believe this novel capability will enable the mining of scholarly knowledge corpora when embedded and integrated into artificial intelligence-driven natural language processing workflows at scale.

Kannan, Ramakrishnan {ramki}↗

DOE OSTI2018

Resilient Cyber Infrastructure for the Minimum Wind Curtailment Remedial Control Scheme

Integration of Distributed Energy Resources (DER) has been significantly increasing in the electric power grid. One of the predominant renewable energy sources, wind energy, possess a significant challenge for grid operations, due to its intermittency, uncertainty and variability. In changing operating conditions with wind uncertainty, part of wind may need to be curtailed for the safe utilization of the transmission system. In order to minimize the wind power curtailment and to keep the power flow on a transmission line under limits, a cyber resilient Distributed Remedial Action Scheme (DRAS) is developed in this paper. The RAS is formulated as an optimization problem, considering the real power flow constraints of the transmission lines and given wind variability. In this paper, DRAS is developed to operate even with failures in the computing nodes to make it cyber-resilient. Distributed simplex algorithm is used for optimization in the distributed RAS. The DRAS is implemented in a decentralized platform called Resilient Information Architecture Platform for Smart Grid (RIAPS) to enable cyber resiliency of DRAS. The effectiveness of the proposed approach is validated through offline simulations of the New England 39 bus system and online simulations of IEEE 14 bus test system. In conclusion, a cyber physical test bed utilizing Real Time Digital Simulator (RTDS), Phasor Measurement Units (PMUs), and BeagleBones has been used for the online simulation and validation of the developed cyber-resilient RAS.

17 WIND ENERGY↗

DOE OSTI2017

Imaging of the Li spatial distribution within V 2 O 5 cathode in a coin cell by neutron computed tomography

An understanding of Lithium (Li) spatial distribution within the electrodes of a Li-ion cell, during charge and discharge cycles, is essential to optimize the electrode parameters for increased performance under cycling. In this work, it is demonstrated that the spatial distribution of Li within Vanadium Pentoxide (V 2 O 5 ) electrodes of a small coin cell can be imaged by neutron computed tomography. The neutron attenuation data has been used to construct the three-dimensional Li spatial images. Specifically, it is shown that there is sufficient neutron imaging contrast between lithiated and delithiated regions of V 2 O 5 electrode making it possible to map Li distributions even in small electrodes with thicknesses <1 mm. The images reveal that the Li spatial distribution is inhomogeneous and a relatively higher C-rate leads to more non-uniform Li distribution after Li insertion. The non-uniform distribution suggests the limitation of Li diffusion within the electrode during the lithiation process under the relatively high cycling rates.

25 ENERGY STORAGE↗

DOE OSTI2022

PythonFOAM: In-situ data analyses with OpenFOAM and Python

Here, we outline the development of a general-purpose Python-based data analysis tool for OpenFOAM. Our implementation relies on the construction of OpenFOAM applications that have bindings to data analysis libraries in Python. Double precision data in OpenFOAM is cast to a NumPy array using the NumPy C-API and Python modules may then be used for arbitrary data analysis and manipulation on flow-field information. We highlight how the proposed wrapper may be used for an in-situ online singular value decomposition (SVD) implemented in Python and accessed from the OpenFOAM solver PimpleFOAM. Here, 'in-situ' refers to a programming paradigm that allows for a concurrent computation of the data analysis on the same computational resources utilized for the partial differential equation solver. In addition, to demonstrate parallel deployments, we deploy a distributed SVD, which collects snapshot data across the ranks of a distributed simulation to compute the global left singular vectors. Crucially, both OpenFOAM and Python share the same message passing interface (MPI) communicator for this deployment which allows Python objects and functions to exchange NumPy arrays across ranks. Subsequently, we provide scaling assessments of this distributed SVD on multiple nodes of Intel Broadwell and KNL architectures for canonical test cases such as the large eddy simulations of a backward facing step and a channel flow at friction Reynolds number of 395. Finally, we demonstrate the deployment of a deep neural network for compressing the flow-field information using an autoencoder to demonstrate an ability to use state-of-the-art machine learning tools in the Python ecosystem.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Sharpness of interference pattern of the 3-pole wiggler

Due to the small emittance, radiation from neighboring poles of a strong wiggler in future multi-bend achromat-based storage rings can exhibit sharp interference patterns. The spectral-angular distributions of the 3-pole wiggler for the proposed Advanced Photon Source (APS) upgrade were computed and prominent interference patterns were found. In this paper we provide an understanding of such interference patterns. The equations governing the interference pattern are described and computed spectral-angular distributions of a modeled 3-pole wiggler magnetic field using these equations are presented.

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

Nucleon valence quark distribution functions from Lattice QCD

We present results on the nucleon valence quark distribution extracted from Lattice QCD simulations, using a gauge ensemble of Nf=2+1 Wilson-Clover fermions with a pion mass of m?=350 MeV and lattice spacing of a=0.091 fm. We obtain reduced Ioffe Time Distributions (rITDs) by computing appropriate matrix elements on the lattice, and elaborate on the extraction of the desired quark distributions from the rITDs following the pseudo-PDF approach. A set of techniques are considered in order to ensure ground state dominance. Theoretical and experimental implications of our calculation are discussed.

Kallidonis, Christos↗