Programming and Performance Visualization Tools
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Visualization and Quantum Computation of Moire Superconductivity in Bilayer Graphene, Carbon Nanocones and Nanostrips
The purpose of this project is to visualize and study the electronic properties of carbon nanostructures. The principal structures studied are AA and AB (Bernal) stacked graphene bilayers, an 80C nanocone with pentagonal defects at its apex, and a 16C nanoring. Graphene and other based nanostructures have attracted much attention due to their unique electronic properties. For example, bilayer graphene shows superconductivity at certain magic angles of rotation. Open source molecular modeling and visualization programs like SAMSON, Avogadro, and VMD are utilized to generate the structures above. Using bond information in order to find nearest neighbors, an adjacency matrix can be generated from which the energy eigenvalues are solvable and are used to visualize the dispersion relation, free energy, persistent current, Green’s function, and entanglement entropy. The goal of this project is to study the effect of rotation on the electronic properties of bilayer graphene, particularly as it pertains to the magic angle where superconductivity occurs, as well as the electronic properties of the bilayer Moire patterns which emerge due to the superposition of the identical lattices at different angles. An important goal is to incorporate phonon interactions into the tight-binding model for carbon structures, and to utilize quantum based computing as a tool to find the ground state properties of these structures, particularly in the terminating case where boundary conditions have an effect.
97 MATHEMATICS AND COMPUTING↗
Visualization techniques for the gyrokinetic tokamak simulation code
Gyrokinetic simulations of plasma microturbulence in tokamaks are challenging to visualize because the compute grid follows the magnetic field lines that spiral around the torus. We have overcome this challenge by developing three new approaches that improve visualization of gyrokinetics. Our techniques work directly with the topology of magnetic flux surfaces where the simulation stores variables in concentric rings on poloidal planes (vertical cross sections of the torus). Our visualization preview step triangulates each consecutive pair of rings to display the data on a poloidal plane. The second visualization technique follows spiral field lines around the torus and constructs polygons to visualize a flux surface. Third, the poloidal triangles are connected between planes to form prisms that compose a 3-D model of the entire torus. The visualization workflow produces detailed geometry that matches the high resolution, irregular compute grid for every time step. The surface and solid models are displayed in scientific visualization programs to effectively explore and communicate the results, including fluctuation of electron density, ion temperature, and electrostatic potential. Highly detailed renderings verify plasma behavior along magnetic field lines over time.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
CPU Volume Rendering of Adaptive Mesh Refinement Data
Adaptive Mesh Refinement (AMR) methods are widespread in scientific computing, and visualizing the resulting data with efficient and accurate rendering methods can be vital for enabling interactive data exploration. In this work, we detail a comprehensive solution for directly volume rendering block-structured (Berger-Colella) AMR data in the OSPRay interactive CPU ray tracing framework. In particular, we contribute a general method for representing and traversing AMR data using a kd-tree structure, and four different reconstruction options, one of which in particular (the basis function approach) is novel compared to existing methods. Here, we demonstrate our system on two types of block-structured AMR data and compressed scalar field data, and show how it can be easily used in existing production-ready applications through a prototypical integration in the widely used visualization program ParaView.
97 MATHEMATICS AND COMPUTING↗
Hindsight logging for model training
In modern Machine Learning, model training is an iterative, experimental process that can consume enormous computation resources and developer time. To aid in that process, experienced model developers log and visualize program variables during training runs. Exhaustive logging of all variables is infeasible, so developers are left to choose between slowing down training via extensive conservative logging, or letting training run fast via minimalist optimistic logging that may omit key information. As a compromise, optimistic logging can be accompanied by program checkpoints; this allows developers to add log statements post-hoc, and "replay" desired log statements from checkpoint---a process we refer to as hindsight logging. Unfortunately, hindsight logging raises tricky problems in data management and software engineering. Done poorly, hindsight logging can waste resources and generate technical debt embodied in multiple variants of training code. In this paper, we present methodologies for efficient and effective logging practices for model training, with a focus on techniques for hindsight logging. Our goal is for experienced model developers to learn and adopt these practices. To make this easier, we provide an open-source suite of tools for Fast Low-Overhead Recovery (flor) that embodies our design across three tasks: (i) efficient background logging in Python, (ii) adaptive periodic checkpointing, and (iii) an instrumentation library that codifies hindsight logging for efficient and automatic record-replay of model-training. Model developers can use each flor tool separately as they see fit, or they can use flor in hands-free mode, entrusting it to instrument their code end-to-end for efficient record-replay. Our solutions leverage techniques from physiological transaction logs and recovery in database systems. Evaluations on modern ML benchmarks demonstrate that flor can produce fast checkpointing with small user-specifiable overheads (e.g. 7%), and still provide hindsight log replay times orders of magnitude faster than restarting training from scratch.
Computer Science↗
New Software Architecture Options for the TCL Data Acquisition System
The Turbulent Combustion Laboratory (TCL) conducts research on combustion in turbulent flow environments. To conduct this research, the TCL utilizes several pulse lasers, a traversable wind tunnel, flow controllers, scientific grade CCD cameras, and numerous other components. Responsible for managing these different data-acquiring instruments and data processing components is the Data Acquisition (DAQ) software. However, the current system is constrained to running through VXI hardware—an instrument-computer interface—that is several years old, requiring the use of an outdated version of the visual programming language, LabVIEW. A new Acquisition System is being programmed which will borrow heavily from either a programming model known as the Current Value Table (CVT) System or another model known as the Server-Client System. The CVT System model is in essence, a giant spread sheet from which data or commands may be retrieved or written to, and the Server-Client System is based on network connections between a server and a client, very much like the Server-Client model of the Internet. Currently, the bare elements of a CVT DAQ Software have been implemented, consisting of client programs in addition to a server program that the CVT will run on. This system is being rigorously tested to evaluate the merits of pursuing the CVT System model and to uncover any potential flaws which may result in further implementation. If the CVT System is chosen, which is likely, then future work will consist of build up the system until enough client programs have been created to run the individual components of the lab. The advantages of such a System will be flexibility, portability, and polymorphism. Additionally, the new DAQ software will allow the Lab to replace the VXI with a newer instrument interface—the PXI—and take advantage of the capabilities of current and future versions of LabVIEW.
97 MATHEMATICS AND COMPUTING↗
NUM-DAT File Format Specification: Used in M-9 Gun Experiment Data Archiving
The M-9 Shock and Detonation Physics group executes experiments on gun and explosive platforms with large numbers of oscilloscopes used for data acquisition. The data acquisition from these oscilloscopes was automated many years ago using a custom piece of software called RunDig . The default save format from this software is a custom structure referred to as "NUM-DAT" format. This file format includes a text ".DAT" file which is a header file used to interpret the binary ".NUM" file which contains the oscilloscope data. The data save format was originally developed by John Vorthman and has been in use by M-9 personnel for over 20 years. This data format has been used for archiving data from experiments performed by M-9 personnel at TA-40, TA-39, and the TA-55 Impact Test Facility. Numerous custom analysis and visualization programs have also been developed, and continue to be used, that utilize this data format. This document describes the NUM-DAT format and provides code examples for reading the format and converting it to other formats.
47 OTHER INSTRUMENTATION↗
LongBondEliminator: A Molecular Simulation Tool to Remove Ring Penetrations in Biomolecular Simulation Systems
We develop a workflow, implemented as a plugin to the molecular visualization program VMD, that can fix ring penetrations with minimal user input. LongBondEliminator, detects ring piercing artifacts by the long, strained bonds that are the local minimum energy conformation during minimization for some assembled simulation system. The LongBondEliminator tool then automatically treats regions near these long bonds using multiple biases applied through NAMD. By combining biases implemented through the collective variables module, density-based forces, and alchemical techniques in NAMD, LongBondEliminator will iteratively alleviate long bonds found within molecular simulation systems. Through three concrete examples with increasing complexity, a lignin polymer, an viral capsid assembly, and a large, highly glycosylated protein aggrecan, we demonstrate the utility for this method in eliminating ring penetrations from classical MD simulation systems. The tool is available via gitlab as a VMD plugin, and has been developed to be generically useful across a variety of biomolecular simulations.
59 BASIC BIOLOGICAL SCIENCES↗
Spatial profiling of the interplay between cell type- and vision-dependent transcriptomic programs in the visual cortex
How early sensory experience during “critical periods” of postnatal life affects the organization of the mammalian neocortex at the resolution of neuronal cell types is poorly understood. We previously reported that the functional and molecular profiles of layer 2/3 (L2/3) cell types in the primary visual cortex (V1) are vision-dependent [S. Chenget al.,Cell185, 311–327.e24 (2022)]. Here, we characterize the spatial organization of L2/3 cell types with and without visual experience. Spatial transcriptomic profiling based on 500 genes recapitulates the zonation of L2/3 cell types along the pial–ventricular axis in V1. By applying multitasking theory, we suggest that the spatial zonation of L2/3 cell types is linked to the continuous nature of their gene expression profiles, which can be represented as a 2D manifold bounded by three archetypal cell types. By comparing normally reared and dark reared L2/3 cells, we show that visual deprivation-induced transcriptomic changes comprise two independent gene programs. The first, induced specifically in the visual cortex, includes immediate-early genes and genes associated with metabolic processes. It manifests as a change in cell state that is orthogonal to cell-type-specific gene expression programs. By contrast, the second program impacts L2/3 cell-type identity, regulating a subset of cell-type-specific genes and shifting the distribution of cells within the L2/3 cell-type manifold. Through an integrated analysis of spatial transcriptomics with single-nucleus RNA-seq data, we describe how vision patterns cortical L2/3 cell types during the critical period.
Science & Technology - Other Topics↗
NDE Technology Development Program for Non-Visual Volumetric Inspection Technology; Sensor Effectiveness Testing Report
The Pacific Northwest National Laboratory (PNNL) located in Richland, Washington, hosted and administered Sensor Effectiveness Testing that allowed four different participants to demonstrate the NDE volumetric inspection technologies that were previously demonstrated during the Technology Screening session. This document provides a Sensor Effectiveness Testing report for the final part of Phase I of a three-phase NDE Technology Development Program designed to identify and mature a system or set of non-visual volumetric NDE technologies for Hanford DST primary liner bottom inspection. Phase I of the program will baseline the performance of current or emerging non-visual volumetric NDE technologies for their ability to detect and characterize primary liner bottom flaws, and identify candidate technologies for adaptation and maturation for Phase II of the program.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NDE Technology Development Program for Non-Visual Volumetric Inspection Technology: Technology Screening Report
The Pacific Northwest National Laboratory (PNNL) located in Richland, Washington, hosted and administered a Technology Screening test that allowed four different participants to demonstrate the NDE volumetric inspection technologies that were proposed in response to the WRPS EOI. This document provides a Technology Screening test report for the initial part of Phase I of a three-phase NDE Technology Development Program designed to identify and mature a system or set of non-visual volumetric NDE technologies for Hanford DST primary liner bottom inspection. Phase I of the program will baseline the performance of current or emerging non-visual volumetric NDE technologies for their ability to detect and characterize primary liner bottom flaws, and identify candidate technologies for adaptation and maturation for Phase II of the program.
42 ENGINEERING↗
NDE Technology Development Program for Non-Visual Volumetric Inspection Technology - Phase I Summary Report
This report provides a summary of the outcomes and findings of the sensor testing and evaluation work performed in FY2017 and first quarter of FY2018 under Phase I of the NDE Technology Development Program. The objective of this summary report is to provide a condensed version of the information provided in the Phase I program plan, Phase I test plan, the two comprehensive Phase I interim test reports issued for Technology Screening and advanced Sensor Effectiveness Testing, and the results from testing and demonstrations conducted Post-Sensor Effectiveness Testing for the purpose of supporting the selection of sensor technologies for maturation under Phase II. The orthogonal research and planning that should accompany technology maturation to support Phase II and Phase III are also included. The full details of the test mock-ups and flaws, sensor technologies, tests, evaluation criteria, and evaluation results are included in Phase I and provided in the interim Phase I test reports on Technology Screening and Sensor Effectiveness Testing.
47 OTHER INSTRUMENTATION↗
A Unified Data-Driven Approach for Programming In Situ Analysis and Visualization Final Scientific/Technical Report
The goal of Kitware’s work has been the study of a unified data-driven approach for programming applications and in situ analysis and visualization. We have especially focused on understanding the interplay between data-centric programming model requirements at extreme-scale and the overall impact of those requirements on the design, capabilities, flexibility, and implementation details for both applications and the supporting in situ infrastructure. We have successfully demonstrated the use of pipeline abstractions within Legion and developed a number of algorithms that were integrated within the Legion simulation framework. This report focuses on describing milestones, mainly those focusing on exploring pipeline abstractions in the context of the Legion programming model.
97 MATHEMATICS AND COMPUTING↗
Final Technical Report for: A Unified Data-Driven Approach for Programming In Situ Analysis and Visualization
The placement and movement of data is becoming the key limiting factor on both performance and energy efficiency of high performance computations. As systems generate more data, it is becoming increasingly difficult to actually move that data elsewhere for post-processing, as the rate of improvements in supporting I/O infrastructure is not keeping pace. Together, these trends are creating a shift in how we think about exascale computations, from a viewpoint that focuses on FLOPS to one that focuses on data and data-centric operations as fundamental to the reasoning about, and optimization of, scientific workflows on extreme-scale architectures. The overarching goal of our effort was the study of a unified data-driven approach for programming applications and in situ analysis and visualization. Our work was to understand the interplay between data-centric programming model requirements at extreme-scale and the overall impact of those requirements on the design, capabilities, flexibility, and implementation details for both applications and the supporting in situ infrastructure. In this context, we made many improvements to the Legion programming system (one of the leading data-centric models today) and demonstrated in situ analyses on real application codes using these improvements.
97 MATHEMATICS AND COMPUTING↗
DETECTING FIRE WITH MACHINE LEARNING-ENABLED VISUAL MONITORING FOR NUCLEAR POWER PLANT ENVIRONMENTS
Nuclear power plants are experiencing significant cost challenges to remain competitive with other energy-generation utilities. Unlike other industries, the cost of operation and maintenance activities is mostly attributed to workforce costs. To mitigate this, nuclear power plant stakeholders are increasingly interested in the development and deployment of machine learning methods to potentially automate or augment manually intensive tasks to reduce costs, especially for monitoring activities. One monitoring function that is visually demanding and that can occur frequently to meet the requirements of a fire protection program is visually monitoring an area for fire occurrence. Currently, fire watch activities consist of a worker physically stationed at a given location with the sole responsibility of observing a given area to ensure a fire is detected and mitigated promptly. This effort focused on the development and evaluation of a suitable deep convolutional neural network to classify individual video frames at a sub-second frequency for the occurrence of “fire” and “no fire” in varying industrial environments similar to nuclear power plants. It is believed that a trained neural network model could be integrated with existing facility video surveillance camera feeds to generate alerts when fire inferences occur in individual frames captured at sub-second temporal resolutions. Extensive effort was dedicated to identifying and curating suitable imagery training data representing varying environments and scene settings with and without flame features to maximize generalization in nuclear power plant environments. The data collection effort resulted in the aggregation of a large, labeled image library exceeding 12,000 images to support model training for diverse industrial environments. A deep neural network model incorporating parallel multi-scale capabilities was developed and trained to support accurate image-based detection of flame incidents of varying sizes and spectral feature properties within heterogeneous scenes. Analysis results show that the trained model can achieve high inference accuracy despite heterogeneous scene environments and components. Testing accuracy exceeded 95.0 percent with very low false positive and false negative inferences.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Double-Shell Tank Visual Inspection Changes Resulting from the Tank 241-AY-102 Primary Tank Leak
As part of the Double-Shell Tank (DST) Integrity Program, remote visual inspections are utilized to perform qualitative in-service inspections of the DSTs in order to provide a general overview of the condition of the tanks. During routine visual inspections of tank 241-AY-102 (AY-102) in August 2012, anomalies were identified on the annulus floor which resulted in further evaluations. In October 2012, Washington River Protection Solutions, LLC determined that the primary tank of AY-102 was leaking. Following identification of the tank AY-102 probable leak cause, evaluations considered the adequacy of the existing annulus inspection frequency with respect to the circumstances of the tank AY-102 1eak and the advancing age of the DST structures. The evaluations concluded that the interval between annulus inspections should be shortened for all DSTs, and each annulus inspection should cover > 95 percent of annulus floor area, and the portion of the primary tank (i.e., dome, sidewall, lower knuckle, and insulating refractory) that is visible from the annulus inspection risers. In March 2013, enhanced visual inspections were performed for the six oldest tanks: 241-AY-101, 241-AZ-101,241-AZ-102, 241-SY-101, 241-SY-102, and 241-SY-103, and no evidence of leakage from the primary tank were observed. Prior to October 2012, the approach for conducting visual examinations of DSTs was to perform a video examination of each tank's interior and annulus regions approximately every five years (not to exceed seven years between inspections). Also, the annulus inspection only covered about 42 percent of the annulus floor.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Performance Monitoring Program: Developing Comparative Metrics for Fitness-for-Duty Programs
To comply with U.S. Nuclear Regulatory Commission (NRC) regulations, licensees and entities authorized under Title 10 of the Code of Federal Regulations (CFR) Part 26 Section 26.3 (§ 26.3) are required to have fitness-for-duty (FFD) programs. Under § 26.3, the expectation of these FFD programs is to provide reasonable assurance that individuals who are granted unescorted access to nuclear power reactor protected areas and Category I fuel cycle facility material control areas are trustworthy, will perform their tasks in a reliable manner, are not under the influence of any substance, legal or illegal, that may impair their ability to perform their duties, and are not mentally or physically impaired from any cause that can adversely affect their ability to safely and competently perform their duties. Pacific Northwest National Laboratory (PNNL) was tasked with developing a performance monitoring program to risk inform NRC inspection and policy regarding quantitative FFD performance data. To meet this need, PNNL developed methodologies that could be implemented within a performance monitoring program. Throughout this report, these methodologies are referred to as comparative metrics. The NRC provided PNNL with 2016–2019 data from annual reporting forms and single positive test forms provided by licensees and other entities. For most of the comparative metrics, the analyses required customized processing, such as creating filtering fields, adding data fields and summaries, and joining datasets. The comparative metrics developed include: random testing rate, random policy violation rate, pre-access policy violation rate, subversion attempt rate, and number of policy violations by labor category. The comparative metrics can be used for parsing and visualizing FFD program data and monitoring FFD performance at the labor category, facility, licensee, and industry levels to risk inform NRC inspection and policy with regard to the FFD data currently collected from licensees and other entities that implement Part 26 requirements. Furthermore, these developed comparative metrics allow a more in-depth look at the FFD programs for the industry to discern trends and patterns that may warrant changes at an industry level and to inform policy decisions. These analyses should be refreshed as new FFD data become available.
99 GENERAL AND MISCELLANEOUS↗
XtalCAMP: a comprehensive program for the analysis and visualization of scanning Laue X-ray micro-/nanodiffraction data
XtalCAMP is a software package based on the MATLAB platform, which is suitable for, but not limited to, the analysis and visualization of scanning Laue X-ray micro-/nanodiffraction data. The main objective of the software is to provide complementary functionalities to the Laue indexing software packages used at several synchrotron beamlines. Here, the graphical user interfaces allow the easy analysis of characteristic microstructure features, including real-time intensity mapping for a quick examination of phase, grain and defect distribution, 2D color-coded mapping of microstructural properties from the output of other Laue indexing software, crystal orientation visualization, grain boundary characterization based on orientation/misorientation calculation, principal strain/stress analysis, and strain ellipsoid representation, as well as a series of additional toolkits. As an example, XtalCAMP is applied to the microstructural investigation of a solution-heat-treated Ni-based superalloy manufactured using a laser 3D-printing technique, and a deformed natural quartzite from Val Bregaglia in the Central Alps.
36 MATERIALS SCIENCE↗