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DOE OSTI2022

Xyce(™) Parallel Electronic Simulator v.7.5

The Xyce Parallel Electronic Simulator simulates electronic circuit behavior in DC, AC, HB, MPDE and transient mode using standard analog (DAE) and/or device (PDE) device models including several age and radiation aware devices. It supports a variety of computing platforms (both serial and parallel) computers. Lastly, it uses a variety of modern solution algorithms dynamic parallel load-balancing and iterative solvers.! ! Xyce is primarily used to simulate the voltage and current behavior of a circuit network (a network of electronic devices connected via a conductive network). As a tool, it is mainly used for the design and analysis of electronic circuits.! ! Kirchoff's conservation laws are enforced over a network using modified nodal analysis. This results in a set of differential algebraic equations (DAEs). The resulting nonlinear problem is solved iteratively using a fully coupled Newton method, which in turn results in a linear system that is solved by either a standard sparse-direct solver or iteratively using Trilinos linear solver packages, also developed at Sandia National Laboratories.

Source record↗

DOE OSTI2022

EAP Patterns

EAP Patterns: Memory access and iteration patterns from the EAP code base with the physics removed. This is intended to be a serial app representing memory access patterns. We will populate the data structures from EAP output files to provide representative patterns of face and cell loops within the EAP code base The arguments to the program are the EAP output file and the number of MPI processors to emulate. Currently there is no MPI in the application and the number of processors represents a means of emulating the halo (clone) cells around the domain of the given processor. An optional argument `processor_ID` can be provided to specify the processor to emulate. In the future we plan to include the ability to run multiple MPI ranks to more accurately represent the on-node demands on memory bandwidth.

Swaminarayan, Sriram↗

DOE OSTI2013

Xyce(™) Parallel Electronic Simulator v.7.6

Keiter, Eric↗

DOE OSTI2023

Drink From The Firehose

DFTF converts Redfish events to messages on a Kafka bus. DFTF subscribes to events in Redfish, specifically Alerts and CrayTelemetry. It listens for HTTP POST requests containing event data, reduces the data, serializes the reduced data using Avro, and publishes the data to topics in Kafka.

Morrone, ChristopherJ↗

DOE OSTI2022

Phase Field Dislocation Dynamics (PFDD) version 2.x

This disclosure is for version 2.x of a mesoscale model called Phase Field Dislocation Dynamics (PFDD). PFDD is used for investigating deformation in nanoscale (grain sizes of ~300 nm and less) materials, such as metals and alloys. This approach models the motion and interaction of individual defects, namely dislocations, in the material using scalar-valued phase field variables, also called order parameters. The system is evolved through energy minimization thus the model calculates the total energy density in terms of the phase field variables. The energy minimization is completed using the Ginzburg-Landau equation, and is implemented with explicit time integration. The total system energy can be comprised of several terms, including the strain energy (which describes dislocation-dislocation interactions), the energy due to an applied stress (dislocation interactions with the applied stress), and a core/lattice (perfect dislocations) or generalized stacking fault (partial dislocations) energy (described the dislocation core structure). The latter term in particular may vary based on the crystal structure being modeled and is typically informed using lower length scale (e.g., atomistic) approaches, although no such (atomistic) calculations are completed within the PFDD algorithm. This basic formulation was previously reviewed by Los Alamos National Laboratory and released under license number C17113. This previously reviewed version we will henceforth refer to as PFDD v1.0. PFDD v1.0 consisted of 2 codes (one parallel and one serial) plus input files, all written in the C language. This new disclosure is addressing the next versions of the PFDD, versions 2.x. There have been several enhancements of PFDD v1.0, which are described here and included in the attached code, which we will refer to as PFDD v2.0. There are also several new features described here that are either planned or already in process and are expected to be subsequent releases, i.e., v2.1, v2.2, ...v2.x.

Hunter, Abigail↗

DOE OSTI2015

DIY2

DIY2 is a data parallel out-of-core library, written in C++. It provides facilities for domain decomposition into blocks and abstracts both neighborhood and global communication patterns to the level of blocks. DIY2 supports automatic threading. It also provides a data serialization mechanism, using which it allows for automatic movement of blocks in and out of core.

Morozov, Dmitriy↗

DOE OSTI2024

GDM (Grid Data Models) [SWR-24-31]

Grid Data Models (GDM) is a collection of pydantic data models for power distribution and transmission system components. These data models can be easily serialized and deserialized. GDM provides a standard interface for accessing power system component data with built in validation. See also: https://pypi.org/project/grid-data-models/

Duwadi, Kapil↗

DOE OSTI2024

pyTriBeam

SAND2025-01899O pyTriBeam is a software tool that creates automated processes for a scanning electron microscope including workflows for 3D serial sectioning dataset collection, high-res image montaging, and support for custom script use. This includes integration for 3D chemical mapping (EDS) and crystallographic (EBSD) data collection with select supported detectors. The application allows end users to setup and run customizable data collection workflows without requiring expertise in programming. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Hovey, Chad↗

DOE OSTI2025

MultifidelityOpt- bohydra

Multifidelity Bayesian optimization with serial and MPI-enabled (parallel, asynchronous) workflows.

Grosskopf, Mike [Los Alamos National Laboratory]↗

DOE OSTI2026

lumicap v0.1

Automated HDR luminance imaging system designed for daylighting research and building science. It controls a fisheye-lens camera to capture time-lapse bracket sequences, merges them into calibrated HDR images, and runs a full post-processing pipeline — all unattended. Features: - Scheduled LDR bracket capture via gphoto2 - HDR merging with vignetting, ND filter, and fisheye projection corrections - Illuminance and luminance meter integration (Konica Minolta T-10A, LS-100/150) - Daylight glare probability (DGP) and solar position computation - Automated false-color rendering, JPEG thumbnails, and daily time-lapse video - CSV data logging per timestep Uses: - Long-term monitoring of daylight conditions in buildings - Glare analysis for occupant comfort research - Solar irradiance and sky luminance studies Advantages: - End-to-end automation — capture, calibration, analysis, and archiving run without manual intervention - Built on the proven Radiance toolchain, ensuring photometrically accurate HDR output - Hardware-agnostic meter support via serial auto-detection - Lightweight — no GUI overhead, deployable on a headless Raspberry Pi or similar embedded system

Wang, Taoning [Lawrence Berkeley National Laborato↗

DOE OSTI2014

Applications of immunoPET: Using 124 I-anti-PSCA A11 minibody for imaging disease progression and response to therapy in mouse xenograft models of prostate cancer

Here, prostate stem cell antigen (PSCA) is highly expressed in local prostate cancers and prostate cancer bone metastases and its expression correlates with androgen receptor activation and a poor prognosis. Here in this study, we investigate the potential clinical applications of immunoPET with the anti-PSCA A11 minibody, an antibody fragment optimized for use as an imaging agent. We compare A11 minibody immunoPET to 18 F-Fluoride PET bone scans for detecting prostate cancer bone tumors and evaluate the ability of the A11 minibody to image tumor response to androgen deprivation. Osteoblastic, PSCA expressing, LAPC-9 intratibial xenografts were imaged with serial 124 I-anti-PSCA A11 minibody immunoPET and 18 F-Fluoride bone scans. Mice bearing LAPC-9 subcutaneous xenografts were treated with either vehicle or MDV-3100 and imaged with A11 minibody immunoPET/CT scans pre- and post-treatment. Ex vivo flow cytometry measured the change in PSCA expression in response to androgen deprivation. A11 minibody demonstrated improved sensitivity and specificity over 18 F-Fluoride bone scans for detecting LAPC-9 intratibial xenografts at all time points. Finally, LAPC-9 subcutaneous xenografts showed downregulation of PSCA when treated with MDV-3100 which A11 minibody immunoPET was able to detect in vivo .

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2016

Persistent Microvascular Obstruction After Myocardial Infarction Culminates in the Confluence of Ferric Iron Oxide Crystals, Proinflammatory Burden, and Adverse RemodelingCLINICAL PERSPECTIVE

Emerging evidence now supports the notion that persistent microvascular obstruction (PMO) may be more predictive of major adverse cardiovascular events than MI size itself. But, how PMO, a phenomenon limited to the acute/sub-acute period of MI, imparts adverse remodeling throughout the post MI period, particularly after its resolution, is incompletely understood. We hypothesized that PMOs resolve into chronic iron crystals within MI territories and actively impart a proinflammatory burden and adverse remodeling of infarction and LV in the chronic phase of MI. Canine models reperfused (n=20) and non-reperfused (n=20) with and without PMO were studied with serial cardiac MRI to characterize the spatiotemporal relationships between PMO, iron deposition, and infarct and LV remodeling indices between acute (day 7, post MI) and chronic (week 8, post MI). Histopathology and immunohistochemistry were used to validate the iron deposition, microscopically map and quantify the relationship between iron-rich chronic MI regions against pro-inflammatory macrophages, proinflammatory cytokines and matrix metalloproteinase. Atomic resolution transmission electron microscopy (TEM) was used to determine the crystallinity of iron and assess the physical effects of iron on lysosomes within macrophages, and energy-dispersive X-ray spectroscopy (EDS) to identify the chemical composition of the iron composite. Results showed that PMOs lead to iron deposition within chronic MI and that the extent of chronic iron deposition is strongly related to PMO Volume (r>0.6, p<0.001). TEM and EDS analysis showed that iron within chronic MI is found within macrophages as aggregates of nanocrystals of ~2.5 nm diameter in ferric state. Correlative histological studies showed that iron content, proinflammatory burden and collagen degrading enzyme were highly correlated (r >0.7, p<0.001). Iron within chronic MI was significantly associated with infarct resorption (r>0.5, p<0.001) and adverse structural (r>0.5, p<0.001) remodeling. Territories of PMO in the acute phase of MI resolve into iron oxide nanocrystals in ferric state in the chronic phase of MI. The amount of iron deposition is determined by the extent of persistent microvascular obstruction and is directly related to the extent of pro-inflammatory burden, infarct thinning and adverse LV remodeling. Resolution of PMO into iron deposition could be a potential contributing source to the adverse remodeling of the heart in the chronic phase of MI.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

Determinants of the efficacy of HIV latency-reversing agents and implications for drug and treatment design

HIV eradication studies have focused on developing latency-reversing agents (LRAs). However, it is not understood how the rate of latent reservoir reduction is affected by different steps in the process of latency reversal. Furthermore, as current LRAs are host-directed, LRA treatment is likely to be intermittent to avoid host toxicities. Few careful studies of the serial effects of pulsatile LRA treatment have yet been done. This lack of clarity makes it difficult to evaluate the efficacy of candidate LRAs or predict long-term treatment outcomes. Here, we constructed a mathematical model that describes the dynamics of latently infected cells under LRA treatment. Model analysis showed that, in addition to increasing the immune recognition and clearance of infected cells, the duration of HIV antigen expression (i.e., the period of vulnerability) plays an important role in determining the efficacy of LRAs, especially if effective clearance is achieved. Patients may benefit from pulsatile LRA exposures compared with continuous LRA exposures if the period of vulnerability is long and the clearance rate is high, both in the presence and absence of an LRA. Overall, the model framework serves as a useful tool to evaluate the efficacy and the rational design of LRAs and combination strategies.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Exploring Randomly Wired Neural Networks for Climate Model Emulation

Exploring the climate impacts of various anthropogenic emissions scenarios is key to making informed decisions for climate change mitigation and adaptation. State-of-the-art Earth system models can provide detailed insight into these impacts but have a large associated computational cost on a per-scenario basis. This large computational burden has driven recent interest in developing cheap machine learning models for the task of climate model emulation. In this paper, we explore the efficacy of randomly wired neural networks for this task. We describe how they can be constructed and compare them with their standard feedforward counterparts using the ClimateBench dataset. Specifically, we replace the serially connected dense layers in multilayer perceptrons, convolutional neural networks, and convolutional long short-term memory networks with randomly wired dense layers and assess the impact on model performance for models with 1 million and 10 million parameters. We find that models with less-complex architectures see the greatest performance improvement with the addition of random wiring (up to 30.4% for multilayer perceptrons). Furthermore, of 24 different model architecture, parameter count, and prediction task combinations, only one had a statistically significant performance deficit in randomly wired networks relative to their standard counterparts, with 14 cases showing statistically significant improvement. We also find no significant difference in prediction speed between networks with standard feedforward dense layers and those with randomly wired layers. These findings indicate that randomly wired neural networks may be suitable direct replacements for traditional dense layers in many standard models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Discrete event performance prediction of speculatively parallel temperature-accelerated dynamics

Due to its unrivaled ability to predict the dynamical evolution of interacting atoms, molecular dynamics (MD) is a widely used computational method in theoretical chemistry, physics, biology, and engineering. Despite its success, MD is only capable of modeling time scales within several orders of magnitude of thermal vibrations, leaving out many important phenomena that occur at slower rates. The Temperature Accelerated Dynamics (TAD) method overcomes this limitation by thermally accelerating the state-to-state evolution captured by MD. Due to the algorithmically complex nature of the serial TAD procedure, implementations have yet to improve performance by parallelizing the concurrent exploration of multiple states. Here we utilize a discrete event-based application simulator to introduce and explore a new Speculatively Parallel TAD (SpecTAD) method. We investigate the SpecTAD algorithm, without a full-scale implementation, by constructing an application simulator proxy (SpecTADSim). Finally, following this method, we discover that a nontrivial relationship exists between the optimal SpecTAD parameter set and the number of CPU cores available at run-time. Furthermore, we find that a majority of the available SpecTAD boost can be achieved within an existing TAD application using relatively simple algorithm modifications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

High-performance epistasis detection in quantitative trait GWAS

epiSNP is a program for identifying pairwise single nucleotide polymorphism (SNP) interactions (epistasis) in quantitative-trait genome-wide association studies (GWAS). A parallel MPI version (EPISNPmpi) was created in 2008 to address this computationally expensive analysis on large data sets with many quantitative traits and SNP markers. However, the falling cost of genotyping has led to an explosion of large-scale GWAS data sets that challenge EPISNPmpi’s ability to compute results in a reasonable amount of time. Therefore, we optimized epiSNP for modern multi-core and highly parallel many-core processors to efficiently handle these large data sets. This paper describes the serial optimizations, dynamic load balancing using MPI-3 RMA operations, and shared-memory parallelization with OpenMP to further enhance load balancing and allow execution on the Intel Xeon Phi coprocessor (MIC). For a large GWAS data set, our optimizations provided a 38.43× speedup over EPISNPmpi on 126 nodes using 2 MICs on TACC’s Stampede Supercomputer. We also describe a Coarray Fortran (CAF) version that demonstrates the suitability of PGAS languages for problems with this computational pattern. We show that the Coarray version performs competitively with the MPI version on the NERSC Edison Cray XC30 supercomputer. Finally, the performance benefits of hyper-threading for this application on Edison (average 1.35× speedup) are demonstrated.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2013

Wound outcome in combat injuries is associated with a unique set of protein biomarkers

Background: The ability to forecast whether a wound will heal after closure without further debridement(s), would provide substantial benefits to patients with severe extremity trauma. Methods: Wound effluent is a readily available material which can be collected without disturbing healthy tissue. For analysis of potential host response biomarkers, forty four serial combat wound effluent samples from 19 patients with either healing or failing traumatic- and other combat-related wounds were examined by 2-D DIGE. Spot map patterns were correlated to eventual wound outcome (healed or wound failure) and analyzed using DeCyder 7.0 and differential proteins identified via LC-MS/MS. Results: This approach identified 52 protein spots that were differentially expressed and thus represent candidate biomarkers for this clinical application. Many of these proteins are intimately involved in inflammatory and immune responses. Furthermore, discriminate analysis further refined the 52 differential protein spots to a smaller subset of which successfully differentiate between wounds that will heal and those that will fail and require further surgical intervention with greater than 83% accuracy. Conclusion: These results suggest candidates for a panel of protein biomarkers that may aid traumatic wound care prognosis and treatment. We recommend that this strategy be refined, and then externally validated, in future studies of traumatic wounds.

2-D DIGE↗

DOE OSTI2024

Designing Ti-6Al-4V microstructure for strain delocalization using neural networks

Abstract The deformation behavior of Ti-6Al-4V titanium alloy is significantly influenced by slip localized within crystallographic slip bands. Experimental observations reveal that intense slip bands in Ti-6Al-4V form at strains well below the macroscopic yield strain and may serially propagate across grain boundaries, resulting in long-range localization that percolates through the microstructure. These connected, localized slip bands serve as potential sites for crack initiation. Although slip localization in Ti-6Al-4V is known to be influenced by various factors, an investigation of optimal microstructures that limit localization remains lacking. In this work, we develop a novel strategy that integrates an explicit slip band crystal plasticity technique, graph networks, and neural network models to identify Ti-6Al-4V microstructures that reduce the propensity for strain localization. Simulations are conducted on a dataset of 3D polycrystals, each represented as a graph to account for grain neighborhood and connectivity. The results are then used to train neural network surrogate models that accurately predict localization-based properties of a polycrystal, given its microstructure. These properties include the ratio of slip accumulated in the band to that in the matrix, fraction of total applied strain accommodated by slip bands, and spatial connectivity of slip bands throughout the microstructure. The initial dataset is enriched by synthetic data generated by the surrogate models, and a grid search optimization is subsequently performed to find optimal microstructures. Describing a 3D polycrystal with only a few features and a combination of graph and neural network models offer robustness compared to the alternative approaches without compromising accuracy. We show that while each material property is optimized through a unique microstructure solution, elongated grain shape emerges as a recurring feature among all optimal microstructures. This finding suggests that designing microstructures with elongated grains could potentially mitigate strain localization without compromising strength.

Ahmadikia, Behnam↗