Massively parallel modeling of electromagnetic field in conductive media: An MPI-CUDA implementation on Multi-GPU computers
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Tu, Xiaolei (ORCID:0000000257345558)↗
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Recent Improvements to the NWChem COSMO Module
This work presents recent developments in the COSMO solvation model implementation in NWChem. A new cavity construction approach, based on the solvent-excluding surface (SES) and utilizing the well-established GEPOL algorithm, has been introduced. Additionally, a straightforward procedure to merge surface segments that are too close─often a source of numerical artifacts─has been implemented. The available methods for correcting outlying surface charges have also been reviewed and improved. To validate the new implementation, we computed dielectric solvation energies for a chemically diverse set of approximately 100 molecules, including neutral species, small ions, and common ionic liquid components. Results were compared to those from GAMESS using the double-cavity method as a reference. Although the double-cavity approach can be regarded as more accurate, the simpler correction schemes available in NWChem─based on scaling factors or Lagrange multipliers─can achieve excellent agreement if the potential is also properly corrected, with mean unsigned deviations of around 0.15 kcal/mol. Predictions of typical vapor–liquid and liquid–liquid equilibria using a COSMO-SAC variant based on NWChem also showed very promising results.
Cavities↗
Neuro-Spark: A Submicrosecond Spiking Neural Networks Architecture for In-Sensor Filtering
Neuro-Spark, which is a new neuromorphic architecture with a field-programmable gate array (FPGA) implementation for ultrafast spiking neural network (SNN) inference at the edge, facilitates smart-pixel in-sensor filtering for high-energy physics experiments at the Large Hadron Collider (LHC). Utilizing the evolutionary optimization for neuromorphic systems (EONS) training method, we generate compact SNN models with 91% signal efficiency, akin to convolutional neural networks but with half the parameters. However, deploying near the detector poses a challenge because the SNN must handle a sustained input data rate exceeding 1013 GB/s. To overcome this, we propose a novel hardware architecture that uses high-level synthesis to construct a tuned architecture for the EONS-trained SNN. In addition to the analysis and validation with an AMD Xilinx Artix-A7 FPGA, our solution consumes only ç24% of FPGA LUT and flipflops. We also introduce an innovative quantization method that reduces FPGA resource utilization by ç15% without compromising accuracy. Our FPGA implementation achieves computing latency of ç10 ns for smart-pixel application inference on an edge FPGA.
Miniskar, Narasinga Rao↗
Automated nuclear cloud feature extraction from film
Chemical, biological, radiological, nuclear, and explosives incidents require rapid detection and characterization for appropriate response. For a nuclear detonation, visible-light cameras may be used to locate the cloud and characterize fallout deposition when coupled with numerical models. Films from the United States’ nuclear testing era compose the only sizeable collection of imagery depicting high-yield detonations. These films offer unique insights into characteristics of flows involving scales that are difficult to replicate experimentally, and they are a valuable source of data for the validation of models for nuclear fallout transport, either as part of emergency response or forensic activities. In this work, we implement modern computer vision and machine learning techniques to identify and track the cloud automatically and subsequently determine the time dependence of some of its features. We trained a ResNet-18 image classifier on hundreds of images to categorize nuclear cloud morphology. Each category or cloud regime is determined by early cloud evolution and is associated to constitutive properties of the flow, such as distribution of vorticity. Next, we identified keypoint features using the KAZE algorithm and tracked these keypoints in the images, allowing us to determine the dimensions and velocities of the cloud across film frames. These measurements converted to real-world units provide valuable experimental data that can be used in the development and validation of nuclear cloud models. We compared the results of this method against manual cloud rise measurements from two different films. In one, our automated method accelerated the feature extraction process without sacrificing measurement accuracy.
Khristy, Joel [ORNL] (ORCID:0000000209963060)↗
THE DESIGN OF RADIAL HONEYCOMB LATTICES FOR IMPACT ENERGY ABSORPTION IN RADIOACTIVE MATERIALS PACKAGES
In this research we present a variation on the corrugation technique of honeycomb lattices, for cylindrical honeycombs, making them much easier to design for impact energy absorption in radioactive materials packages. This variation, termed radial honeycomb lattices, eliminates the residual strain and saddle effect. The use of honeycomb lattices provides advantages over the typically used foams. While foams are effective at absorbing impact energy, they can burn, their material properties are difficult to control, they can degrade over time, and procurement of raw materials can be dependent on timing of manufacturer batch runs. While the weaknesses of foam are strengths for honeycomb lattices, lattices have a different set of problems. Typically, when cylindrical honeycomb lattices are manufactured, they are manufactured flat, wrapped around a mandrel of the desired radius, and then brazed. This approach introduces residual strains, resulting in the saddle effect, which limits both the radial thickness and cylinder length. The radial honeycomb lattice approach presented here makes the design of thicker and longer cylinder honeycombs possible. To address these issues, we propose a honeycomb lattice which changes in cross-section from the inner to the outer radius of the cylinder. This causes the lattice to automatically wrap into a cylinder as it exits the corrugating gears. The theoretically bounding case, of a square cross-section at the inner radius, transitioning through a hexagon, to a diamond cross-section at the outer radius, results in a maximum thickness of approximately 41% of the inner radius. Full mathematical derivations, implemented in computer code, allow for the design of an entire radial honeycomb lattice, including the corrugating gears. To accomplish this only the radial thickness, inner cross-section shape, cell size, and nominal gear radius need to be specified, making the design of these lattices very efficient. Radial honeycomb lattice prototypes have demonstrated that the honeycomb does indeed wrap into a cylinder as intended, without the saddle effect, and can therefore be used to create thick-walled cylinders of any length. These design improvements make cylindrical honeycomb lattices much more accessible as a design element for radioactive materials packages
Johnson, William R. [Savannah River National Labor↗
Electronic structure simulations in the cloud computing environment
The transformative impact of modern computational paradigms and technologies, such as high-performance computing, quantum computing, and cloud computing, has opened up profound new opportunities for scientific simulations. Scalable computational chemistry is one beneficiary of this technological progress. The main focus of this paper is on the performance of various quantum chemical formulations, ranging from low-order methods to high-accuracy approaches, implemented in different computational chemistry packages, such as NWChem, NWChemEx, SPEC, ExaChem, and FLOSIC codes on the Azure Quantum Element (AQE) Microsoft cloud services. We pay particular attention to the intricate workflows for performing composite chemistry simulations, associated data curation, and mechanisms for accuracy assessment, as defined by the enabling cloud Computational Chemistry as a Service (CCaaS). Our focus also extends to Arrows' automated workflow for high throughput simulations. Finally, we provide a perspective on the role of cloud computing in supporting the mission of leadership computational facilities (LCFs).
computational chemistry, electronic structure, Clo↗
A mathematical framework for thermodynamic computing with applications to chemical reaction networks
The widespread adoption of energy-intensive computing applications has led to a growing need for energy-efficient computing approaches. Thermodynamic computing offers a promising approach for low-energy computation by leveraging the intrinsic computational capabilities of physical, chemical, or biological systems. However, the mathematical foundations of thermodynamic computing require further development to fully realize the potential energy efficiencies, as well as to assess factors like noise and operational speed. In this paper, we establish a mathematical framework for utilizing thermodynamic processes to perform fundamental operations, including addition, subtraction, multiplication, and division. We highlight the use of chemical reactions as potential computational units and explore synthetic chemical and biochemical systems as practical implementations. Additionally, we demonstrate how these principles can be applied to solving complex mathematical problems, such as ordinary differential equations (ODEs) and suggest the necessary components to implement the thermodynamic computing framework using chemical reactions based in a microfluidic device. This work enhances our understanding of thermodynamic processes for natural computing as a basis for scalable, energy-efficient computation in paradigm disruptive next-generation systems.
Cannon, William R. [Pacific Northwest National Lab↗
Hyperdimensional computing for image classification (HDC) v1.0
This is an implementation of the hyperdimensional computing technique to classify images. It consists of a python script that trains the system for a set of images from a set of images (dataset) specified by the user. This training produces hardware configuration parameters and description vectors that are then loaded into the hardware description part of the project. The hardware description consists of hardware described in Verilog (a well known language for this purpose) that is synthesizable and can be implemented in a real chip. This hardware received the training information generated by python, and then is able to accept images to produce answers for each image on which category (class) from the pre-=trained ones the image belongs to. The hardware and python training scripts are configurable and documented. The advantage of hyperdimensional computing is its robustness to errors and the easy capability for online learning (refining the training during inference slowly over time), which this implementation supports.
Michelogiannakis, Georgios [Lawrence Berkeley Nati↗
Simplifying activations with linear approximations in neural networks
A key step in Neural Networks is activation. Among the different types of activation functions, sigmoid, tanh, and others involve the usage of exponents for calculation. From a hardware perspective, exponential implementation implies the usage of Taylor series or repeated methods involving many addition, multiplication, and division steps, and as a result are power-hungry and consume many clock cycles. We implement a piecewise linear approximation of the sigmoid function as a replacement for standard sigmoid activation libraries. This approach provides a practical alternative by leveraging piecewise segmentation, which simplifies hardware implementation and improves computational efficiency. In this paper, we detail piecewise functions that can be implemented using linear approximations and their implications for overall model accuracy and performance gain. Our results show that for the DenseNet, ResNet, and GoogLeNet architectures, the piecewise linear approximation of the sigmoid function provides faster execution times compared to the standard TensorFlow sigmoid implementation while maintaining comparable accuracy. Specifically, for MNIST with DenseNet, accuracy reaches 99.91% (Piecewise) vs. 99.97% (Base) with up to 1.31x speedup in execution time. For CIFAR-10 with DenseNet, accuracy improves to 98.97% (Piecewise) vs. 99.40% (Base) while achieving 1.24x faster execution. Similarly, for CIFAR-100 with DenseNet, the accuracy is 97.93% (Piecewise) vs. 98.39% (Base), with a 1.18x execution time reduction. These results confirm the proposed method’s capability to efficiently process large-scale datasets and computationally demanding tasks, offering a practical means to accelerate deep learning models, including LSTMs, without compromising accuracy.
Activation function↗
Implementing a Laser Stabilization System for Trapping Ca+ Ions: an Internship Reflection
At Lawrence Livermore National Laboratory, I contributed to a project developing 3D printed micro ion traps for quantum computing. I designed, implemented, and assessed a laser stabilization system that locked lasers to the frequencies required for calibrating our High Finesse WS8-10 wavelength meter and for laser cooling and trapping of Ca+ ions. I also programmed a Python interface for hardware communication, data collection, and statistical analysis. Additionally, I optimized and aligned laser beam paths, and I implemented a closed digital feedback loop using Proportional, Integral, and Derivative (PID) control parameters. I analyzed both the long-term and short-term behavior of our locked lasers and adjusted PID parameters to enhance performance. Furthermore, I used COMSOL to simulate the capacitance of a linear Paul trap design and predict our trap’s performance. The procedures I developed for the interface, analysis, and simulations will continue to support the ion trapping experiment after my appointment. I strengthened my skills in data analysis, Python coding, and optical alignment for laser systems. My confidence as a researcher grew, particularly in communicating my research. This experience taught me the importance of careful planning and consideration in research and solidified my desire to continue exploring novel quantum technology as an undergraduate
42 ENGINEERING↗
Viscoelastic Modeling of Polymers in ALEGRA with the GAP Model
The Glassy Amorphous Polymer (GAP) model is a viscoelastic/plastic model developed at Los Alamos National Laboratory to accurately model a variety of polymers across a wide range of conditions and loading rates, including shock loading. In the present report we introduce and assess this model, newly implemented in the ALEGRA shock and multiphysics code, using a series of verification and application-related validation problems. We describe the mathematical and theoretical formulation of the model, as well as its implementation in ALEGRA, in detail. We provide verification results that assess the model implementation against published computational results, as well as validation results which we compare to existing experimental results when possible. These comparisons instill confidence in the implementation and indicate that the addition of the GAP model to the ALEGRA code provides users with a high-fidelity polymer modeling capability that is capable of recreating complex polymer phenomena.
36 MATERIALS SCIENCE↗
Neuromorphic intermediate representation: A unified instruction set for interoperable brain-inspired computing
Abstract Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine Learning. Despite a well-established mathematical foundation for neural dynamics, there exists numerous software and hardware solutions and stacks whose variability makes it difficult to reproduce findings. Here, we establish a common reference frame for computations in digital neuromorphic systems, titled Neuromorphic Intermediate Representation (NIR). NIR defines a set of computational and composable model primitives as hybrid systems combining continuous-time dynamics and discrete events. By abstracting away assumptions around discretization and hardware constraints, NIR faithfully captures the computational model, while bridging differences between the evaluated implementation and the underlying mathematical formalism. NIR supports an unprecedented number of neuromorphic systems, which we demonstrate by reproducing three spiking neural network models of different complexity across 7 neuromorphic simulators and 4 digital hardware platforms. NIR decouples the development of neuromorphic hardware and software, enabling interoperability between platforms and improving accessibility to multiple neuromorphic technologies. We believe that NIR is a key next step in brain-inspired hardware-software co-evolution, enabling research towards the implementation of energy efficient computational principles of nervous systems. NIR is available atneuroir.org
Science & Technology - Other Topics↗
Classical Benchmarks for Variational Quantum Eigensolver Simulations of the Hubbard Model
Simulating the Hubbard model is of great interest to a wide range of applications within condensed matter physics, however its solution on classical computers remains challenging in dimensions larger than one. The relative simplicity of this model, embodied by the sparseness of the Hamiltonian matrix, allows for its efficient implementation on quantum computers, and for its approximate solution using variational algorithms such as the variational quantum eigensolver. While these algorithms have been shown to reproduce the qualitative features of the Hubbard model, their quantitative accuracy in terms of producing true ground state energies and other properties, and the dependence of this accuracy on the system size and interaction strength, the choice of variational ansatz, and the degree of spatial inhomogeneity in the model, remains unknown. Here we present a rigorous classical benchmarking study, demonstrating the potential impact of these factors on the accuracy of the variational solution of the Hubbard model on quantum hardware, for systems with up to 32 qubits. We find that even when using the most accurate wavefunction ansätze for the Hubbard model, the error in its ground state energy and wavefunction plateaus for larger lattices, while stronger electronic correlations magnify this issue. Concurrently, spatially inhomogeneous parameters and the presence of off-site Coulomb interactions only have a small effect on the accuracy of the computed ground state energies. Our study highlights the capabilities and limitations of current approaches for solving the Hubbard model on quantum hardware, and we discuss potential future avenues of research.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Development of a Method for Shape Optimization for a Gas Turbine Fuel Injector Design Using Metal-Additive Manufacturing
Adjoint shape optimization has enabled physics-based optimal designs for aerodynamic surfaces. Additive manufacturing (AM) makes it possible to manufacture complex shapes. However, there has been a gap between optimal and manufacturable surfaces due to the inherent limitations of commercial computational fluid dynamics (CFD) codes to implement geometric constraints during adjoint computation. In such cases, the design sensitivities are exported and used to perform constrained shape modifications using parametric information stored in computer aided design (CAD) files to satisfy manufacturability constraints. However, modifying the design using adjoint methods in CFD solvers and performing constrained shape modification in CAD can lead to inconsistencies due to different shape parameterization schemes. This paper describes a method to enable the simultaneous optimization of the fluid domain and impose AM manufacturability constraints, resolving one of the key issues of geometry definition for isogeometric analysis. Similar to a grid convergence study, the proposed method verifies the consistencies between shape parameterization techniques present within commercial CAD and CFD software during mesh movement as a part of the adjoint shape optimization routine. By identifying the appropriate parameters essential to a shape optimization study, the error metric between the different parameterization techniques converges to demonstrate sufficient consistencies for justifiable exchange of data between CAD and CFD. For the identified shape optimization parameters, the error metric to measure the deviation between the two parameterization schemes lies within the AM laser-powder bed fusion (L-PBF) process tolerance. Additionally, comparison for subsequent objective function calculations between iterations of the optimization loop showed acceptable differences within 1% variation between the modified geometries obtained using the two parameterization schemes. This method provides justification for the use of multiphysics guided adjoint design sensitivities computed in CFD software to perform shape modifications in CAD to incorporate AM manufacturability constraints during the shape optimization loop such that optimal designs are also additively manufacturable.
33 ADVANCED PROPULSION SYSTEMS↗
Large-Message All-to-All Communication at Frontier Scale
Near the full scale of exascale supercomputers, latency can dominate the cost of all-to-all communication even for very large message sizes. We describe GPU-aware all-to-all implementations designed to reduce latency for large message sizes at extreme scales, and we present their performance using 65536 tasks (8192 nodes) on the Frontier supercomputer at the Oak Ridge Leadership Computing Facility. Two implementations perform best for different ranges of message size, and all outperform the vendor-provided MPI_Alltoall. Our results show promising options for improving implementations of MPI_Alltoall_init.
White, Trey [ORNL] (ORCID:000900052186075X)↗
Numerical Method Improvements in Griffin for Pebble Bed Reactors with a Focus on the Computation of Burnup
Griffin, a MOOSE (Multiphysics Object-Oriented Simulation Environment) based application targeting transient multiphysics modeling of advanced reactors, has been used recently to model both high-temperature gas-cooled and fluoride-salt-cooled pebble bed reactors (PBRs). Griffin uses deterministic methods for solving neutron transport and an Eulerian approach to model pebble movement. An Eulerian approach is also used to directly compute burnup instead of using a pass approach like other tools such as VSOP or PANGU. This work discusses verification efforts and numerical method improvements related specifically to the Eulerian modeling approach implemented for directly computing pebble burnup.
22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
Knowledge gaps for neuromorphic ionic computing
BACKGROUND Neuromorphic computing, inspired by the human brain’s ability to process information efficiently, represents a transformative approach to computation. In this Review, we explore the emerging field of neuromorphic ionic computing, which leverages ionic conduction and coupling to mimic neural processes, and identify critical knowledge gaps that must be addressed to realize its full potential. A central theme of the discussion is energy efficiency, a challenge that is both a limitation and an opportunity for this technology. Although complementary metal-oxide semiconductor (CMOS)–based neuromorphic technologies have made strides in scaling to billions of neurons and are increasingly applied in artificial intelligence and numerical computing, they remain orders of magnitude behind the human brain in terms of connectivity and energy efficiency. Neuromorphic ionic computing promises to overcome these limitations by leveraging the distinct architectural and operational principles of the brain. Our brains achieve this energy efficiency by combining several key features: using the same network elements to store and process information; using an incredibly complex and massively interconnected three-dimensional (3D) network of locally active elements that enables sparsity, robustness in the presence of noise, adaptation, and life-long learning; computing at comparatively low voltage and frequency; and last, taking advantage of a plethora of ions and small molecules as information carriers. Here, we propose that ionic computing systems can take advantage of similar features to achieve substantial gains in energy efficiency. ADVANCES Since the first reports of neuromorphic ionic behavior in nanofluidic channels, we have witnessed an explosion of reports that used ionic devices to produce synaptomimetic behaviors. However, achieving the goals of ionic computing requires not only implementation of much more sophisticated device functionality but also overcoming fundamental barriers in materials science, device architecture, and system integration. Current ionic devices, even those incorporating state-of-the-art materials, still suffer from limited functionality and stability, which restrict their performance and increase energy demands. Developing new materials with enhanced ionic properties is essential to overcome these limitations. Similarly, the design of neuromorphic devices must evolve to leverage the particular advantages of ionic processes. Existing architectures often follow a single-information-carrier logic of conventional electronics or are constructed of mesoscale fluidics, failing to capitalize on the energy-efficient mechanisms inherent to ionic systems or implement the multiple-information-carrier paradigm. Current neuromorphic chips focus on large-scale networks of analog memory elements based on mechanisms such as charge trap (flash), filamentary, phase change, or spin, which are built on top of a network of artificial CMOS neurons. Although such prototype networks have achieved impressive performance, it is difficult to envision how they can implement the key features such as massive connectivity, sophisticated plasticity, adaptability, sparsity, and “multichromatic” computing. Although small-scale devices have demonstrated promising results, integrating them, maintaining energy efficiency, and implementing temperature control as systems grow in complexity and size to computationally relevant scale remain major hurdles. Furthermore, interfacing neuromorphic ionic devices with existing computing technologies presents technical and conceptual challenges that will require innovative approaches that combine insights from neuroscience, materials science, and engineering. OUTLOOK Despite these challenges, the potential impact of neuromorphic ionic computing is profound with potential applications ranging from artificial intelligence to robotics and beyond. We also argue that neuromorphic ionic computing systems should not, at least in the beginning, compete with CMOS technologies but rather should focus on applications that require extreme energy efficiency with chemical and/or biological compatibility, such as biomedical applications (for example, brain-computer interfaces), environmental monitoring, and agricultural and food applications. Ultimately, this Review highlights the crucial role of interdisciplinary collaboration in advancing the field. Neuromorphic ionic computing is not merely a technological innovation; it represents a substantial step toward sustainable computation, aligning with the growing demand for energy-conscious solutions in a world that is increasingly reliant on data and computation.
Neuromorphic↗
Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference
In this work, we present two neural network approaches that approximate the solutions of static and dynamic conditional optimal transport (COT) problems. Both approaches enable conditional sampling and conditional density estimation, which are core tasks in Bayesian inference—particularly in the simulation-based (“likelihood-free”) setting. Our methods represent the target conditional distribution as a transformation of a tractable reference distribution. Obtaining such a transformation, chosen here to be an approximation of the COT map, is computationally challenging even in moderate dimensions. To improve scalability, our numerical algorithms use neural networks to parameterize candidate maps and further exploit the structure of the COT problem. Our static approach approximates the map as the gradient of a partially input convex neural network. It uses a novel numerical implementation to increase computational efficiency compared to state-of-the-art alternatives. Our dynamic approach approximates the conditional optimal transport via the flow map of a regularized neural ODE; compared to the static approach, it is slower to train but offers more modeling choices and can lead to faster sampling. We demonstrate both algorithms numerically, comparing them with competing state-of-the-art approaches, using benchmark datasets and simulation-based Bayesian inverse problems.
97 MATHEMATICS AND COMPUTING↗