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All sources NASA NTRS DOE OSTI

DOE OSTI2024

Recent Advances on Computational Modeling of Supported Single-Atom and Cluster Catalysts: Characterization, Catalyst–Support Interaction, and Active Site Heterogeneity

To satisfy the need for catalyst materials with high activity, selectivity, and stability for energy conversion, material design and discovery guided by theoretical insights are a necessity. In the past decades, the rise in theoretical investigations into the properties of catalyst materials, reaction mechanisms, and catalyst design principles has shed light on the catalysis field. Quantitative structure–activity relationships have been developed through incorporating spectroscopic simulations, electronic structure calculations, and reaction mechanistic studies. Here, in this review, we report the state-of-the-art computational approaches to catalyst materials characterization for supported single-atom and cluster catalysts utilizing spectroscopic simulations, i.e., XANES simulation, and material properties investigation via electronic-structure calculations. Furthermore, approaches regarding reaction mechanisms, focusing on active site heterogeneity, are also discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2014

ORBiT - The Oak Ridge Biosurveillance Toolkit

We describe ORBiT, the Oak Ridge Biosurveillance Toolkit, the objective of which is to provide 1) forecasting and early warning, and 2) an extensible data analytics platform for biosurveillance by enabling the use of traditional and non-traditional datasets, consisting of heterogeneous data types and modalities. ORBiT includes a data collection interface, analytic components, higher-order statistical tools, and a visual front-end with visual analytics. ORBiT is implemented as a distributed analytic platform consisting of a software stack atop of Hadoop and uses Titan, a distributed graph database as a backend for data storage.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

Partnership For Edge Physics Simulation

In this effort, we will extend our prior work as part of CPES (i.e., DART and DataSpaces) to support in-situ tight coupling between application codes that exploits data locality and core-level parallelism to maximize on-chip data exchange and reuse. This will be accomplished by mapping coupled simulations so that the data exchanges are more localized within the nodes. Coupled simulation workflows can more effectively utilize the resources available on emerging HEC platforms if they can be mapped and executed to exploit data locality as well as the communication patterns between application components. Scheduling and running such workflows requires an extended framework that should provide a unified hybrid abstraction to enable coordination and data sharing across computation tasks that run on the heterogeneous multi-core-based systems, and develop a data-locality based dynamic tasks scheduling approach to increase on-chip or intra-node data exchanges and in-situ execution. This effort will extend our prior work as part of CPES (i.e., DART and DataSpaces), which provided a simple virtual shared-space abstraction hosted at the staging nodes, to support application coordination, data sharing and active data processing services. Moreover, it will transparently manage the low-level operations associated with the inter-application data exchange, such as data redistributions, and will enable running coupled simulation workflow on multi-cores computing platforms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Addressing Load Imbalance in Bioinformatics and Biomedical Applications: Efficient Scheduling across Multiple GPUs

Computational bioinformatics and biomedical applications frequently contain heterogeneously sized units of work or tasks, for instance due to variability in the sizes of biological sequences and molecules. Variable-sized workloads lead to load imbalances in parallel implementations which detract from efficiency and performance. Many modern computing resources now have multiple graphics processing units(GPUs) per computer for acceleration. These multiple GPU resources need to be used efficiently through balancing of workloads across the GPUs. OpenMP is a portable directive-based parallel programming API used ubiquitously in bioscience applications to program CPUs; recently, the use of OpenMP directives for GPU acceleration has become possible. Here, motivated by experiences with imbalanced loads in GPU-accelerated bioinformatics applications, we address the load balancing problem using OpenMP task-to-GPU scheduling combined with OpenMP GPU offloading for multiply heterogeneous workloads – loads with both variable input sizes, and simultaneously, variable convergence rates for algorithms with a stochastic component – scheduled across multiple GPUs. We aim to develop strategies which are both easy to use and have lower overheads, and may be incorporated incrementally in existing programs which already make use of OpenMP for CPU-based threading in order to make use of multi-GPU computers. We test different combinations of input size variability and convergence rate variability, and characterize the effects of these different scenarios on the performance of scheduling strategies across multiple GPUs with OpenMP. We present several dynamic scheduling solutions for different parallel patterns, explore optimizations, and provide publicly available example computational kernels to make these strategies easy to use in programs. This work will enable application developers to efficiently and easily use multiple GPUs for imbalanced workloads found in bioinformatics and biomedical applications.

Thavappiragasam, Mathialakan↗

DOE OSTI2022

OpenMP Target Task: Tasking and Target Offloading on Heterogeneous Systems

This work evaluated the use of OpenMP tasking with target GPU offloading as a potential solution for programming productivity and performance on heterogeneous systems. Also, it is proposed a new OpenMP specification to make the implementation of heterogeneous codes simpler by using OpenMP target task, which integrates both OpenMP tasking and target GPU offloading in a single OpenMP pragma. As a test case, the authors used one of the most popular and widely used Basic Linear Algebra Subprogram Level-3 routines: triangular solver (TRSM). To benefit from the heterogeneity of the current high-performance computing systems, the authors propose a different parallelization of the algorithm by using a nonuniform decomposition of the problem. This work used target GPU offloading inside OpenMP tasks to address the heterogeneity found in the hardware. This new approach can outperform the state-of-the-art algorithms, which use a uniform decomposition of the data, on both the CPU-only and hybrid CPU-GPU systems, reaching speedups of up to one order of magnitude. The performance that this approach achieves is faster than the IBM ESSL math library on CPU and competitive relative to a highly optimized heterogeneous CUDA version. One node of Oak Ridge National Laboratory’s supercomputer, Summit, was used for performance analysis.

Valero Lara, Pedro↗

DOE OSTI2021

Isotopic signatures as tools to reconstruct the primordial architecture of the Solar System

The significant reorganization of the early Solar System due to giant planet migration has hampered our understanding of where planetary bodies formed. Previously employed proxies for reconstructing the primordial planetary architecture, such as water content or oxidation state, are complicated by post-accretionary processes. Furthermore we investigate basaltic achondrites for their nucleosynthetic isotope signatures in the elements neodymium (Nd) and zirconium (Zr) and show that they are—similar to previously investigated chondritic meteorites—characterized by a relative deficit in isotopes produced by the s-process of nucleosynthesis. Importantly, these data are well correlated with nucleosynthetic signatures observed in other elements, demonstrating that s-process matter was heterogeneously distributed throughout the early Solar System. By comparing these isotopic signatures with potential proxies for Solar System reconstruction and computer modeling, we here argue that this isotopic heterogeneity in bulk meteoritic materials is linked to the original heliocentric distance of formation. Such scaling of nucleosynthetic signatures with heliocentric distance could permit reconstruction of the primordial architecture of the Solar System by ‘cosmolocating’ the accretion orbits of meteoritic parent bodies as a function of incorporated s-process matter.

58 GEOSCIENCES↗

DOE OSTI2015

Costa - Introduction to 2015 Annual Report

In parallel with Sandia National Laboratories having two major locations (NM and CA), along with a number of smaller facilities across the nation, so too is the distribution of scientific, engineering and computing resources. As a part of Sandia’s Institutional Computing Program, CA site-based Sandia computer scientists and engineers have been providing mission and research staff with local CA resident expertise on computing options while also focusing on two growing high performance computing research problems. The first is how to increase system resilience to failure, as machines grow larger, more complex and heterogeneous. The second is how to ensure that computer hardware and configurations are optimized for specialized data analytical mission needs within the overall Sandia computing environment, including the HPC subenvironment. All of these activities support the larger Sandia effort in accelerating development and integration of high performance computing into national security missions. Sandia continues to both promote national R&D objectives, including the recent Presidential Executive Order establishing the National Strategic Computing Initiative and work to ensure that the full range of computing services and capabilities are available for all mission responsibilities, from national security to energy to homeland defense.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

X-ray micro computed tomography characterization of cellular SiC foams for their applications in chemical engineering

Open-cell SiC foams clearly are promising materials for continuous-flow chemical applications such as heterogeneous catalysis and distillation. X-ray micro computed tomography characterization of cellular β-SiC foams at a spatial voxel size of 13.6{sup 3} μm{sup 3} and the interpretation of morphological properties of SiC open-cell foams with implications to their transport properties are presented. Static liquid hold-up in SiC foams was investigated through in-situ draining experiments for the first time using the μ-CT technique providing thorough 3D information about the amount and distribution of liquid hold-up inside the foam. This will enable better modeling and design of structured reactors based on SiC foams in the future. In order to see more practical uses, μ-CT data of cellular foams must be exploited to optimize the design of the morphology of foams for a specific application. - Highlights: •Characterization of SiC foams using novel X-ray micro computed tomography. •Interpretation of structural properties of SiC foams regarding to their transport properties. •Static liquid hold-up analysis of SiC foams through in-situ draining experiments.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Bringing heterogeneity to the CMS software framework

The advent of computing resources with co-processors, for example Graphics Processing Units (GPU) or Field-Programmable Gate Arrays (FPGA), for use cases like the CMS High-Level Trigger (HLT) or data processing at leadership-class supercomputers imposes challenges for the current data processing frameworks. These challenges include developing a model for algorithms to offload their computations on the co-processors as well as keeping the traditional CPU busy doing other work. The CMS data processing framework, CMSSW, implements multithreading using the Intel Threading Building Blocks (TBB) library, that utilizes tasks as concurrent units of work. In this paper we will discuss a generic mechanism to interact effectively with non-CPU resources that has been implemented in CMSSW. In addition, configuring such a heterogeneous system is challenging. In CMSSW an application is configured with a configuration file written in the Python language. The algorithm types are part of the configuration. The challenge therefore is to unify the CPU and co-processor settings while allowing their implementations to be separate. We will explain how we solved these challenges while minimizing the necessary changes to the CMSSW framework. We will also discuss on a concrete example how algorithms would offload work to NVIDIA GPUs using directly the CUDA API.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2016

MOCUM Verification with a Heterogeneous MOX Whole Core C5G7 Benchmark Problem

Recent advancements in computer technology has allowed for the possibility to solve complex reactor problems without homogenization, but the present benchmark problems are all limited to small scale with few fuel assemblies and homogeneity at the core level. Therefore, to verify the capabilities of the advanced neutronics codes, heterogeneous full core model shall be developed. Recently several whole-core benchmark problems such as BEAVRS and VERA are developed with measured data to which computational codes can be compared. With a particular research interest on the MOX fuel, this study develops a two-dimensional (2-D) whole core MOX neutronics benchmark based on the C5G7 benchmark concept. The developed MOX core benchmark will then be used to verify the accuracy of the MOCUM code, which is developed using the method of characteristic (MOC) as the flux solver with an advanced geometry processor designed for arbitrary core geometries. MOCUM results will be compared against MCNP5 results as the reference solutions. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

D2NO: Efficient handling of heterogeneous input function spaces with distributed deep neural operators

Neural operators have been applied in various scientific fields, such as solving parametric partial differential equations, dynamical systems with control, and inverse problems. However, challenges arise when dealing with input functions that exhibit heterogeneous properties, requiring multiple sensors to handle functions with minimal regularity. To address this issue, discretization-invariant neural operators have been used, allowing the sampling of diverse input functions with different sensor locations. However, existing frameworks still require an equal number of sensors for all functions. We propose a novel distributed approach to further relax the discretization requirements and solve the heterogeneous dataset challenges. Our method involves partitioning the input function space and processing individual input functions using independent and separate neural networks. A centralized neural network is used to handle shared information across all output functions. This distributed methodology reduces the number of gradient descent back-propagation steps, improving efficiency while maintaining accuracy. Here, we demonstrate that the corresponding neural network is a universal approximator of continuous nonlinear operators and present three numerical examples to validate its performance.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Liquid-Phase Modeling in Heterogeneous Catalysis

The notion that solvents can affect the chemical reactivity has been prevalent in the homogeneous catalysis community, going back as far as 1863.(1) Remarkable changes in reaction rate have been reported in the seminal work of Menschutkin, who demonstrated a change in reaction rate constant up to a factor of 700 as a function of the solvents employed for the reaction of triethylamine with ethyl iodide at 373 K.(2) It is well-known nowadays that solvents can affect the reaction rate, reaction mechanism, and selectivity of chemical reactions occurring in condensed phase. While solvent effects usually lead to changes in reaction rates of up to 3 orders of magnitude, rate increases of 9 orders of magnitude have been reported.(3, 4) In homogeneous metal catalysis such as hydroformylation, hydrogenation, and cross-coupling reactions, solvent effects have been studied systematically and exploited for industrial applications.(5) Substantial solvent effects have also been reported in heterogeneous catalysis for several hydrogenation,(6-9) oxidation,(10-12) and electrochemical reactions (where electric field effects lead in addition to an electric double layer(13-16)). However, in heterogeneous catalysis, systematic studies of solvation effects are rare, and solvent effects are generally not well understood. In this context, we note that liquid-phase processing is highly desirable for process cost reduction and high product selectivity for the heterogeneously catalyzed conversion of highly functionalized lignocellulosic biomass, because the feedstocks contain significant amounts of water, are produced in aqueous-phase environments, and reactant molecules are highly water-soluble, reactive, and thermally unstable.(17-19) Processing at relatively low temperatures in condensed phase has therefore the potential to (1) minimize undesirable thermal degradation reactions, (2) increase the targeted product selectivity, and (3) facilitate the product separation from excess water because reaction products often contain less oxygen and are therefore less hydrophilic than the feed streams.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Uncertainty quantification and propagation in lithium-ion battery electrodes using bayesian convolutional neural networks

The complex nature of manufacturing processes stipulates electrodes to possess high variability with increased heterogeneity during production. X-ray computed tomography imaging has proved to be critical in visualizing the complicated stochastic particle distribution of as-manufactured electrodes in lithium-ion batteries. However, accurate prediction of their electrochemical performance necessitates precise evaluation of kinetic and transport properties from real electrodes. Image segmentation that characterizes voxels to particle/pore phase is often meticulous and fraught with subjectivity owing to a myriad of unconstrained choices and filter algorithms. Here we utilize a Bayesian convolutional neural network to tackle segmentation subjectivity and quantify its pertinent uncertainties. Otsu inter-variance and Blind/Referenceless Imaging Spatial Quality Evaluator are used to assess the relative image quality of grayscale tomograms, thus evaluating the uncertainty in the derived microstructural attributes. We analyze how image uncertainty is correlated with the uncertainties and magnitude of kinetic and transport properties of an electrode, further identifying pathways of uncertainty propagation within microstructural attributes. The coupled effect of spatial heterogeneity and microstructural anisotropy on the uncertainty quantification of transport parameters is also understood. This work demonstrates a novel methodology to extract microstructural descriptors from real electrode images through quantification of associated uncertainties and discerning the relative strength of their propagation, thus facilitating feedback to manufacturing processes from accurate image based electrochemical simulations.

25 ENERGY STORAGE↗

DOE OSTI2014

Constructing compact and effective graphs for recommender systems via node and edge aggregations

Exploiting graphs for recommender systems has great potential to flexibly incorporate heterogeneous information for producing better recommendation results. As our baseline approach, we first introduce a naive graph-based recommendation method, which operates with a heterogeneous log-metadata graph constructed from user log and content metadata databases. Although the na ve graph-based recommendation method is simple, it allows us to take advantages of heterogeneous information and shows promising flexibility and recommendation accuracy. However, it often leads to extensive processing time due to the sheer size of the graphs constructed from entire user log and content metadata databases. In this paper, we propose node and edge aggregation approaches to constructing compact and e ective graphs called Factor-Item bipartite graphs by aggregating nodes and edges of a log-metadata graph. Furthermore, experimental results using real world datasets indicate that our approach can significantly reduce the size of graphs exploited for recommender systems without sacrificing the recommendation quality.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Classic and Quantum Task-Based Intelligent Runtime for QIRs Running on Multiple QPUs

High-performance computing systems are rapidly evolving into heterogeneous platforms that fuse quantum accelerators with traditional classical processing units (CPUs) and graphical processing units (GPUs). This convergence calls for runtimes capable of managing both classical and quantum workloads in a unified manner. We introduce an intelligent, task-based runtime that marries the Intelligent RuntIme System (IRIS) asynchronous scheduler with a quantum programming stack through the Quantum Intermediate Representation Execution Engine (QIR-EE). Our design allows programs written in the quantum intermediate representation (QIR) to be dispatched concurrently to a variety of back-ends, including multiple quantum simulators and nascent quantum processors, enabling genuine hybrid execution on a single node. To illustrate its practicality, we partition a 4-qubit and 20-qubit circuit into three sub-circuits using quantum circuit cutting via the QCut library. Each sub-circuit is simulated independently by the QIR-EE driver within IRIS, after which a classical post-processing step merges the simulation results to recover the outcome of the original full-circuit computation. This case study demonstrates how finer task granularity can enable the parallel execution and lower the simulation burden per quantum task while preserving overall accuracy, highlighting the feasibility of our hybrid approach.

Miniskar, Narasinga Rao [ORNL] (ORCID:000000018259↗

DOE OSTI2015

Peridynamic Multiscale Finite Element Methods

The problem of computing quantum-accurate design-scale solutions to mechanics problems is rich with applications and serves as the background to modern multiscale science research. The prob- lem can be broken into component problems comprised of communicating across adjacent scales, which when strung together create a pipeline for information to travel from quantum scales to design scales. Traditionally, this involves connections between a) quantum electronic structure calculations and molecular dynamics and between b) molecular dynamics and local partial differ- ential equation models at the design scale. The second step, b), is particularly challenging since the appropriate scales of molecular dynamic and local partial differential equation models do not overlap. The peridynamic model for continuum mechanics provides an advantage in this endeavor, as the basic equations of peridynamics are valid at a wide range of scales limiting from the classical partial differential equation models valid at the design scale to the scale of molecular dynamics. In this work we focus on the development of multiscale finite element methods for the peridynamic model, in an effort to create a mathematically consistent channel for microscale information to travel from the upper limits of the molecular dynamics scale to the design scale. In particular, we first develop a Nonlocal Multiscale Finite Element Method which solves the peridynamic model at multiple scales to include microscale information at the coarse-scale. We then consider a method that solves a fine-scale peridynamic model to build element-support basis functions for a coarse- scale local partial differential equation model, called the Mixed Locality Multiscale Finite Element Method. Given decades of research and development into finite element codes for the local partial differential equation models of continuum mechanics there is a strong desire to couple local and nonlocal models to leverage the speed and state of the art of local models with the flexibility and accuracy of the nonlocal peridynamic model. In the mixed locality method this coupling occurs across scales, so that the nonlocal model can be used to communicate material heterogeneity at scales inappropriate to local partial differential equation models. Additionally, the computational burden of the weak form of the peridynamic model is reduced dramatically by only requiring that the model be solved on local patches of the simulation domain which may be computed in parallel, taking advantage of the heterogeneous nature of next generation computing platforms. Addition- ally, we present a novel Galerkin framework, the 'Ambulant Galerkin Method', which represents a first step towards a unified mathematical analysis of local and nonlocal multiscale finite element methods, and whose future extension will allow the analysis of multiscale finite element methods that mix models across scales under certain assumptions of the consistency of those models.

42 ENGINEERING↗

NASA NTRS1983

ECUT: Energy Conversion and Utilization Technologies program. Heterogeneous catalysis modeling program concept

Insufficient theoretical definition of heterogeneous catalysts is the major difficulty confronting industrial suppliers who seek catalyst systems which are more active, selective, and stable than those currently available. In contrast, progress was made in tailoring homogeneous catalysts to specific reactions because more is known about the reaction intermediates promoted and/or stabilized by these catalysts during the course of reaction. However, modeling heterogeneous catalysts on a microscopic scale requires compiling and verifying complex information on reaction intermediates and pathways. This can be achieved by adapting homogeneous catalyzed reaction intermediate species, applying theoretical quantum chemistry and computer technology, and developing a better understanding of heterogeneous catalyst system environments. Research in microscopic reaction modeling is now at a stage where computer modeling, supported by physical experimental verification, could provide information about the dynamics of the reactions that will lead to designing supported catalysts with improved selectivity and stability.

Voecks, G. E.↗

DOE OSTI2020

A numerical-homogenization based phase-field fracture modeling of linear elastic heterogeneous porous media

Most porous media, such as geomaterials and biomaterials are highly heterogeneous in nature, and they contain large variations of microscopic pore structures, such as pore sizes, pore distribution, and pore shapes. The oscillation of microscopic structures is a substantial challenge in theoretical characterization and is usually ignored in continuous modeling. However, mechanical behavior of porous media such as deformation and failure, are essentially impacted by the microscopic heterogeneity which needs to be considered in modeling a porous media. Here, this research proposes a numerical modeling framework with a capability to investigate the effect of microscopic heterogeneity on the macroscopic fracture behavior in porous media by using a numerical homogenization technique, combined with the phase-field fracture modeling method. This numerical modeling strategy computes a homogenized elasticity tensor based on microscopic heterogeneous pore structures heterogenous porous domain by solving boundary value problems at microscopic domain. The strain energy and subsequent propagation of macroscopic fractures will be updated using homogenized stiffness information. Using this numerical scheme, the microscopic pore structure’s impact on the fracture behavior through the homogenized elastic tensor will be taken into account. This multiscale technique is benchmarked against classical problems. Finally, the results highlight the importance of the underlying pore structure and reveal that both fracture strength and propagation path can be influenced by the microscopic heterogeneity.

36 MATERIALS SCIENCE↗