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DOE OSTI2023

SIDIS-RC EvGen: A Monte-Carlo event generator of semi-inclusive deep inelastic scattering with the lowest-order QED radiative corrections

SIDIS-RC EvGen is a C++ standalone Monte-Carlo event generator for studies of semi-inclusive deep inelastic scattering (SIDIS) processes at medium to high lepton beam energies. In particular, the generator contains binary and library components for generating SIDIS events and calculating cross sections for unpolarized or longitudinally polarized beam and unpolarized, longitudinally or transversely polarized target. The structure of the generator incorporates transverse momentum-dependent parton distribution and fragmentation functions, whereby we obtain multi-dimensional binned simulation results, which will facilitate the extraction of important information about the three-dimensional nucleon structure from SIDIS measurements. In order to build this software, we have used recent elaborate QED calculations of the lowest-order radiative effects, applied to the leading order Born cross section in SIDIS. Here, in this paper, we provide details on the theoretical formalism as well as the construction and operation of SIDIS-RC EvGen, e.g., how we handle the event generation process and perform multi-dimensional integration. We also provide example programs, flowcharts, and numerical results on azimuthal transverse single-spin asymmetries.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Stochastic Programming Approach for Resource Selection under Demand Uncertainty

Cost-efficient selection and scheduling of a subset of geographically distributed resources to meet the demands of a scientific workflow is a challenging problem. The problem is exacerbated by uncertainties in demand and availability of resources. In this paper, we present a stochastic optimization based framework for robust decision making in the selection of distributed resources over a planning horizon under demand uncertainty. We present a novel two-stage stochastic programming model for resource selection, and implement an L-shaped decomposition algorithm to solve this model. A Sample Average Approximation algorithm is integrated to enable stochastic optimization to solve problems with a large number of scenarios. Using the metric of stochastic solution, we demonstrate up to 30% cost reduction relative to solutions without explicit consideration of demand uncertainty for a 24-month problem. We also demonstrate up to 54% cost reduction relative to a previously developed solution for a 36-month problem. We further argue that the composition of resources selected is superior to solutions computed without explicit consideration of uncertainties. Given the importance of resource selection and scheduling of complex scientific workflows, especially in the context of commercial cloud computing, we believe that our novel stochastic programming framework will benefit many researchers as well as users of distributed computing resources.

scientific workflows, task scheduling, optimizatio↗

DOE OSTI2024

MolSym : A Python package for handling symmetry in molecular quantum chemistry

A consideration of the point group symmetry of molecules is often advantageous from a computational efficiency standpoint and sometimes necessary for the correct treatment of chemical physics problems. Many modern electronic structure software packages include a treatment of symmetry, but these are sometimes incomplete or unusable outside of that program’s environment. Therefore, we have developed the MolSym package for handling molecular symmetry and its associated functionalities to provide a platform for including symmetry in the implementation and development of other methods. Features include point group detection, molecule symmetrization, arbitrary generation of symmetry element sets and character tables, and symmetry adapted linear combinations of real spherical harmonic basis functions, Cartesian displacement coordinates, and internal coordinates. We present some of the advantages of using molecular symmetry as achieved by MolSym, particularly with respect to Hartree–Fock theory, and the reduction of finite difference displacements in gradient/Hessian computations. Furthermore, this package is designed to be easily integrated into other software development efforts and may be extended to further symmetry applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Summary of Thermodynamic Data Integration (M4SF-23LL010301052)

This progress report (Level 4 Milestone Number M4SF-23LL010301052) summarizes research conducted at Lawrence Livermore National Laboratory (LLNL) within the Argillite Activity Number M4SF-23LL010301052. SUPCRTNE is being developed as the primary engine for thermodynamic database development in support of geologic disposal of high-level nuclear waste. As used here, “SUPCRT” refers to both a computer program and its supporting database. Additional letters or numbers refer to specific variants (SUPCRT92, Johnson et al., 1992; SUPCRTBL, Zimmer et al., 2016; and SUPCRTNE). A SUPCRT database contains the data required to calculate the thermodynamic properties of solid, gas, and aqueous species over a wide range of temperature and pressure. Normally SUPCRT is used to create a higher-level data base that directly supports modeling and simulation codes such as EQ3/6, GWB, PHREEQC, and PFLOTRAN.

58 GEOSCIENCES↗

DOE OSTI2024

PETSc/TAO Users Manual V.3.21

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

The E3SM Diagnostics Package (E3SM Diags v2.7): a Python-based diagnostics package for Earth system model evaluation

Abstract. The E3SM Diagnostics Package (E3SM Diags) is a modern, Python-based Earth system model (ESM) evaluation tool (with Python module name e3sm_diags), developed to support the Department of Energy (DOE) Energy Exascale Earth System Model (E3SM). E3SM Diags provides a wide suite of tools for evaluating native E3SM output, as well as ESM data on regular latitude–longitude grids, including output from Coupled Model Intercomparison Project (CMIP) class models. E3SM Diags is modeled after the National Center for Atmospheric Research (NCAR) Atmosphere Model Working Group (AMWG, 2022) diagnostics package. In its version 1 release, E3SM Diags included a set of core essential diagnostics to evaluate the mean physical climate from model simulations. As of version 2.7, more process-oriented and phenomenon-based evaluation diagnostics have been implemented, such as analysis of the quasi-biennial oscillation (QBO), the El Niño–Southern Oscillation (ENSO), streamflow, the diurnal cycle of precipitation, tropical cyclones, ozone and aerosol properties. An in situ dataset from DOE's Atmospheric Radiation Measurement (ARM) program has been integrated into the package for evaluating the representation of simulated cloud and precipitation processes. This tool is designed with enough flexibility to allow for the addition of new observational datasets and new diagnostic algorithms. Additional features include customizable figures; streamlined installation, configuration and execution; and multiprocessing for fast computation. The package uses an up-to-date observational data repository maintained by its developers, where recent datasets are added to the repository as they become available. Finally, several applications for the E3SM Diags module were introduced to fit a diverse set of use cases from the scientific community.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Decomposing a renewable energy design and dispatch model

We address a mixed-integer linear programming model which selects a cost-minimizing set of available technologies with which to design a renewable energy system and prescribe their associated dispatch decisions. Realistically sized instances of such models pose computational challenges. To this end, we develop a Lagrangian heuristic based on a decomposition methodology which partitions the model into blocks and optimizes these more manageable, smaller subproblems. It also provides a lower bound to assess solution quality. In conclusion, we apply this methodology to the National Renewable Energy Laboratory's Renewable Energy Integration and Optimization (REopt TM ) model to generate near-optimal solutions to realistic instances containing, on average, approximately 300,000 variables and at least as many constraints, with a mean 30% optimality gap improvement using a five-minute solution time limit, compared to directly solving the original monolith.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

4-Clique network minor embedding for quantum annealers

Quantum annealing is a quantum algorithm for computing solutions to combinatorial optimization problems. This study proposes a method for minor embedding optimization problems onto sparse quantum annealing hardware graphs called 4-clique network minor embedding. This method is in contrast to the standard minor embedding technique of using a path of linearly connected qubits in order to represent a logical variable state. The 4-clique minor embedding is possible on Pegasus graph connectivity, which is the native hardware graph for some of the current D-Wave quantum annealers. The Pegasus hardware graph contains many cliques of size 4, making it possible to form a graph composed entirely of paths of connected 4-cliques on which a problem can be minor-embedded. The 4-clique chains come at the cost of additional qubit usage on the hardware graph, but they allow for stronger coupling within each chain, thereby increasing chain integrity, reducing chain breaks, and allow for greater usage of the available energy scale for programming logical problem coefficients on current quantum annealers. The 4-clique minor embedding technique is compared with the standard linear path minor embedding with experiments on two D-Wave quantum annealing processors with Pegasus hardware graphs. We show proof-of-concept experiments where the 4-clique minor embeddings can use weak chain strengths while successfully carrying out the computation of minimizing random all-to-all spin glass problem instances. Published by the American Physical Society 2024

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2012

Implicit high-order method for calculating rarefied gas flow in a planar microchannel

An efficient numerical algorithm for calculating rarefied gas flows in planar microchannels on the basis of the Boltzmann kinetic equation with the linearized S-model collision integral is presented. The algorithm consists of a high-order spatial discretisation on unstructured meshes, conservative procedure to calculate macroscopic quantities and efficient one-step implicit time evolution method. It therefore works across all flow regimes from the free-molecular to nearly continuum ones and provides rapid convergence to steady state solutions. The parallel implementation is provided via MPI programming paradigm. The performance of the method is illustrated by computing the flow for length to the width ratios for up to 10{sup 3} in the wide range of Knudsen numbers. Numerical estimates of efficiency of implicit time marching and parallel performance as well as convergence studies in both physical and velocity spaces are provided. The end effects and other essentially two-dimensional features of the flow are analysed and a detailed comparison with the case of an infinitely long channel is carried out. The presented numerical data can be used as reference for future studies of nonlinear flows as well as for comparison with other numerical approaches and flow models.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Minimax Program Terminal Control in Two-Level Hierarchic Nonlinear Discrete Dynamical System

We consider a discrete dynamical system consisting of a number of controllable objects whose dynamics is governed by vector nonlinear discrete recurrent relations with control parameters and perturbations (noises). In such system, two levels of making management decisions are distinguished: the dominating (first) level and the subordinate (second) level, which have different criteria of functioning. These levels are integrated by certain information and control relations. We consider the problem of two-level minimax program terminal control in a discrete dynamical system and propose its mathematical statement and a general scheme of its solution.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Simulation Dataset - Input files

The input data that are required to run the model are divided into fundamental data and advanced data. The fundamental data are essential to run the model, while the advanced data allow optional model features to be activated. The names of all input data files are to be entered as ASCII characters in a tabular format. Spaces, commas, or tabs can separate numeric fields, the use of any other special visible or hidden characters must be avoided. It may be useful to note that all files are generally read in using standard free-format style FORTRAN READ statements. The input data files can be generated/modified using any standard editor, spreadsheet or word processor (in non-document mode), given that the above guidelines are complied with. However, the inclusion of special formatting characters and the insertion of blank lines must be avoided. The input data files may also be generated through the use of special purpose translation programs that convert the input data files that were initially generated for another traffic simulation or transportation planning model into an INTEGRATION format.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

PETSc/TAO Users Manual (Rev. 3.19)

This manual describes the use of the Portable, Extensible Toolkit for Scientific Computation (PETSc) and the Toolkit for Advanced Optimization (TAO) for the numerical solution of partial differential equations and related problems on high-performance computers. PETSc/TAO is a suite of data structures and routines that provide the building blocks for the implementation of large-scale application codes on parallel (and serial) computers. PETSc uses the MPI standard for all distributed memory communication. PETSc/TAO includes a large suite of parallel linear solvers, nonlinear solvers, time integrators, and opti mization that may be used in application codes written in Fortran, C, C++, and Python (via petsc4py; see Getting Started). PETSc provides many of the mechanisms needed within parallel application codes, such as parallel matrix and vector assembly routines. The library is organized hierarchically, enabling users to employ the level of abstraction that is most appropriate for a particular problem. By using techniques of object-oriented programming, PETSc provides enormous flexibility for users. PETSc is a sophisticated set of software tools; as such, for some users it initially has a much steeper learning curve than packages such as MATLAB or a simple subroutine library. In particular, for individuals without some computer science background, experience programming in C, C++, python, or Fortran and experience using a debugger such as gdb or lldb, it may require a significant amount of time to take full advantage of the features that enable efficient software use. However, the power of the PETSc design and the algorithms it incorporates may make the efficient implementation of many application codes simpler than “rolling them” yourself. For many tasks a package such as MATLAB is often the best tool; PETSc is not intended for the classes of problems for which effective MATLAB code can be written. There are several packages, built on PETSc, that may satisfy your needs without requiring directly using PETSc. We recommend reviewing these packages functionality before starting to code directly with PETSc. PETSc can be used to provide a “MPI parallel linear solver” in an otherwise sequential, or OpenMP parallel code. This approach cannot provide extremely large improvements in the application time by utilizing large numbers of MPI processes but can still improve the performance. Certainly all parts of a previously sequential code need not be parallelized but the matrix generation portion must be parallelized to expect true scalability to large numbers of MPI processes. See PCMPI for details on how to utilize the PETSc MPI linear solver server. Since PETSc is under continued development, small changes in usage and calling sequences of routines will occur. PETSc has been supported for twenty-five years; see mailing list information on our website for information on contacting support.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

A Hierarchical Task Scheduler for Heterogeneous Computing

Heterogeneous computing is one of the future directions of HPC. Task scheduling in heterogeneous computing must balance the challenge of optimizing the application performance and the need for an intuitive interface with the programming run-time to maintain programming portability. The challenge is further compounded by the varying data communication time between tasks. This paper proposes RANGER, a hardware-assisted task-scheduling framework. By integrating RISC-V cores with accelerators, the RANGER scheduling framework divides scheduling into global and local levels. At the local level, RANGER further partitions each task into fine-grained subtasks to reduce the overall makespan. At the global level, RANGER maintains the coarse granularity of the task specification, thereby maintaining programming portability. The extensive experimental results demonstrate that RANGER achieves a 12.7× performance improvement on average, while only requires 2.7% of area overhead.

Miniskar, Narasinga Rao↗

DOE OSTI2023

Equation-based and data-driven modeling: Open-source software current state and future directions

Here, a review of current trends in scientific computing reveals a broad shift to open-source and higher-level programming languages such as Python and growing career opportunities over the next decade. Open-source modeling tools accelerate innovation in equation-based and data-driven applications. Significant resources have been deployed to develop data-driven tools (PyTorch, TensorFlow, Scikit-learn) from tech companies that rely on machine learning services to meet business needs while keeping the foundational tools open. Open-source equation-based tools such as Pyomo, CasADi, Gekko, and JuMP are also gaining momentum according to user community and development pace metrics. Integration of data-driven and principles-based tools is emerging. New compute hardware, productivity software, and training resources have the potential to radically accelerate progress. However, long-term support mechanisms are still necessary to sustain the momentum and maintenance of critical foundational packages.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Implementation of the Molecular Electrostatic Potential over Graphics Processing Units

The molecular electrostatic potential (MEP) generated by quantum chemistry methods and Gaussian functions is evaluated over graphics processing units (GPUs). This implementation is based on full-range Rys polynomials with nodes and weights obtained in each thread of a GPU. For high angular moments, the corresponding integral is solved using a one dimension vertical recurrence relation. Thus, we computed the MEP with minimal approximations. We show that this implementation is stable and very efficient since the time consumed over GPUs is quite small compared with similar implementations over CPUs. The implementation was done by using CUDA-C programming techniques within the Graphics Processing Units for Atoms and Molecules (GPUAM) project, which has been designed to analyze quantum chemistry fields over heterogeneous computational resources. With this new scalar field GPUAM is a useful application for the quantum chemistry community, in particular for people interested in chemical reactivity analysis.

Cruz-Monterrosas, Julio-Cesar↗

DOE OSTI2017

Uncertainty Quantification and Sensitivity Analysis for Coupled VERA-CS and FRAPCON

The current nuclear power plant fleet in the U.S. is facing numerous challenges economically ranging from low natural gas price to the rapidly growing renewable energy sources such as wind and solar power. In order for the nuclear industry to keep the viability of its operating fleet, new innovations have to be introduced to lower the costs to operate the fleet. One area that could contribute to the cost reduction is to lower the fuel cycle cost and improve the plant safety through initiatives such as higher burnup fuel and accident tolerant fuel. At the same time, the nuclear industry has to address future challenges on the horizon such as load following and other flexible operating strategies as well as those associated with new regulations such as 10 CFR 50.69 and 10 CFR50.46c etc. All these require coupled analysis of core neutronics, thermal hydraulics and fuel performance, which necessitates the development of a multi-physics toolkit. Since different models, assumptions, and fidelities have gone into various computer codes, how to consistently propagate uncertainties in a multi-physics environment warrants careful evaluation. The U.S. DOE's Light Water Reactor Sustainability (LWRS) Program initiated a project to develop analytical capabilities to support the industry to address these challenges. The general idea behind the initiative was the development of an integrated multiphysics evaluation model. This integrated evaluation model is called LOTUS which stands for LOCA Toolkit for the U.S. Light Water Reactors. The focus of LOTUS is to establish the automation of the interface between the five disciplines. LOTUS framework ensures that uncertainties can be easily propagated to solve the problems forward and backward (inverse) with large numbers of simulations. The objective of this work is to perform uncertainty quantification (UQ) and sensitivity analysis (SA) on a coupling of CASL's Virtual Environment for Reactor Applications-Core Simulator (VERA-CS) and NRC's fuel performance code FRAPCON. The work presented herein is a subset of the overall LOTUS framework. The figures of merit (FOM) selected for analysis are minimum departure from nucleate boiling ratio (MDNBR), maximum fuel centerline temperature (MFCT), and the gap conductance at peak power (GCPP). All FOM are obtained at the end of the of a depletion case for a half cycle lasting 263 days. The minimum is taken over all spatial location for each pin at the end of the half cycle. This value is calculated in VERACS alone and has been analyzed in previous work by Brown and Zhang. The MFCT is available in both VERA-CS and FRAPCON, but is calculated via different methods (see Model Inconsistencies section for more details). For this work, only the FRAPCON values are reported due to the code's maturity from extensive validation in fuel performance. Previous work includes SA on a standalone nuclear fuel performance code VTT-modified FRAPCON- 3.4 by Ikonen. The GCPP is the gap conductance at the location corresponding to maximum power in the fuel rod. This value is available in FRAPCON alone and highlights the capability of LOTUS to obtain unique fuel performance metrics for each pin in a core simulation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2017

Nuclear Computational Low Energy Initiative (NUCLEI)

This is the Final Report for Ohio State University for the NUCLEI SciDAC3 project. In addition to the local OSU summary given here, we have attached a global NUCLEI summary and a list of papers and talks by the full collaboration, with OSU contributions marked in red. The NUCLEI project, as defined by the scope of work, developed, implemented and ran codes for largescale computations of many topics in lowenergy nuclear physics. Physics studied included the properties of nuclei and nuclear decays, nuclear structure and reactions, and the properties of nuclear matter. The computational techniques used included Quantum Monte Carlo, Configuration Interaction, Coupled Cluster, InMedium SRG, and Density Functional methods. The research program emphasized areas of high interest to current and possible future DOE nuclear physics facilities, including ATLAS and FRIB (nuclear structure and reactions, and nuclear astrophysics), TJNAF (neutron distributions in nuclei, few body systems, and electroweak processes), NIF (thermonuclear reactions), MAJORANA and FNPB (neutrinoless doublebeta decay and physics beyond the Standard Model), and LANSCE (fission studies). Research at Ohio State University played an integral role in NUCLEI by providing input microscopic interactions and operators needed for ab initio structure and reaction calculations including uncertainty quantification, independent ab initio calculations using the inmedium similarity renormalization group (IMSRG), and developing microscopic constraints for the nextgeneration energy density functionals (EDF). Much of the theoretical work at OSU was closely coordinated with the theory team at Michigan State University. Although the OSU group did not usually directly develop codes for the highest performance computing components of NUCLEI, we provided needed inputs for much of the effort. In particular, our work contributes to the “Validated Nuclear Interactions” task as well as to both of the “Structure and Reactions” tasks.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2011

From Multileg Loops to Trees (by-passing Feynman's Tree Theorem)

We illustrate a duality relation between one-loop integrals and single-cut phase-space integrals. The duality relation is realised by a modification of the customary +i0 prescription of the Feynman propagators. The new prescription regularizing the propagators, which we write in a Lorentz covariant form, compensates for the absence of multiple-cut contributions that appear in the Feynman Tree Theorem. The duality relation can be extended to generic one-loop quantities, such as Green's functions, in any relativistic, local and unitary field theories. The physics program of LHC requires the evaluation of multi-leg signal and background processes at next-to-leading order (NLO). In the recent years, important efforts have been devoted to the calculation of many 2 {yields} 3 processes and some 2 {yields} 4 processes. We have recently proposed a method to compute multi-leg one-loop cross sections in perturbative field theories. The method uses combined analytical and numerical techniques. The starting point of the method is a duality relation between one-loop integrals and phase-space integrals. In this respect, the duality relation has analogies with the Feynman's Tree Theorem (FTT). The key difference with the FTT is that the duality relation involves only single cuts of the one-loop Feynman diagrams. In this talk, we illustrate the duality relation, and discuss its correspondence, similarities, and differences with the FTT.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗