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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

JACC: Leveraging HPC Meta-Programming and Performance Portability with the Just-in-Time and LLVM-based Julia Language

We present JACC (Julia for Accelerators), the first high-level, and performance-portable model for the just-in-time and LLVM-based Julia language. JACC provides a unified and lightweight front end across different back ends available in Julia, enabling the same Julia code to run efficiently on many HPC CPU and GPU targets. We evaluated the performance of JACC for common HPC kernels as well as for the most computationally demanding kernels used in applications, HPCCG, a supercomputing benchmark test for sparse domains, and HARVEY, a blood flow simulator to assist in the diagnosis and treatment of patients suffering from vascular diseases. We carried out the performance analysis on the most advanced US DOE supercomputers: Aurora, Frontier, and Perlmutter. Overall, we show that JACC has a negligible overhead versus vendor-specific solutions, reporting GPU speedups with no extra cost to programmability.

Valero-Lara, Pedro↗
DOE OSTI2026

PowerAnalytics.jl: User-Centric Power Systems Analysis in Julia

The National Laboratory of the Rockies recently released version 1 of PowerAnalytics.jl, an analysis module for the outputs of its popular open-source electrical power systems modeling platform Sienna. It features an extensible framework - based on the flexible selecting of components, the execution of arbitrary metrics on them, and a familiar DataFrames-based output interface with embedded metadata - to process results in the Sienna style while keeping the interface as simple as possible for non-Julia experts. Here, I describe the package and where it fits into the Sienna ecosystem, how I harnessed user-centered design and Julia features to achieve beginner friendliness without sacrificing performance and expressibility, and what lessons might be drawn from the package's design and implementation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

JuTrack: A Julia package for auto-differentiable accelerator modeling and particle tracking

Efficient accelerator modeling and particle tracking are key for the design and configuration of modern particle accelerators. In this work, we present JuTrack, a nested accelerator modeling package developed in the Julia programming language and enhanced with compiler-level automatic differentiation (AD). With the aid of AD, JuTrack enables rapid derivative calculations in accelerator modeling, facilitating sensitivity analyses and optimization tasks. Here we demonstrate the effectiveness of AD-derived derivatives through several practical applications, including sensitivity analysis of space-charge-induced emittance growth, nonlinear beam dynamics analysis for a synchrotron light source, and lattice parameter tuning of the future Electron-Ion Collider (EIC). Through the incorporation of automatic differentiation, this package opens up new possibilities for accelerator physicists in beam physics studies and accelerator design optimization.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Exascale Julia Grid Optimization

Simple Julia scrips for solving AC power flow, AC optimal power flow, and security-constrained AC optimal power flow. These scripts are intended for experimentation with different (possibly, new) methods, formulations, and settings for solving these power system problem. Their implementation, therefore, intentionally avoids excessive encapsulation, which makes other packages difficult to modify by non-developers.

Petra, Cosmin [Lawrence Livermore National Laborat↗
DOE OSTI2026

JACC.jl: Julia for Accelerators

CPU/GPU portable array, parallel_for/parallel_reduce in Julia for productive science. Funded by the US DOE Advanced Scientific Computing Research (ASCR).

Fackler, Philip [Oak Ridge National Laboratory (OR↗
DOE OSTI2025

Sunny.jl: A Julia Package for Spin Dynamics

Sunny is a Julia package designed to serve the needs of the quantum magnetism community. It supports the specification of a very broad class of spin models and a diverse suite of numerical solvers. These include powerful methods for simulating spin dynamics both in and out of equilibrium. Uniquely, it features a broad generalization of classical and semiclassical approaches to SU(N) coherent states, which is useful for studying systems exhibiting strong spin-orbit coupling or local entanglement effects. Sunny also offers a well-developed framework for calculating the dynamical spin structure factor, enabling direct comparison with scattering experiments. Ease of use is a priority, with tools for symmetry-guided modeling and interactive visualization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

RegularizedOptimization.jl: A Julia framework for regularized and nonsmooth optimization

RegularizedOptimization.jl is a Julia package that implements families of quadratic regularization and trust-region methods for solving the nonsmooth optimization problem $^{\textrm{minimize}}_{𝑥∈ℝ^𝑛}$ 𝑓(𝑥) + ℎ(𝑥) subject to 𝑐(𝑥) = 0, (1) where 𝑓 ∶ ℝ 𝑛 → ℝ and 𝑐 ∶ ℝ 𝑛 → ℝ 𝑚 are continuously differentiable, and ℎ ∶ ℝ 𝑛 → ℝ∪{+∞} is lower semi-continuous. The nonsmooth objective ℎ can be a regularizer, such as a sparsity inducing penalty, model simple constraints, such as 𝑥 belonging to a simple convex set, or can be a combination of both. All 𝑓, ℎ, and 𝑐 can be nonconvex. RegularizedOptimization.jl provides a modular and extensible framework for solving (1), and developing novel solvers. Currently, the following solvers are implemented: • Trust-region solvers TR and TRDH (Aravkin et al., 2022; Leconte & Orban, 2025) • Quadratic regularization solvers R2, R2DH and R2N (Aravkin et al., 2022; Diouane, Habiboullah, et al., 2024) • Levenberg-Marquardt solvers LM and LMTR (Aravkin et al., 2024) used when 𝑓 is a least-squares residual. • Augmented Lagrangian solver AL (De Marchi et al., 2023). All solvers rely on first derivatives of 𝑓 and 𝑐, and optionally on their second derivatives in the form of Hessian-vector products. If second derivatives are not available, quasi-Newton approximations can be used. In addition, the proximal mapping of the nonsmooth part ℎ, or adequate models thereof, must be evaluated. At each iteration, a step is computed by solving a subproblem of the form (1) inexactly, in which 𝑓, ℎ, and 𝑐 are replaced with appropriate models around the current iterate. The solvers R2, R2DH, and TRDH are particularly well suited to solve the subproblems, though they are general enough to solve (1). All solvers are allocation-free, so re-solves incur no additional allocations. To illustrate our claim of extensibility, a first version of the AL solver was implemented by an external contributor. Furthermore, a nonsmooth penalty approach, described in Diouane, Gollier, et al. (2024), is currently being developed, that relies on the library to efficiently solve the subproblems.

Gollier, Maxence [Polytechnique Montréal, QC (Cana↗
DOE OSTI2024

CurvilinearGrids.jl: A Julia package for curvilinear coordinate transformations

Finite-difference discretizations of partial differential equations are widespread throughout the scientific community. Oftentimes finite-differences are used to compute spatial gradients of fields on a discrete grid, which is typically a uniform or rectilinear Cartesian mesh. Arbitrary multidimensional geometry is difficult to discretize directly with finite differences, however, due to non-uniform grid spacing and non-orthogonality. Curvilinear coordinate transformations can be used as a strategy to enable arbitrary geometry. While these curvilinear transformations are straightforward, the governing PDEs require additional terms (metrics) and must adhere to strict conservation laws; these criteria complicate the application of the transformation and require careful implementation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

JACC.shared: Leveraging HPC Metaprogramming and Performance Portability for Computations That Use Shared Memory GPUs

In this work, we present JACC.shared, a new feature of Julia for ACCelerators (JACC), which is the performanceportable and metaprogramming model of the just-in-time and LLVM-based Julia language. This new feature allows JACC applications to leverage the high-performance computing (HPC) capabilities of high-bandwidth, on-chip GPU memory. Historically, exploiting high-bandwidth, shared-memory GPUs has not been a priority for high-level programming solutions. JACC.shared covers that gap for the first time, thereby providing a highlevel, portable, and easy-to-use solution for programmers to exploit this memory and supporting all current major accelerator architectures. Well-known HPC and AI workloads, such as multi/hyperspectral imaging and AI convolutions, have been used to evaluate JACC.shared on two exascale GPU architectures hosted by some of the most powerful US Department of Energy supercomputers: Perlmutter (NVIDIA A100) and Frontier (AMD MI250X). The performance evaluation reports speedup of up to 3.5× by adding only one line of code to the base codes, thus providing important accelerators in a simple, portable, and transparent way and elevating the programming productivity and performance-portability capabilities for Julia/JACC HPC, AI, and scientific applications.

Valero Lara, Pedro [ORNL] (ORCID:0000000214794310)↗
DOE OSTI2024

Large language model evaluation for high–performance computing software development

We apply AI-assisted large language model (LLM) capabilities of GPT-3 targeting high-performance computing (HPC) kernels for (i) code generation, and (ii) auto-parallelization of serial code in C ++, Fortran, Python and Julia. Our scope includes the following fundamental numerical kernels: AXPY, GEMV, GEMM, SpMV, Jacobi Stencil, and CG, and language/programming models: (1) C++ (e.g., OpenMP [including offload], OpenACC, Kokkos, SyCL, CUDA, and HIP), (2) Fortran (e.g., OpenMP [including offload] and OpenACC), (3) Python (e.g., numpy, Numba, cuPy, and pyCUDA), and (4) Julia (e.g., Threads, CUDA.jl, AMDGPU.jl, and KernelAbstractions.jl). Kernel implementations are generated using GitHub Copilot capabilities powered by the GPT-based OpenAI Codex available in Visual Studio Code given simple + + prompt variants. To quantify and compare the generated results, we propose a proficiency metric around the initial 10 suggestions given for each prompt. For auto-parallelization, we use ChatGPT interactively giving simple prompts as in a dialogue with another human including simple “prompt engineering” follow ups. Results suggest that correct outputs for C++ correlate with the adoption and maturity of programming models. For example, OpenMP and CUDA score really high, whereas HIP is still lacking. We found that prompts from either a targeted language such as Fortran or the more general-purpose Python can benefit from adding language keywords, while Julia prompts perform acceptably well for its Threads and CUDA.jl programming models. Finally, we expect to provide an initial quantifiable point of reference for code generation in each programming model using a state-of-the-art LLM. Overall, understanding the convergence of LLMs, AI, and HPC is crucial due to its rapidly evolving nature and how it is redefining human-computer interactions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Carbon Transport and Storage Planning and Viability Support Tools

The EDX disCO2ver Carbon Transport and Storage Planning and Viability Support Tools are made up of the Carbon Storage Planning Inquiry Tool (CS PlanIT, Justman et al. 2024) and the Carbon Storage Technical Viability Approach Support Tool (CS TVA). Together, these tools support data access to support understanding data availability to support planning efforts for carbon transport and storage. The Carbon Storage Planning Inquiry Tool (CS PlanIT) is an online web mapping application designed to help users explore, query, and evaluate multiple data layers to support and accelerate carbon storage resource and feasibility assessments and planning efforts. CS PlanIT currently contains a range of datasets associated with geologic, technical, and infrastructure factors. The data sets can be filtered geographically for an area of interest to update statistics and charts within the dashboard. The dashboard is divided into different sections called widgets, relating to different steps in the carbon storage planning process. The resources in this submission include a link to PlanIT, as well as a data catalog and link to user documentation. The original citation for the CS PlanIT tool, which has now been integrated into the toolset here, was: - Devin Justman, Scott Pantaleone, Maneesh Sharma, Lucy Romeo, Paige Morkner, CS PlanIT (Carbon Storage Planning Inquiry Tool) , 6/28/2024, https://edx.netl.doe.gov/dataset/cs-planit-carbon-storage-planning-inquiry-tool, DOI: 10.18141/2377953 The Carbon Storage Technical Viability Approach Support (CS TVA) Tool displays spatial data availability for the many components of Geologic Carbon Storage (GCS) technical viability assessment (Creason al 2025). Identifying sites suitable for GCS requires evaluating the intersection of myriad factors, including reservoir conditions, subsurface and surface hazards, infrastructure requirements, and energy community metrics. The technical viability of a site can only be confirmed for instances where all these factors have data available, and where those data support viability. Additional Resources related to the Technical Viability Assessment Tool: - Julia Mulhern, Casey White, Araceli Lara, Neyda Cordero Rodriguez, Zachary Jackson, Jacob Shay, Gabriel Creason, MacKenzie Mark-Moser, Paige Morkner, Kelly Rose, Carbon Storage Technical Viability Approach (CS TVA) Database, 3/26/2025, https://edx.netl.doe.gov/dataset/edx4ccs-carbon-storage-technical-viability-approach-database , DOI:10.18141/1984655 - Julia Mulhern, MacKenzie Mark-Moser, Gabriel Creason, Casey White, Araceli Lara, Neyda Cordero Rodriguez, Zach Jackson, Paige Morkner, Kelly Rose, Carbon Storage Technical Viability Approach (CS TVA) Matrix, 3/27/2025, https://edx.netl.doe.gov/dataset/carbon-storage-technical-viability-approach-cs-tva-matrix , DOI: 10.18141/2539979 - Gabriel Creason, Zach Jackson, Neyda Cordero Rodriguez, Julia Mulhern, Casey White, Araceli Lara, MacKenzie Mark-Moser, Paige Morkner, Kelly Rose, Carbon Storage Technical Viability Approach (CS TVA) Data Availability Results Database, 3/27/2025, https://edx.netl.doe.gov/dataset/carbon-storage-technical-viability-approach-cs-tva-data-availability-results-database, DOI:10.18141/2538557

Carbon storage↗
DOE OSTI2026

Graph-Based Modeling and Decomposition of Hierarchical Optimization Problems

We present a graph-theoretic modeling approach for hierarchical optimization that leverages the OptiGraph abstraction implemented in the Julia package Plasmo.jl. We show that the abstraction is flexible and can effectively capture complex hierarchical connectivity that arises from decision-making over multiple spatial and temporal scales (e.g., integration of planning, scheduling, and operations in manufacturing and infrastructures). We also show that the graph abstraction facilitates the conceptualization and implementation of decomposition and approximation schemes. Specifically, we propose a graph-based Benders decomposition (gBD) framework that enables the exploitation of hierarchical (nested) structures and that uses graph aggregation/partitioning procedures to discover such structures. In addition, we provide a Julia implementation of gBD, which we call PlasmoBenders.jl. We illustrate the capabilities using examples arising in the context of energy and power systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

ReactionMechanismSimulator.jl: A modern approach to chemical kinetic mechanism simulation and analysis

Abstract We present ReactionMechanismSimulator.jl (RMS), a modern differentiable software for the simulation and analysis of chemical kinetic mechanisms, including multiphase systems. RMS has already been applied to problems in combustion, pyrolysis, polymers, pharmaceuticals, catalysis, and electrocatalysis. RMS is written in Julia, making it easy to develop and allowing it to take advantage of Julia's extensive numerical computing ecosystem. In addition to its extensive library of optimized analytic Jacobians, RMS can generate and use Jacobians computed using automatic differentiation and symbolically generated analytic Jacobians. RMS is demonstrated to be faster than Cantera and Chemkin in several benchmarks. RMS also implements an extensive set of features for analyzing chemical mechanisms, including a library of easy‐to‐call plotting functions, molecular structure resolved flux diagram generation, crash analysis, traditional sensitivity analysis, transitory sensitivity analysis, and an automatic mechanism analysis toolkit. RMS implements efficient adjoint and parallel forward sensitivity analyses. We also demonstrate the ease of adding new features to RMS.

Johnson, Matthew S.↗
DOE OSTI2025

Enabling Scientific Applications with Performance-Portability and High-Productivity for Multi-GPU Programming with JACC.Multi

This work bridges the gap between multi-GPU computing and high-productivity, performance-portable programming solutions. Our goal is to enhance scientific applications with a productive and portable solution—program once, deploy everywhere—for multi-GPU programming with no cost to programmability. To accomplish this, we implemented JACC.Multi, which is part of the Julia for ACCelerators (JACC) performance-portable framework. JACC. Multi is the only high-level, portable metaprogramming solution that targets multi-GPU environments and is integrated in a readily accessible programming language (e.g., Julia language). With transparent GPU-to-GPU communication, JACC. Multi is optimized for scientific application workloads and is portable for NVIDIA and AMD accelerators. For the evaluation, we use two modern multi-GPU systems: Hudson, which features two NVIDIA H100 Hopper GPUs per node, and Frontier, which features four AMD MI250X GPUs per node, each with two Graphics Compute Dies (GCDs) for a total of eight GCDs per node. Additionally, as part of the evaluation, we use JACC (one GPU), MPI+JACC, and JACC. Multi codes that implement well-known and widely used scientific algorithms/kernels such as the conjugate gradient algorithm and an explicit forward Euler solver that requires GPU-to-GPU communication. Overall, JACC. Multi codes achieve better performance than MPI+JACC codes and significant speedups over JACC (one GPU), with up to 1.9× on Hudson and 6× on Frontier.

Valero Lara, Pedro [ORNL] (ORCID:0000000214794310)↗
DOE OSTI2024

Riemannian Optimization Applied to AC Optimal Power Flow: Preprint

The nonlinear, nonconvex AC optimal power flow problem is of growing importance as the nature of the power grid evolves. This problem can be difficult to solve for interior point methods. However, the advent of optimization algorithms over smooth Riemannian manifolds presents an alternative approach. The nonlinear, nonconvex constraints in the AC power flow problem form an embedded submanifold of Euclidean space. In this paper, the authors explore the performance of Riemannian optimization algorithms for the ACOPF problem where the optimization is performed directly on the AC power flow manifold. They demonstrate that these are viable computational alternatives to interior point methods. This is done by using Julia and the packages PowerModels.jl and Manopt.jl.

manifold optimization↗
DOE OSTI2024

Integrating ORNL’s HPC and Neutron Facilities with a Performance-Portable CPU/GPU Ecosystem

We explore the development of a performance-portable CPU/GPU ecosystem to integrate two of the US Department of Energy’s (DOE’s) largest scientific instruments, the Oak Ridge Leadership Computing facility and the Spallation Neutron Source (SNS), both of which are housed at Oak Ridge National Laboratory. We select a relevant data reduction workflow use-case to obtain the differential scattering cross-section from data collected by SNS’s CORELLI and TOPAZ instruments. We compare the current CPU-only production implementation using the Garnet Python multiprocess package based on the Mantid C++ framework against our proposed CPU/GPU implementation that uses the LLVM-based, just-in-time Julia scientific language and the JACC.jl performance-portable package. Two proxy apps were developed: (i) an app for extracting relevant Mantid kernels (MDNorm) in C++ and (ii) the Julia MiniVATES.jl miniapp. We present performance results for NVIDIA A100 and AMD MI100 GPUs and AMD EPYC 7513 and 7662 CPUs. The results provide insights for future generations of data reduction software that can embrace performance portability for an integrated research infrastructure across DOE’s experimental and computational facilities.

Hahn, Steven↗
DOE OSTI2025

OODG (OnlineOptDynaGrid) [SWR-24-07]

This is a repository for the code developed by NREL under the DynaGrid project. It is a Julia package for running a model-free real-time OPF algorithm to operate dynamic networked microgrids given network configuration results from the Maximal Load Delivery (MLD) problem in the PowerModelsONM.jl package. See Example.jl for a working example on how to use the package. Note: Julia package PowerModelsONM.jl is needed.

Poolla, Bala Kameshwar [National Renewable Energy ↗
DOE OSTI2026

GeneratorSE.jl

SAND2026-22941O GeneratorSE.jl is a Julia software package for analytical sizing of variable-speed wind turbine generators. It translates and maintains generator sizing methods from the NREL WISDEM GeneratorSE framework in a Julia package form. 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.

Michelen Strofer, Carlos [Sandia National Lab. (SN↗