Computer Physics Communications

This paper presents the Parallel Coupler for Multimodel Simulations (PCMS), a new GPU accelerated generalized framework for coupling simulation codes on leadership class supercomputers. PCMS includes distributed control and field mapping methods for up to five dimensions. For field mapping, PCMS can utilize discretization and field information to accommodate physics constraints. PCMS is demonstrated with a coupling of the gyrokinetic microturbulence code XGC with a Monte Carlo neutral transport code DEGAS2 and with a 5D distribution function coupling of an energetic particle transport code (GNET) to a gyrokinetic microturbulence code (GTC). A scaling study is also presented to stress the PCMS APIs and underlying rendezvous-based communication protocol implemented with ADIOS2. It demonstrates the high performance of the ADIOS2 SST engine using remote direct memory access versus the filesystem-based BP4 engine on up to 260 nodes of Frontier; 16 to 2048 processes for the application being scaled and a fixed 16 processes each for the coupler and the second application.

This paper introduces an efficient tensor-vector product technique for the fast and accurate approximation of integral operators within physics-informed deep learning frameworks. Our approach leverages Kolmogorov-Arnold networks to evaluate problem dynamics at specific points, while employing Gaussian quadrature formulas to approximate the integral components, even in the presence of semi-infinite domains or singularities. We demonstrate the applicability of the proposed method to both Fredholm and Volterra integral operators, as well as to optimal control problems involving continuous time. Additionally, we outline how this approach can be extended to approximate fractional derivatives and integrals and propose a fast matrix-vector product algorithm for efficiently computing the fractional Caputo derivative. In the numerical section, we conduct comprehensive experiments on forward and inverse problems. For forward problems, we evaluate the performance of our method on over 50 diverse mathematical problems, including multi-dimensional integral equations, systems of integral equations, partial and fractional integro-differential equations, and various optimal control problems in delay, fractional, multi-dimensional, and nonlinear configurations. For inverse problems, we test our approach on several integral equations and fractional integro-differential problems. Finally, we introduce the pinnies Python package to facilitate the implementation and usability of the proposed method.

A Fortran program that can be applied to simulate the four-dimensional dynamical structure factors (Dysurf) for inelastic neutron and inelastic X-ray scattering experiments is presented. With the underlying theoretical formalism, the detailed implementation of the program is described. Based on the second-order force constants from the first-principles method, the Dysurf code can well reproduce the measured spectroscopies of those scattering experiments. Four main applications of this code with the corresponding examples are introduced here, including the multi-dimensional dynamical structure factors, thermal diffuse scattering, line cut at specific points in the Brillouin zone and sample design. This program will be helpful in terms of designing and explaining related inelastic scattering experiments.

Accurate modeling of thermal and non-thermal plasma systems requires reliable thermodynamic and transport properties, which are often difficult to measure or unavailable for many plasma mixtures and operating conditions. These quantities have a strong influence on the predictive quality of simulations. Current methods to compute these quantities for modeling may often lack the physical consistency required for high-fidelity applications, and computing them from very first principles can be cumbersome as they are the result of a complex problem that has to be systematically addressed. To overcome current challenges in computing plasma properties, we present PPFM (Plasma Properties For Many), an open-source C++ library for the computation of plasma thermodynamic and transport properties in both local and non-local thermodynamic equilibrium, LTE and NLTE, respectively. PPFM combines well-established theoretical models with a modular and advanced object-oriented architecture designed for flexibility, extensibility and scalability while ensuring minimal and intuitive user inputs. PPFM offers a promising reference platform to address thermodynamic and transport properties determinations, starting from very basic physical principles and microscopic properties, to compute macroscopic quantities.

Algorithmic formulations of GPU programs provide a high-level alternative to device-specific code by expressing computations as compositions of well-defined parallel primitives (e.g., map, sort, reduce), rather than through handcrafted GPU kernels. In this work, we demonstrate that this paradigm can be extended to complex and challenging problems in computational physics: the simulation of granular flows and fluid-particle interactions. LEDDS, our open-source framework, performs fully coupled Lattice Boltzmann – Discrete Element Method (LBM-DEM) simulations using only algorithmic primitives, and runs efficiently on single-GPU platforms. The entire workflow, including neighbor search, collision detection, and fluid-particle coupling, is expressed as a sequence of portable primitives. Performance results are primarily reported for an NVIDIA A100 GPU, while portability to AMD GPUs and CPUs is also demonstrated. The code relies on an abstraction layer that dispatches generic algorithms to platform-specific function calls. Most operations are handled by the C++ parallel algorithms layer, which provides a sufficient abstraction by itself, while in selected cases a backend-specific variant is chosen for performance reasons, using either the Thrust library or AMD's rocThrust layer. LEDDS is validated through benchmarks spanning both DEM and LBM-DEM configurations, including sphere and ellipsoid collisions, wall friction tests, single-particle settling, Jeffery's orbits, and particle-laden shear flows. Despite its high level of abstraction, LEDDS achieves performance comparable to those of hand-tuned CUDA solvers, while maintaining portability and code clarity. These results show that high-performance LBM-DEM coupling can be achieved without sacrificing generality or readability, establishing LEDDS as a blueprint for portable multiphysics frameworks based on algorithmic primitives.

We introduce kalypsso (a Kokkos Applicative LaYer for Parallel and Scalable Solvers on Octrees): a new octree-based block-structured adaptive mesh refinement (AMR) framework using the C++ kokkos library for designing performance portable applications in computational fluid dynamics (CFD). Mesh management in distributed memory is implemented with the help of the p4est library, which provides a MPI parallel CPU implementation of the forest of octrees AMR algorithms. All heavyweight application data are allocated on a computing device, either a CPU or a GPU, and managed directly by kalypsso. One of the key design choice of kalypsso architecture is to use a lightweight hash-table-based (or dictionary) data structure for exchanging mesh geometry information between p4est, running on the host CPU, and the computational kernels executed on the accelerated device. Several finite volume methods for compressible monofluid and bifluid hydrodynamics, as well as magnetohydrodynamics are implemented using the kokkos programing model for exploiting shared memory parallelism on most existing CPU and GPU-based architecture. Node-level performance metrics for a second-order MUSCL-Hancock finite volume solver are measured to evaluate the impact of the size of the grid of cells attached onto octree leaves. A single Nvidia GH200 GPU can perform about 1.4 billions cell-updates per second. The performance portability on a cluster of CPU and GPU is demonstrated; a node-to-node weak scaling efficiency of  ∼ 80% is obtained on a cluster of 512 Nvidia GH200 GPUs. Using comparable hardware resources and considering the Euler equation solver in kalypsso with AMR activated, a consistent  × 5 CPU to GPU time to solution speed-up is obtained.

The construction of atomic model featuring realistic initial dislocation structures poses a critical challenge for molecular dynamics (MD) simulations. Most existing methods are limited to generating dislocation with simple geometrical morphologies, restricting the generalization of atomic simulations. In this study, we present a scientific software CryDisGen, a versatile and robust toolkit designed to create atomic models with arbitrary dislocation configurations. Based on the displacement field of dislocation derived from the classical Burgers model, CryDisGen can effectively handle dislocations of complex morphologies. The versatility and effectiveness of CryDisGen have been demonstrated through the successful construction of a variety of representative dislocation structures in face-centered cubic (FCC) and body-centered cubic (BCC) crystals. CryDisGen provides a powerful and flexible framework for generating dislocations in crystalline materials, facilitating the atomic modelling with realistic dislocation structures in MD simulations.

We introduce κALDo 2.0, an open-source Python package for computing vibrational, elastic, and thermal transport properties of crystalline and disordered solids from first principles and machine-learned interatomic potentials. Building on the anharmonic lattice dynamics (ALD) framework, κALDo 2.0 provides efficient CPU and GPU-accelerated implementations of the Boltzmann transport equation (BTE) for crystals and the quasi-harmonic Green-Kubo (QHGK) method. The QHGK formalism extends thermal transport predictions beyond translationally-invariant crystals to materials lacking long-range order, including glasses, alloys, and complex nanostructures. κALDo 2.0 introduces native integration with modern machine-learned potentials (MLPs), enabling thermal transport workflows that combine the accuracy of first-principles methods with the scalability of classical force fields. It also features comprehensive support for temperature-dependent effective potentials (TDEP) workflows, flexible storage backends for large-scale calculations, and advanced quantification of anharmonicity. The software seamlessly interfaces with electronic structure codes (Quantum ESPRESSO, VASP), molecular dynamics packages (LAMMPS), and state-of-the-art MLPs (ACE, NEP, MACE, MatterSim, Orb), enabling thermal transport studies from 0 K to finite temperatures. κALDo 2.0 implements multiple BTE solution strategies (relaxation time approximation, self-consistent iteration, full matrix inversion, and eigendecomposition) and supports essential physical corrections, including isotopic scattering and non-analytical terms for polar materials. A modular Python architecture with lazy evaluation and multiple storage formats (formatted text, NumPy, HDF5) enables simulations of systems containing more than 10,000 atoms. This paper describes the theoretical framework, implementation details, software architecture, and validation examples demonstrating κALDo 2.0’s capabilities for studying complex materials, including halide perovskites with strong anharmonicity and polar oxides requiring long-range electrostatic corrections.

Multifractal Detrended Fluctuation Analysis (MFDFA) is a powerful and widely used technique for characterizing the scaling properties and long-range correlations of complex time series. However, its application often involves significant practical challenges, such as the subjective identification of scaling regions (crossovers) and the disambiguation of the physical origins of multifractality. We introduce MF-toolkit, a high-performance, parallelized Python library designed to address these challenges. It integrates three key innovations: (1) fully automatic crossover detection algorithms (CDV-A and SPIC), which remove operator bias and enhance reproducibility; (2) a built-in implementation of the Iterative Amplitude Adjusted Fourier Transform (IAAFT) for generating surrogate data, enabling the robust identification of the source of multifractality; and (3) a comprehensive suite for generating synthetic time series for rigorous validation. We demonstrate the rigor and utility of MF-toolkit through its application to characterize the multifractal properties of non-stationary noise in gravitational wave (LIGO) data. The MF-toolkit library offers a robust, efficient, and user-friendly tool for advanced time series analysis, facilitating more rigorous and reproducible research across physics and other data-intensive fields.

The aim of this paper is to present the extension of the HPC pseudo-spectral solver BEC2HPC to compute the dynamics of 2D/3D rotating Bose-Einstein condensates modeled by the Gross-Pitaevskii equations with a rotation term. Numerical examples are provided to show the efficiency of the solver for large-scale simulations.