Computer Physics Communications

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.

We develop a matrix-free Full Approximation Storage (FAS) multigrid solver based on staggered finite differences and implemented in MATLAB with GPU acceleration. To improve single-GPU efficiency, intermediate variables are reused and an X-shape Multi-Color Gauss–Seidel (X-MCGS) smoother is introduced. In the present MATLAB GPU setting, this parity-based multicolor organization enables regular vectorized updates and avoids the masking-style implementation that is less convenient for GPU array execution. Restriction and prolongation operators are also implemented in MATLAB GPU arrays. Algebraic and asymptotic convergence tests verify the solver’s robustness and accuracy, while benchmark studies on large-scale problems show effective multigrid execution on large grids. To overcome GPU memory limitations, we further design memory-efficient implementations of first- and second-order projection schemes for the Navier–Stokes equations using a dynamic reuse strategy, which reduces GPU-resident variables from 12 (first-order scheme) and 15 (second-order scheme) to only 8, lowering memory footprint and improving performance by 20–30%. This enables 512^3 Navier–Stokes computations on a single RTX 4090, where classical implementations exceed device memory. The applicability of the solver is further demonstrated through large-scale simulations. Grain growth simulations on a 512^2 grid accommodate up to q = 1189 orientations in 2D and q = 123 in 3D, with fitted growth exponents reproducing the expected scaling laws. Moreover, the memory-efficient Navier–Stokes implementations, coupled with the Cahn–Hilliard equations, enable air–water two-bubble coalescence simulations on a 256 × 256 × 1024 grid using a single RTX 4090 GPU, yielding results in close agreement with experimental observations.

TNL-SPH is a novel open-source implementation of Smoothed Particle Hydrodynamics (SPH), integrated as a submodule of the Template Numerical Library (TNL), designed for modern distributed computing platforms with GPU accelerators. Focusing on hydrodynamic problems using weakly compressible formulations, TNL-SPH offers a modular, high-performance framework for implementing various SPH schemes and particle methods. Developed in C++17, it leverages TNL’s templated vectors and expression templates to provide compact, algebraic representations of numerical schemes, simplifying the development of complex physical models. Its multilayered design separates parallelism, performance, and numerical methods, enabling seamless execution across diverse hardware, including multi-GPU and distributed systems. TNL-SPH outperforms existing SPH codes for free-surface flows and supports scalable, high-performance simulations. This paper presents the design, implementation, and performance of TNL-SPH, alongside its applications in hydraulic problems, demonstrating its versatility and efficiency for scientific and engineering computations.

We present GollumFit, a framework designed for performing binned-likelihood analyses on neutrino telescope data. GollumFit incorporates model parameters common to any neutrino telescope and also model parameters specific to the IceCube Neutrino Observatory. We provide a high-level overview of its key features and how the code is organized. We then discuss the performance of the fitting in a typical analysis scenario, highlighting the ability to fit over tens of nuisance parameters. We present some examples showing how to use the package for likelihood minimization tasks. This framework uniquely incorporates the particular model parameters necessary for neutrino telescopes, and solves an associated likelihood problem in a time-efficient manner.

We present H-NESSi (The Hierarchical Non-Equilibrium Systems Simulation package), an open-source software package for solving the Kadanoff-Baym equations (KBE) of nonequilibrium Green’s function (NEGF) theory using hierarchical low-rank compression techniques. The simulation of correlated quantum systems out of equilibrium is severely limited by the cubic scaling in propagation time and quadratic memory growth associated with conventional two-time formulations. H-NESSi overcomes these limitations by combining high-order time-stepping schemes with hierarchical off-diagonal low-rank (HODLR) representations of the retarded and lesser Green’s functions, enabling controllable accuracy at substantially reduced computational cost and memory usage. Imaginary-time quantities are efficiently represented using the discrete Lehmann representation (DLR), enabling compact and accurate treatment of thermal initial states. The implementation supports multiorbital systems, adaptive singular value truncation, and both shared-memory (OpenMP) and distributed-memory (MPI) parallelization strategies suitable for large-scale lattice calculations. The workflow closely mirrors established NEGF frameworks while introducing compression transparently into the propagation procedure. Benchmark applications to driven superconductors within dynamical mean-field theory and to the two-dimensional Hubbard model demonstrate favorable scaling compared to conventional implementations, with asymptotic time complexity significantly below the cubic scaling of uncompressed approaches. H-NESSi thus enables long-time and large-system nonequilibrium simulations of correlated quantum materials, which were previously computationally prohibitive.

A major revision of the ChemNetworks software (originally published in the Journal of Computational Chemistry, 2014, 35, 495–505) is presented. While the original ChemNetworks provided foundational graph construction capabilities for chemical systems, it was limited to simple distance and 3-body angular edge criteria, was not designed for high-performance computing environments or real-time operation alongside running simulations. This release addresses these limitations through three core contributions. First, a recursive Z-matrix-based search algorithm is introduced that enables chemically intuitive, arbitrarily descriptive three-dimensional structure searches, supporting geometric, energetic, and logical criteria. Second, the DataSpaces data staging framework is incorporated as an optional I/O engine, enabling in-memory data exchange between ChemNetworks and running simulations that eliminates persistent storage bottlenecks and supports real-time graph construction and analysis. Third, a modular analysis framework leveraging the igraph library is introduced, providing a straightforward plugin architecture for community-contributed workflows. Benchmark results demonstrate linear scaling with system size and efficient MPI parallelization across up to 64 cores, with total computational complexity of O(N^R T/P), where N is the number of atoms, R is the Z-matrix depth, T is the number of timesteps, and P is the number of MPI processes.

We introduce SoliDualSPHysics, a novel open-source and GPU-accelerated software that extends DualSPHysics to enable the numerical simulation of hyperelastic, finite-strain plastic, and brittle fracture behavior in deformable solids within a unified smoothed particle hydrodynamics (SPH) software framework. The software implements a total Lagrangian formulation for solid mechanics that allows direct application of external loads and boundary conditions, enabling independent solid mechanics simulations. Brittle fracture is modeled through a phase-field approach coupled with SPH, allowing crack initiation, propagation, and branching under dynamic loading without explicit crack tracking, ad hoc crack-path criteria, or local refinement. The framework also supports user-defined mathematical expressions to prescribe time- and space-dependent quantities, complementing the solid and fracture extensions and enhancing flexibility across existing and future DualSPHysics applications. Leveraging DualSPHysics’ native CPU/GPU parallel architecture, the software achieves substantial computational acceleration for large-scale simulations, and the implementation is verified and validated against benchmark numerical problems and experimental data, demonstrating accuracy, robustness, and favorable scaling performance. Comprehensive implementation details and user documentation are provided to ensure reproducibility and to support further development by the community. The framework and source code are freely available through a public GitHub repository.

The MCPlas toolbox represents a collection of MATLAB functions for the automated generation of an equation-based fluid-Poisson model for non-thermal plasmas in the multiphysics simulation software COMSOL. Following the development of the new generation of the LXCat platform, all input data are prepared in a structured and interoperable JSON format and can be supplied and validated using existing JSON schemas. The toolbox includes fully transparent, editable MATLAB source code and offers an advanced description of electron transport in addition to commonly used approaches in the plasma modelling community. It supports one-dimensional and two-dimensional modelling geometries employing Cartesian, polar and cylindrical coordinate systems. MCPlas is tested on two reference cases: DC- and RF-driven low-pressure glow discharges in argon. Comparison of MCPlas results with results obtained by employing COMSOL’s Plasma Module verifies the reliability of the plasma model implemented by MCPlas and demonstrates the significance of electron transport treatment and boundary conditions applied in the toolbox. Using the same examples, the easy handling of complex reaction kinetic models in MCPlas and the reusability of its JSON input data across different modelling platforms are illustrated. This demonstrates that MCPlas provides a transparent and reproducible workflow for the simulation of non-thermal plasmas using COMSOL.

We present C-BerryTrans, a C++ code designed for first-principles calculations of Berry-curvature-driven transverse transport properties, namely the anomalous Hall conductivity (AHC) i.e., σ_{μν}^{AHC} and anomalous Nernst conductivity (ANC) i.e., α_{μν}^{ANC}. The code directly extracts eigenvalues and momentum-matrix elements from WIEN2k calculations and evaluates the Berry curvature (Ω) using a Kubo-like formalism, thereby avoiding the interpolation errors inherent in Wannier-based approaches. To ensure computational efficiency, C-BerryTrans parallelizes Ω evaluation over k-points using OpenMP and stores band-resolved curvature data in binary format, significantly reducing memory usage. This design enables rapid post-processing of AHC and ANC over a wide range of temperature (T) and chemical potential (ω) values in a single run. The code has been benchmarked on well-studied ferromagnetic materials- Fe, Fe3Ge, Pd, Fe3Al, and Co2FeAl. For Fe, the σ_{xy}^{AHC} is obtained to be  ∼ 775 ( ∼ 744) S/cm at 0 (300) K. In case of Fe3Ge, the calculated value of σ_{xy}^{AHC} is found to be 311 S/cm at 300 K. Nextly, for Co2FeAl, the magnitude of computed value of σ_{xy}^{AHC} at 2 K is found to be  ∼ 56 S/cm. Moving further, the room temperature magnitude of α_{xy}^{ANC} for Pd is obtained to be  ∼ 0.97 AK^{-1}m^{-1}. In case of Fe3Al, the maximum magnitude of α_{xy}^{ANC} for T ≤ 500 K is computed as  ∼ 2.83 AK^{-1}m^{-1}. Lastly, for Co2FeAl, the value of α_{xy}^{ANC} is obtained to be  ∼ 0.10 AK^{-1}m^{-1} at 300 K. These results show excellent agreement with previously reported data. With its accuracy, scalability, and user-friendly workflow, C-BerryTrans provides a powerful tool for exploring Ω-driven transport phenomena and is well suited for high-throughput materials discovery. The code further enables the evaluation of Ω-derived AHC/ANC contributions along user-defined high-symmetry k-point paths. This provides valuable microscopic insight into how specific band-structure features contribute to Ω-driven AHC/ANC. Additionally, the code is equipped with a visualization module that allows analysis of k-point contributions to AHC or ANC in any material. This further enhances its capability for exploring topological materials.