Pub Date : 2025-01-30DOI: 10.1016/j.cpc.2025.109524
Dhairya R. Vyas , Julio M. Ottino , Richard M. Lueptow , Paul B. Umbanhowar
The Discrete Element Method is widely employed for simulating granular flows, but conventional integration techniques may produce unphysical results for simulations with static friction when particle size ratios exceed . These inaccuracies arise under certain circumstances because some variables in the velocity-Verlet algorithm are calculated at the half-timestep, while others are computed at the full timestep. To correct this, we develop an improved velocity-Verlet integration algorithm to ensure physically accurate outcomes up to the largest size ratios examined (). The implementation of this improved synchronized_verlet integration method within the LAMMPS framework is detailed, and its effectiveness is validated through a simple three-particle test case and a more general example of granular flow in mixtures with large size-ratios, for which we provide general guidelines for selecting simulation parameters and accurately modeling inelasticity in large particle size-ratio simulations.
{"title":"Improved velocity-Verlet algorithm for the discrete element method","authors":"Dhairya R. Vyas , Julio M. Ottino , Richard M. Lueptow , Paul B. Umbanhowar","doi":"10.1016/j.cpc.2025.109524","DOIUrl":"10.1016/j.cpc.2025.109524","url":null,"abstract":"<div><div>The Discrete Element Method is widely employed for simulating granular flows, but conventional integration techniques may produce unphysical results for simulations with static friction when particle size ratios exceed <span><math><mi>R</mi><mo>≈</mo><mn>3</mn></math></span>. These inaccuracies arise under certain circumstances because some variables in the velocity-Verlet algorithm are calculated at the half-timestep, while others are computed at the full timestep. To correct this, we develop an improved velocity-Verlet integration algorithm to ensure physically accurate outcomes up to the largest size ratios examined (<span><math><mi>R</mi><mo>=</mo><mn>100</mn></math></span>). The implementation of this improved <span>synchronized_verlet</span> integration method within the LAMMPS framework is detailed, and its effectiveness is validated through a simple three-particle test case and a more general example of granular flow in mixtures with large size-ratios, for which we provide general guidelines for selecting simulation parameters and accurately modeling inelasticity in large particle size-ratio simulations.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"310 ","pages":"Article 109524"},"PeriodicalIF":7.2,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-30DOI: 10.1016/j.cpc.2025.109528
Wayne Arter
The work addresses the problem of the interoperability of modelling codes, especially in the context of the relatively limited bandwidth of current High-Performance Computers (HPC). Many codes employ non-dimensionalisations that effectively set units by the scalings they choose for length, time and other key quantities. These units can be difficult to unpick in a multiscale environment, but the natural choice of SI units usually leads to a need to treat a much large range of number with negative consequences for HPC deployment. A compromise, applicable to both particle and mesh-based codes, is sought whereby the user may set length- and time-scales in SI units appropriate to the plasma or other modelling problem under consideration. Application is made both analytically and to existing plasma software that demonstrates reduced need for number range relative to SI. An algorithm is presented that enables treatment of the often tricky problem of changing units with minimal user intervention. The code work indicates use of single-precision (32-bit real number representations) may be adequate for particle-mesh modelling in an unexpectedly large range of circumstances.
{"title":"Physical units helpful for multiscale modelling","authors":"Wayne Arter","doi":"10.1016/j.cpc.2025.109528","DOIUrl":"10.1016/j.cpc.2025.109528","url":null,"abstract":"<div><div>The work addresses the problem of the interoperability of modelling codes, especially in the context of the relatively limited bandwidth of current High-Performance Computers (HPC). Many codes employ non-dimensionalisations that effectively set units by the scalings they choose for length, time and other key quantities. These units can be difficult to unpick in a multiscale environment, but the natural choice of SI units usually leads to a need to treat a much large range of number with negative consequences for HPC deployment. A compromise, applicable to both particle and mesh-based codes, is sought whereby the user may set length- and time-scales in SI units appropriate to the plasma or other modelling problem under consideration. Application is made both analytically and to existing plasma software that demonstrates reduced need for number range relative to SI. An algorithm is presented that enables treatment of the often tricky problem of changing units with minimal user intervention. The code work indicates use of single-precision (32-bit real number representations) may be adequate for particle-mesh modelling in an unexpectedly large range of circumstances.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"310 ","pages":"Article 109528"},"PeriodicalIF":7.2,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-29DOI: 10.1016/j.cpc.2025.109526
Ning Wang
In this study, Gradient Descent Generation (GDGen) is presented, an innovative methodological framework that utilizes gradient descent algorithms to create dense, non-overlapping configurations of multiple, user-customized clusters and shapes. This technique is crucial for the accuracy and efficacy of molecular dynamics (MD) simulations, finite element analyses, and a multitude of scientific applications where precise spatial arrangement is paramount. GDGen intricately minimizes a loss function tailored to assess spatial overlaps and guide the arrangement process.
The implementation of GDGen is encapsulated in Pygdgen, a Python package developed to generate intricate atomic configurations, particularly excelling in scenarios involving dense clustering and unconventional geometries. Pygdgen ensures efficient arrangement processes through its optimized coding structure and GPU acceleration capabilities. Its adaptability is evidenced by its application in various fields, from material science and chemistry to urban planning and mechanical design, for arranging complex structures within constrained spaces.
{"title":"GDGen: A gradient descent-based methodology for the generation of optimized spatial configurations of customized clusters in computational simulations","authors":"Ning Wang","doi":"10.1016/j.cpc.2025.109526","DOIUrl":"10.1016/j.cpc.2025.109526","url":null,"abstract":"<div><div>In this study, Gradient Descent Generation (GDGen) is presented, an innovative methodological framework that utilizes gradient descent algorithms to create dense, non-overlapping configurations of multiple, user-customized clusters and shapes. This technique is crucial for the accuracy and efficacy of molecular dynamics (MD) simulations, finite element analyses, and a multitude of scientific applications where precise spatial arrangement is paramount. GDGen intricately minimizes a loss function tailored to assess spatial overlaps and guide the arrangement process.</div><div>The implementation of GDGen is encapsulated in <em>Pygdgen</em>, a Python package developed to generate intricate atomic configurations, particularly excelling in scenarios involving dense clustering and unconventional geometries. <em>Pygdgen</em> ensures efficient arrangement processes through its optimized coding structure and GPU acceleration capabilities. Its adaptability is evidenced by its application in various fields, from material science and chemistry to urban planning and mechanical design, for arranging complex structures within constrained spaces.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"310 ","pages":"Article 109526"},"PeriodicalIF":7.2,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-28DOI: 10.1016/j.cpc.2025.109527
Renfan Mao , Junxue Ren , Haibin Tang , Zhihui Li
A novel domain decomposition parallelization scheme for solving the Poisson equation in explicit electrostatic particle-in-cell (PIC) plasma simulation is proposed in this paper. Using the Schwarz method, the original problem is transformed into a mapping process on a series of subsets of unknowns, thereby reducing the original problem to a problem with much fewer unknowns. The solver using this scheme will have explicit parallelism brought by domain decomposition and near theoretical scalability benefiting from low communication cost. A two-dimensional Poisson solver code is developed and a series of tests are implemented to verify its correctness and performance. This scheme can provide effective performance improvement for electrostatic PIC simulations.
{"title":"A domain decomposition parallelization scheme for the Poisson equation in particle-in-cell simulation","authors":"Renfan Mao , Junxue Ren , Haibin Tang , Zhihui Li","doi":"10.1016/j.cpc.2025.109527","DOIUrl":"10.1016/j.cpc.2025.109527","url":null,"abstract":"<div><div>A novel domain decomposition parallelization scheme for solving the Poisson equation in explicit electrostatic particle-in-cell (PIC) plasma simulation is proposed in this paper. Using the Schwarz method, the original problem is transformed into a mapping process on a series of subsets of unknowns, thereby reducing the original problem to a problem with much fewer unknowns. The solver using this scheme will have explicit parallelism brought by domain decomposition and near theoretical scalability benefiting from low communication cost. A two-dimensional Poisson solver code is developed and a series of tests are implemented to verify its correctness and performance. This scheme can provide effective performance improvement for electrostatic PIC simulations.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"310 ","pages":"Article 109527"},"PeriodicalIF":7.2,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-28DOI: 10.1016/j.cpc.2025.109521
Ruben Otin
<div><div>ERMES 20.0 is an open-source software which solves the Maxwell's equations in frequency domain with the Finite Element Method (FEM). The new ERMES 20.0 is a significant upgrade from the previous ERMES 7.0 [1]. It introduces new features, modules, and FEM formulations to address the challenging problems commonly encountered in the design and analysis of nuclear fusion reactors [2]. Key additions are the electrostatic and cold plasma module, along with new FEM formulations as the stabilized double-curl edge element formulation [3] and the local <span><math><msup><mrow><mi>L</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> projection method with nodal and bubble elements [4,5]. Furthermore, all the formulations now include an A-V potentials version. The ample set of methods available in the new ERMES 20.0 allows the user to select the most suitable FEM formulation to generate the best possible conditioned matrix for each specific problem.</div><div>ERMES 20.0 operates in the static, quasi-static and the high-frequency regimens, making it a versatile tool which can be used in a wide variety of situations. For instance, it had been applied to microwave engineering, bioelectromagnetics, and electromagnetic compatibility. Now, thanks to the new electrostatic and cold plasma modules, the range of applications has been extended to relevant nuclear fusion engineering problems as: the computation of induced forces, plasma control, probability estimation of electric arc initiation, current distribution in arbitrary geometries, and the study of electromagnetic wave-plasma-wall interactions inside a fusion reactor.</div><div>ERMES 20.0 is available for Windows and Linux systems and it has improved its capabilities to solve large problems on High Performance Computing (HPC) infrastructures thanks to its new interface with the solver libraries PETSc [6] and Python NumPy [7]. As in previous versions, ERMES 20.0 features a graphical user-friendly interface integrated into the pre- and post-processor GiD [8]. GiD handles geometrical modeling, data input, meshing, and result visualization. ERMES 20.0 is licensed under the open-source software 2-clause BSD license.</div><div>This document is accompanied by a comprehensive manual that provides a step-by-step installation guide, a detailed description of all the new features and formulations, as well as the executables, user interface, examples, and source code of ERMES 20.0.</div></div><div><h3>New version program summary</h3><div><em>Program Title:</em> ERMES 20.0</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/v946dvxn54.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://ruben-otin.blogspot.com/</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> BSD 2-clause</div><div><em>Programming language:</em> C++</div><div><em>Journal reference of previous version:</em> Comput. Phys.
{"title":"ERMES 20.0: Open-source finite element tool for computational electromagnetics in the frequency domain","authors":"Ruben Otin","doi":"10.1016/j.cpc.2025.109521","DOIUrl":"10.1016/j.cpc.2025.109521","url":null,"abstract":"<div><div>ERMES 20.0 is an open-source software which solves the Maxwell's equations in frequency domain with the Finite Element Method (FEM). The new ERMES 20.0 is a significant upgrade from the previous ERMES 7.0 [1]. It introduces new features, modules, and FEM formulations to address the challenging problems commonly encountered in the design and analysis of nuclear fusion reactors [2]. Key additions are the electrostatic and cold plasma module, along with new FEM formulations as the stabilized double-curl edge element formulation [3] and the local <span><math><msup><mrow><mi>L</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> projection method with nodal and bubble elements [4,5]. Furthermore, all the formulations now include an A-V potentials version. The ample set of methods available in the new ERMES 20.0 allows the user to select the most suitable FEM formulation to generate the best possible conditioned matrix for each specific problem.</div><div>ERMES 20.0 operates in the static, quasi-static and the high-frequency regimens, making it a versatile tool which can be used in a wide variety of situations. For instance, it had been applied to microwave engineering, bioelectromagnetics, and electromagnetic compatibility. Now, thanks to the new electrostatic and cold plasma modules, the range of applications has been extended to relevant nuclear fusion engineering problems as: the computation of induced forces, plasma control, probability estimation of electric arc initiation, current distribution in arbitrary geometries, and the study of electromagnetic wave-plasma-wall interactions inside a fusion reactor.</div><div>ERMES 20.0 is available for Windows and Linux systems and it has improved its capabilities to solve large problems on High Performance Computing (HPC) infrastructures thanks to its new interface with the solver libraries PETSc [6] and Python NumPy [7]. As in previous versions, ERMES 20.0 features a graphical user-friendly interface integrated into the pre- and post-processor GiD [8]. GiD handles geometrical modeling, data input, meshing, and result visualization. ERMES 20.0 is licensed under the open-source software 2-clause BSD license.</div><div>This document is accompanied by a comprehensive manual that provides a step-by-step installation guide, a detailed description of all the new features and formulations, as well as the executables, user interface, examples, and source code of ERMES 20.0.</div></div><div><h3>New version program summary</h3><div><em>Program Title:</em> ERMES 20.0</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/v946dvxn54.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://ruben-otin.blogspot.com/</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> BSD 2-clause</div><div><em>Programming language:</em> C++</div><div><em>Journal reference of previous version:</em> Comput. Phys. ","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"310 ","pages":"Article 109521"},"PeriodicalIF":7.2,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-28DOI: 10.1016/j.cpc.2025.109525
Diem Thi-Xuan Dang, Dai-Nam Le, Lilia M. Woods
A new scheme for the computation of dispersive interactions from first principles is presented. This cost-effective approach relies on a Wannier function representation compatible with density function theory descriptions. This is an electronic-based many-body method that captures the full electronic and optical response properties of the materials. It provides the foundation to discern van der Waals and induction energies as well as the role of anisotropy and different stacking patterns when computing dispersive interactions in systems. Calculated results for binding energies in benchmarked materials and layered materials, such as graphite, hBN, and MoS2 give encouraging comparisons with available experimental data. Strategies for broadened computational descriptions of dispersive interactions are also discussed. Our investigation aims at stimulating new experimental studies to measure van der Waals energies in a wider range of materials, especially in layered systems.
{"title":"Dissecting van der Waals interactions with density functional theory – Wannier-basis approach","authors":"Diem Thi-Xuan Dang, Dai-Nam Le, Lilia M. Woods","doi":"10.1016/j.cpc.2025.109525","DOIUrl":"10.1016/j.cpc.2025.109525","url":null,"abstract":"<div><div>A new scheme for the computation of dispersive interactions from first principles is presented. This cost-effective approach relies on a Wannier function representation compatible with density function theory descriptions. This is an electronic-based many-body method that captures the full electronic and optical response properties of the materials. It provides the foundation to discern van der Waals and induction energies as well as the role of anisotropy and different stacking patterns when computing dispersive interactions in systems. Calculated results for binding energies in benchmarked materials and layered materials, such as graphite, hBN, and MoS<sub>2</sub> give encouraging comparisons with available experimental data. Strategies for broadened computational descriptions of dispersive interactions are also discussed. Our investigation aims at stimulating new experimental studies to measure van der Waals energies in a wider range of materials, especially in layered systems.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"310 ","pages":"Article 109525"},"PeriodicalIF":7.2,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-28DOI: 10.1016/j.cpc.2025.109512
Paul Fuchs , Stephan Thaler , Sebastien Röcken , Julija Zavadlav
<div><div>Neural Networks (NNs) are effective models for refining the accuracy of molecular dynamics, opening up new fields of application. Typically trained bottom-up, atomistic NN potential models can reach first-principle accuracy, while coarse-grained implicit solvent NN potentials surpass classical continuum solvent models. However, overcoming the limitations of costly generation of accurate reference data and data inefficiency of common bottom-up training demands efficient incorporation of data from many sources. This paper introduces the framework <span>chemtrain</span> to learn sophisticated NN potential models through customizable training routines and advanced training algorithms. These routines can combine multiple top-down and bottom-up algorithms, e.g., to incorporate both experimental and simulation data or pre-train potentials with less costly algorithms. <span>chemtrain</span> provides an object-oriented high-level interface to simplify the creation of custom routines. On the lower level, <span>chemtrain</span> relies on JAX to compute gradients and scale the computations to use available resources. We demonstrate the simplicity and importance of combining multiple algorithms in the examples of parametrizing an all-atomistic model of titanium and a coarse-grained implicit solvent model of alanine dipeptide.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> <span>chemtrain</span></div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/m6fxmcmfzz.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/tummfm/chemtrain</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> Apache-2.0</div><div><em>Programming language:</em> python</div><div><em>Nature of problem:</em> Neural Network (NN) potentials provide the means to accurately model high-order many-body interactions between particles on a molecular level. Through linear computational scaling with the system size, their high expressivity opens up new possibilities for efficiently modeling systems at a higher precision without resorting to expensive, finer-scale computational methods. However, as common for data-driven approaches, the success of NN potentials depends crucially on the availability of accurate training data. Bottom-up trained state-of-the-art models can match ab initio computations closer than their actual accuracy but can still predict deviations from experimental measurements. Including more accurate reference data can, in principle, resolve this issue, but generating sufficient data is infeasible even with less precise methods for increasingly larger systems. Supplementing the training procedure with more data-efficient methods can limit required training data [1]. In addition, the models can be fully or partially trained on macroscopic reference data [2,3]. Therefore, a framework supporting a combination of multiple training
{"title":"chemtrain: Learning deep potential models via automatic differentiation and statistical physics","authors":"Paul Fuchs , Stephan Thaler , Sebastien Röcken , Julija Zavadlav","doi":"10.1016/j.cpc.2025.109512","DOIUrl":"10.1016/j.cpc.2025.109512","url":null,"abstract":"<div><div>Neural Networks (NNs) are effective models for refining the accuracy of molecular dynamics, opening up new fields of application. Typically trained bottom-up, atomistic NN potential models can reach first-principle accuracy, while coarse-grained implicit solvent NN potentials surpass classical continuum solvent models. However, overcoming the limitations of costly generation of accurate reference data and data inefficiency of common bottom-up training demands efficient incorporation of data from many sources. This paper introduces the framework <span>chemtrain</span> to learn sophisticated NN potential models through customizable training routines and advanced training algorithms. These routines can combine multiple top-down and bottom-up algorithms, e.g., to incorporate both experimental and simulation data or pre-train potentials with less costly algorithms. <span>chemtrain</span> provides an object-oriented high-level interface to simplify the creation of custom routines. On the lower level, <span>chemtrain</span> relies on JAX to compute gradients and scale the computations to use available resources. We demonstrate the simplicity and importance of combining multiple algorithms in the examples of parametrizing an all-atomistic model of titanium and a coarse-grained implicit solvent model of alanine dipeptide.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> <span>chemtrain</span></div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/m6fxmcmfzz.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/tummfm/chemtrain</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> Apache-2.0</div><div><em>Programming language:</em> python</div><div><em>Nature of problem:</em> Neural Network (NN) potentials provide the means to accurately model high-order many-body interactions between particles on a molecular level. Through linear computational scaling with the system size, their high expressivity opens up new possibilities for efficiently modeling systems at a higher precision without resorting to expensive, finer-scale computational methods. However, as common for data-driven approaches, the success of NN potentials depends crucially on the availability of accurate training data. Bottom-up trained state-of-the-art models can match ab initio computations closer than their actual accuracy but can still predict deviations from experimental measurements. Including more accurate reference data can, in principle, resolve this issue, but generating sufficient data is infeasible even with less precise methods for increasingly larger systems. Supplementing the training procedure with more data-efficient methods can limit required training data [1]. In addition, the models can be fully or partially trained on macroscopic reference data [2,3]. Therefore, a framework supporting a combination of multiple training ","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"310 ","pages":"Article 109512"},"PeriodicalIF":7.2,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-27DOI: 10.1016/j.cpc.2025.109520
An-Ke Lei , Zhi-Lei She , Yu-Liang Yan , Dai-Mei Zhou , Liang Zheng , Wen-Chao Zhang , Hua Zheng , Larissa V. Bravina , Evgeny E. Zabrodin , Ben-Hao Sa
<div><div>Parton And-hadron China Institute of Atomic Energy (PACIAE) is a multipurpose Monte Carlo event generator developed to describe a wide range of high-energy collisions, including lepton-lepton, lepton-hadron, lepton-nucleus, hadron-hadron, hadron-nucleus, and nucleus-nucleus collisions. It is built based on the PYTHIA program, and incorporates parton and hadron cascades to address the nuclear medium effects. PACIAE 4.0 is the new generation of PACIAE model surpassing the version 3.0. In PACIAE 4.0, the old fixed-format FORTRAN 77 code has been refactored and rewritten by the free-format modern Fortran and C++ languages. The C++-based PYTHIA 8.3 is interfaced in, while previous versions connected to the Fortran-based PYTHIA 6.4 only. Several improvements are also introduced, which enable PACIAE 4.0 to contain more physics and features to model the high-energy collisions. This is the first attempt to transition PACIAE from Fortran to C++.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> PACIAE 4.0</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/w3g68dj4d9.5</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/ArcsaberHep/PACIAE4</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> GPLv2 or later</div><div><em>Programming language:</em> Fortran, C++</div><div><em>Journal reference of previous version:</em> Computer Physics Communications 284 (2023) 108615</div><div><em>Does the new version supersede the previous version?:</em> Yes</div><div><em>Reasons for the new version:</em> Improved and expanded physics models, transition from FORTRAN 77 to the modern Fortran mixed with C++</div><div><em>Summary of revisions:</em> PYTHIA 8 interface, transition to modern Fortran mixed with C++, and much more.</div><div><em>Nature of problem:</em> The Monte Carlo (MC) simulation has been successfully applied to the study of the high-energy collisions. MC models are able to give a fairly good description of the basic experimental observables. However, as more and more experimental data become available, more accurate modeling is required.</div><div><em>Solution method:</em> The parton and hadron cascade model PACIAE 2 series [1-6] and 3.0 [7] are based on the Fortran-based PYTHIA 6.4 [8]. PYTHIA has been upgraded to the C++-based PYTHIA 8.3 [9] with more physics and features. Therefore we upgrade the PACIAE model to the new version of PACIAE 4.0 with the option to either PYTHIA 6.4 [8] or PYTHIA 8.3 [9]. In addition, several improvements have been introduced in this new version.</div><div><em>Additional comments including restrictions and unusual features:</em> Restrictions depend on the problem studied. The running time is 1–1000 events per minute, depending on the collisions system studied.</div></div><div><h3>References</h3><div><ul><li><span>[1]</span><span><div>B.-H. Sa, et al., Comput. Phys. Commun.
{"title":"A brief introduction to PACIAE 4.0","authors":"An-Ke Lei , Zhi-Lei She , Yu-Liang Yan , Dai-Mei Zhou , Liang Zheng , Wen-Chao Zhang , Hua Zheng , Larissa V. Bravina , Evgeny E. Zabrodin , Ben-Hao Sa","doi":"10.1016/j.cpc.2025.109520","DOIUrl":"10.1016/j.cpc.2025.109520","url":null,"abstract":"<div><div>Parton And-hadron China Institute of Atomic Energy (PACIAE) is a multipurpose Monte Carlo event generator developed to describe a wide range of high-energy collisions, including lepton-lepton, lepton-hadron, lepton-nucleus, hadron-hadron, hadron-nucleus, and nucleus-nucleus collisions. It is built based on the PYTHIA program, and incorporates parton and hadron cascades to address the nuclear medium effects. PACIAE 4.0 is the new generation of PACIAE model surpassing the version 3.0. In PACIAE 4.0, the old fixed-format FORTRAN 77 code has been refactored and rewritten by the free-format modern Fortran and C++ languages. The C++-based PYTHIA 8.3 is interfaced in, while previous versions connected to the Fortran-based PYTHIA 6.4 only. Several improvements are also introduced, which enable PACIAE 4.0 to contain more physics and features to model the high-energy collisions. This is the first attempt to transition PACIAE from Fortran to C++.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> PACIAE 4.0</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/w3g68dj4d9.5</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/ArcsaberHep/PACIAE4</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> GPLv2 or later</div><div><em>Programming language:</em> Fortran, C++</div><div><em>Journal reference of previous version:</em> Computer Physics Communications 284 (2023) 108615</div><div><em>Does the new version supersede the previous version?:</em> Yes</div><div><em>Reasons for the new version:</em> Improved and expanded physics models, transition from FORTRAN 77 to the modern Fortran mixed with C++</div><div><em>Summary of revisions:</em> PYTHIA 8 interface, transition to modern Fortran mixed with C++, and much more.</div><div><em>Nature of problem:</em> The Monte Carlo (MC) simulation has been successfully applied to the study of the high-energy collisions. MC models are able to give a fairly good description of the basic experimental observables. However, as more and more experimental data become available, more accurate modeling is required.</div><div><em>Solution method:</em> The parton and hadron cascade model PACIAE 2 series [1-6] and 3.0 [7] are based on the Fortran-based PYTHIA 6.4 [8]. PYTHIA has been upgraded to the C++-based PYTHIA 8.3 [9] with more physics and features. Therefore we upgrade the PACIAE model to the new version of PACIAE 4.0 with the option to either PYTHIA 6.4 [8] or PYTHIA 8.3 [9]. In addition, several improvements have been introduced in this new version.</div><div><em>Additional comments including restrictions and unusual features:</em> Restrictions depend on the problem studied. The running time is 1–1000 events per minute, depending on the collisions system studied.</div></div><div><h3>References</h3><div><ul><li><span>[1]</span><span><div>B.-H. Sa, et al., Comput. Phys. Commun.","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"310 ","pages":"Article 109520"},"PeriodicalIF":7.2,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-22DOI: 10.1016/j.cpc.2025.109511
Aleksandr V. Boitsov, Karen Z. Hatsagortsyan, Christoph H. Keitel
The coordinate scaling method, previously developed for the numerical solution of the time-dependent Schrodinger equation, is generalized for the numerical treatment of the atomic ionization problem in relativistically strong laser fields, developing the prototype of the method for a one-dimensional case. To enable the use of the scaling method in relativistic settings, the Foldy-Wouthuysen transformation is employed in Silenko's form within the quasiclassical approximation, reducing the one-dimensional time-dependent Dirac equation (TDDE) to the square root Klein-Gordon-like equation. We demonstrate the computational advantage of the relativistic scaling method over the standard direct implementation of the TDDE solution, especially in the case of an applied non-uniform mesh.
{"title":"Scaling method for the numerical solution of the strong-field ionization problem in the relativistic regime","authors":"Aleksandr V. Boitsov, Karen Z. Hatsagortsyan, Christoph H. Keitel","doi":"10.1016/j.cpc.2025.109511","DOIUrl":"10.1016/j.cpc.2025.109511","url":null,"abstract":"<div><div>The coordinate scaling method, previously developed for the numerical solution of the time-dependent Schrodinger equation, is generalized for the numerical treatment of the atomic ionization problem in relativistically strong laser fields, developing the prototype of the method for a one-dimensional case. To enable the use of the scaling method in relativistic settings, the Foldy-Wouthuysen transformation is employed in Silenko's form within the quasiclassical approximation, reducing the one-dimensional time-dependent Dirac equation (TDDE) to the square root Klein-Gordon-like equation. We demonstrate the computational advantage of the relativistic scaling method over the standard direct implementation of the TDDE solution, especially in the case of an applied non-uniform mesh.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"310 ","pages":"Article 109511"},"PeriodicalIF":7.2,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-22DOI: 10.1016/j.cpc.2025.109513
Yuhang Wang, Waixiang Cao, Liang Pan
Recently, successes have been achieved for the high-order gas-kinetic schemes (HGKS) on unstructured meshes for compressible flows. In this paper, to accelerate the computation, HGKS is implemented with the graphical processing unit (GPU) using the compute unified device architecture (CUDA). HGKS on unstructured meshes is a fully explicit scheme, and the acceleration framework can be developed based on the cell-level parallelism. For single-GPU computation, the connectivity of geometric information is generated for the requirement of data localization and independence. Based on such data structure, the kernels and corresponding girds of CUDA are set. With the one-to-one mapping between the indices of cells and CUDA threads, the single-GPU computation using CUDA can be implemented for HGKS. For multiple-GPU computation, the domain decomposition and data exchange need to be taken into account. The domain is decomposed into subdomains by METIS, and the MPI processes are created for the control of each process and communication among GPUs. With reconstruction of connectivity and adding ghost cells, the main configuration of CUDA for single-GPU can be inherited by each GPU. The benchmark cases for compressible flows, including accuracy test and flow passing through a sphere, are presented to assess the numerical performance of HGKS with Nvidia RTX A5000 and Tesla V100 GPUs. For single-GPU computation, compared with the parallel central processing unit (CPU) code running on the Intel Xeon Gold 5120 CPU with open multi-processing (OpenMP) directives, 5x speedup is achieved by RTX A5000 and 9x speedup is achieved by Tesla V100. For multiple-GPU computation, HGKS code scales properly with the increasing number of GPU. Numerical results confirm the excellent performance of multiple-GPU accelerated HGKS on unstructured meshes.
{"title":"Multiple-GPU accelerated high-order gas-kinetic scheme on three-dimensional unstructured meshes","authors":"Yuhang Wang, Waixiang Cao, Liang Pan","doi":"10.1016/j.cpc.2025.109513","DOIUrl":"10.1016/j.cpc.2025.109513","url":null,"abstract":"<div><div>Recently, successes have been achieved for the high-order gas-kinetic schemes (HGKS) on unstructured meshes for compressible flows. In this paper, to accelerate the computation, HGKS is implemented with the graphical processing unit (GPU) using the compute unified device architecture (CUDA). HGKS on unstructured meshes is a fully explicit scheme, and the acceleration framework can be developed based on the cell-level parallelism. For single-GPU computation, the connectivity of geometric information is generated for the requirement of data localization and independence. Based on such data structure, the kernels and corresponding girds of CUDA are set. With the one-to-one mapping between the indices of cells and CUDA threads, the single-GPU computation using CUDA can be implemented for HGKS. For multiple-GPU computation, the domain decomposition and data exchange need to be taken into account. The domain is decomposed into subdomains by METIS, and the MPI processes are created for the control of each process and communication among GPUs. With reconstruction of connectivity and adding ghost cells, the main configuration of CUDA for single-GPU can be inherited by each GPU. The benchmark cases for compressible flows, including accuracy test and flow passing through a sphere, are presented to assess the numerical performance of HGKS with Nvidia RTX A5000 and Tesla V100 GPUs. For single-GPU computation, compared with the parallel central processing unit (CPU) code running on the Intel Xeon Gold 5120 CPU with open multi-processing (OpenMP) directives, 5x speedup is achieved by RTX A5000 and 9x speedup is achieved by Tesla V100. For multiple-GPU computation, HGKS code scales properly with the increasing number of GPU. Numerical results confirm the excellent performance of multiple-GPU accelerated HGKS on unstructured meshes.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"310 ","pages":"Article 109513"},"PeriodicalIF":7.2,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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