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Kratos

NOASSERTIONC++v10.4.0

A framework for building parallel, multi-disciplinary simulation software, focusing on modularity, extensibility, and high-performance computing.

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1.3k stars292 forks0 contributors

What is Kratos?

Kratos Multiphysics is a framework for building parallel, multi-disciplinary simulation software. It provides a modular and extensible core written in C++ with a Python interface, enabling the development of custom simulation tools for engineering and scientific applications. The framework supports high-performance computing and includes applications for areas like fluid dynamics, structural mechanics, and particle simulations.

Target Audience

Researchers, engineers, and developers in computational mechanics, fluid dynamics, and structural analysis who need to create or extend simulation software for complex multi-physics problems.

Value Proposition

Developers choose Kratos for its open-source BSD license, modular architecture that allows easy extension, and strong support for parallel computing, making it a versatile foundation for both academic and industrial simulation projects.

Overview

Kratos Multiphysics (A.K.A Kratos) is a framework for building parallel multi-disciplinary simulation software. Modularity, extensibility and HPC are the main objectives. Kratos has BSD license and is written in C++ with extensive Python interface.

Use Cases

Best For

  • Developing custom simulation software for multi-physics problems
  • Running large-scale parallel simulations in computational fluid dynamics
  • Modeling structural mechanics with nonlinear and dynamic behavior
  • Simulating discrete element methods (DEM) for particle dynamics
  • Creating fluid-structure interaction (FSI) analysis tools
  • Building extensible research platforms for scientific computing

Not Ideal For

  • Commercial teams needing turnkey simulation software with dedicated support and no coding required
  • Projects focused on real-time or interactive simulations where GUI integration is the primary concern
  • Small research groups without access to high-performance computing clusters or expertise in MPI/OpenMP
  • Organizations seeking a lightweight, single-purpose tool for basic 2D or linear analysis

Pros & Cons

Pros

High-Performance Parallelism

Supports OpenMP and MPI, enabling scalable simulations across thousands of cores, as highlighted in the README for large-scale engineering problems.

Extensible Application Framework

Core system with pluggable applications for domains like DEM and Fluid Dynamics allows tailored extensions, fostering multi-disciplinary simulation development.

Python Scripting Interface

Extensive Python bindings to the C++ core facilitate scripting, integration, and rapid prototyping, as noted in the features for user accessibility.

Cross-Platform Availability

Runs on Windows, Linux, and macOS, ensuring flexibility in deployment environments, as specified in the main features section.

Modular BSD License

BSD-4 license allows free commercial use, and modular architecture separates core from applications, promoting open-source collaboration and customization.

Cons

Complex Compilation Process

Compiling from source requires managing dependencies like Boost and Eigen via INSTALL.md, which can be time-consuming and error-prone for newcomers.

Documentation Scattered Across Sites

Documentation is split between GitHub, external wikis, and tutorials, potentially making it harder for users to find cohesive, up-to-date guidance.

Steep Integration Curve

Adding custom applications demands deep knowledge of C++ and the framework's architecture, limiting rapid experimentation for domain-specific needs.

Limited Built-In GUI

Relies on external tools like GiD for pre- and post-processing, lacking a native graphical interface, which may hinder usability for non-programmers.

Frequently Asked Questions

Quick Stats

Stars1,257
Forks292
Contributors0
Open Issues421
Last commit19 hours ago
CreatedSince 2017

Tags

#scientific-computing#parallel-computing#high-performance-computing#finite-element-analysis#c-plus-plus#python#openmp#python-bindings#multi-platform#numerical-methods

Built With

E
Eigen
M
MPI
O
OpenMP
P
Python
P
Pybind11
B
Boost
C
C++

Links & Resources

Website

Included in

C/C++70.6k
Auto-fetched 7 hours ago

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