FASTSolver: High-Performance Scientific Computing Framework

Overview

FASTSolver is a comprehensive scientific computing framework designed for high-performance numerical solutions, computational fluid dynamics (CFD), linear algebra operations, and scientific visualization.

Key Features

Numerical Methods

• Linear algebra operations
• Iterative solvers (GMRES, CG)
• Multi-grid methods
• ODE solvers
• Newton Method for Nonlinear Systems
  • Solves nonlinear systems of equations
  • Supports custom Jacobian computation
  • Integration with GMRES for linear system solving
  • Convergence checks

CFD Capabilities

• Lattice Boltzmann Method (LBM)
  • D2Q9 and D3Q19 models
  • Various boundary conditions
  • Immersed Boundary Method (IBM)
• Flow visualization
• Performance monitoring

Tools

• VTK output generation
• Real-time flow visualization
• Performance metrics logging

Requirements

• C++17 or higher
• CMake 3.15+
• Python 3.9+ (optional)

Installation

Clone the repository and run the setup script:

git clone https://github.com/Yin169/FASTSolver.git
cd FASTSolver
bash script_test.sh

For Python installation:

cd FASTSolver
python setup.py install

Usage

Here's an example of how to use the FASTSolver module in Python:

import fastsolver as fs

A = fs.SparseMatrix(2, 2)
A.addValue(0, 0, 4.0)
A.addValue(0, 1, 1.0)
A.addValue(1, 0, 1.0)
A.addValue(1, 1, 3.0)
A.finalize()

b = fs.Vector(2)
b[0] = 1.0
b[1] = 2.0

x = fs.Vector(2)
x[0] = 0.0
x[1] = 0.0

amg = fs.AlgebraicMultiGrid()

levels = 2
smoothing_steps = 10
theta = 0.5
amg.amgVCycle(A, b, x, levels, smoothing_steps, theta)

print("Solution x:", [x[i] for i in range(x.size())])

Project Structure

FASTSolver
├── Doxyfile
├── LICENSE
├── README.md
├── application
│   ├── LatticeBoltz
│   │   └── LBMSolver.hpp
│   ├── Mesh
│   │   └── MeshObj.hpp
│   └── PostProcess
│       └── Visual.hpp
├── code
│   ├── test.ipynb
│   └── test.py
├── conanfile.txt
├── data
│   ├── Chem97ZtZ
│   │   └── Chem97ZtZ.mtx
│   └── Chem97ZtZ.tar
├── examples
│   ├── CylinderFlow.cpp
│   ├── JetFlow_2D.cpp
│   └── JetFlow_3D.cpp
├── python
│   └── pybind.cpp
├── script_test.sh
├── src
│   ├── Intergal
│   │   └── GaussianQuad.hpp
│   ├── LinearAlgebra
│   │   ├── Factorized
│   │   │   └── basic.hpp
│   │   ├── Krylov
│   │   │   ├── ConjugateGradient.hpp
│   │   │   ├── GMRES.hpp
│   │   │   └── KrylovSubspace.hpp
│   │   ├── Preconditioner
│   │   │   ├── LU.hpp
│   │   │   └── MultiGrid.hpp
│   │   └── Solver
│   │       └── IterSolver.hpp
│   ├── ODE
│   │   └── RungeKutta.hpp
│   ├── Obj
│   │   ├── DenseObj.hpp
│   │   ├── MatrixObj.hpp
│   │   ├── SparseObj.hpp
│   │   └── VectorObj.hpp
│   ├── PDEs
│   │   └── SpectralElementMethod.hpp
│   └── utils.hpp
└── test
    ├── ConjugateGradient_test.cpp
    ├── GMRES_test.cpp
    ├── GaussianQuad_test.cpp
    ├── KrylovSubspace_test.cpp
    ├── LBMSolver_test.cpp
    ├── LU_test.cpp
    ├── MeshObj_test.cpp
    ├── MultiGrid_test.cpp
    ├── RungeKutta_test.cpp
    ├── SparseMatrixCSCTest.cpp
    ├── SpectralElementMethod_test.cpp
    ├── Visual_test.cpp
    ├── basic_test.cpp
    ├── debuglogger.cpp
    ├── demo.cpp
    ├── itersolver_test.cpp
    ├── matrix_obj_test.cpp
    ├── test.cpp
    └── testfile.cpp

Contributing

Contributions are welcome! Feel free to fork the repository and submit a pull request.

1. Fork the project
2. Create a feature branch (git checkout -b feature-branch)
3. Commit your changes (git commit -am 'Add new feature')
4. Push to the branch (git push origin feature-branch)
5. Open a Pull Request

License

This project is licensed under the MIT License.

Acknowledgments

• Pybind11 for seamless C++ and Python integration
• Community contributions to numerical methods and solvers

Citation

If you use FASTSolver in your research, please cite it as follows:

@software{FASTSolver2024,
  author = {NG YIN CHEANG},
  title = {FASTSolver: High-Performance Scientific Computing Framework},
  year = {2024},
  url = {https://github.com/Yin169/FASTSolver}
}