FASTSolver: Advanced Scientific Computing Framework

Overview

FASTSolver is a state-of-the-art scientific computing framework that delivers exceptional performance for numerical computations, CFD simulations, matrix operations, and scientific data visualization.

Core Capabilities

Advanced Numerical Methods

• Optimized linear algebra computations
  • LU Decomposition
  • Cholesky Decomposition
  • ILU (Incomplete LU) Factorization
• High-performance iterative solvers
  • GMRES (Generalized Minimal Residual)
  • CG (Conjugate Gradient)
• Adaptive multi-grid algorithms
• Robust ODE integration
  • Runge-Kutta Methods
• Advanced Newton-Raphson Implementation
  • Efficient nonlinear system resolution
  • Flexible Jacobian matrix handling
  • Seamless GMRES integration
  • Sophisticated convergence monitoring

CFD Engine

• Advanced Lattice Boltzmann Implementation
  • Full D2Q9 and D3Q19 support
  • Comprehensive boundary condition library
  • Integrated IBM capabilities
• Interactive flow visualization
• Real-time performance analytics

Development Tools

• Streamlined VTK export
• Dynamic flow visualization
• Comprehensive performance tracking

System Prerequisites

• Modern C++ (17+)
• CMake 3.15 or newer
• Python 3.9+ (for optional features)

Quick Start

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:

iimport fastsolver as fs
import numpy as np


matrix = fs.SparseMatrix(1, 1)
fs.read_matrix_market("../data/bcsstk08/bcsstk08.mtx", matrix)

# Generate a random exact solution

x_ext = fs.Vector(np.random.rand(matrix.cols()))

# # Generate a random right-hand side
b = matrix * x_ext
x = fs.Vector(matrix.cols())
# # Solve the system using GMRES
gmres = fs.GMRES()

max_iter = 100
krylov_dim = 300
tol = 1e-6
gmres.solve(matrix, b, x, max_iter, krylov_dim, tol)

Project Structure

FASTSolver
├── Doxyfile
├── LICENSE
├── README.md
├── application
│   ├── LatticeBoltz
│   │   └── LBMSolver.hpp
│   ├── Mesh
│   │   └── MeshObj.hpp
│   └── PostProcess
│       └── Visual.hpp
├── conanfile.txt
├── 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
 

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}
}