Sage Linear System Solver

Introduction

GPU-accelerated algorithms for solving large sparse linear algebraic equations using C++ language, implementing Jacobi Method, Gauss—Seidel Method, Successive Over-Relaxation (SOR) Method, and Conjugate Gradient Method. Among these, we also implemented Jacobi Method and Conjugate Gradient using Nvidia CUDA API to accelerate these algorithms.

It is a collaborative, interdisciplinary project drawing on expertise from School of Software Engineering and College of Civil Engineering, Tongji University, Shanghai.

Getting Started

Environment Requirements

• NVIDIA Graphics Card (Support at least versions after CUDA 10.0)
• Microsoft Windows 10 (NVIDIA has ceased CUDA driver support for Apple MacOS X)
• Microsoft Visual Studio (Special support for CUDA application)

Get the Project

• Get the source code from GitHub

  git clone https://github.com/1751200/Xlab-k8s-gpu.git

Experiments

* Only experiments of non-iterative methods are listed here.

• Implementation of Gaussian Elimination, LU decomposition, SVD decomposition algorithms using Eigen (used for comparison and analysis)
  • Report: Performance analysis of several conventional solutions using C + + Eigen Library
  • Code: ./Non-iterative-Methods/Eigen/
• Implementation of non-iterative method based on Gaussian Elimination using C++ and CUDA (both CPU and GPU versions)
  • Report
  • Code: ./Non-iterative-Methods/Gaussian-elimination/
• Implementation of non-iterative method based on LU Decomposition using C++ and CUDA (both CPU and GPU versions)
  • Report
  • Code: ./Non-iterative-Methods/LU-Decomposition/
• Implementation of non-iterative method based on SVD Decomposition using C++ and CUDA (both CPU and GPU versions)
  • Report
  • Code: ./Non-iterative-Methods/svd-solver/

Import the Project to IDE

• Basic CPU versions (jacobi, gauss, SOR, conjugate gradient)

  • Use Visual Studio (2015/16/17/18) to create a Command Line Application project.
  • Copy the relevent .cpp, .h files to the project path xxx/src
  • You can also use compilers other than Visual Studio such as dev c++, llvm, ant etc.

• CUDA GPU versions (jacobi_gpu, cg_gpu)

  • Use Visual Studio to create a blank CUDA project.
  • Copy the relevant .cu, .cuh, .cpp, .h, .lib, .exp files to the project path xxx/src
  • You can also use ncvv compilers (requires gcc and g++ on Linux, clang and clang++ on Mac OS X, and cl.exe on Windows)

Build the Project

In Visual Studio:

• After configuring the compilation, click Build to build the project.

In Command Line:

• If using g++ compiler, we strongly recommend you to turn on O3 compiler optimization using g++ -o3 command.

Running the Project

• Install npm if missing npm environment
• Enter project folder

  cd project
  

• Run npm start
• Open your browser and visit localhost://3000

Project Functionalities

• Upload your matrix files (each includes a dense matrix and vector)
• Solve the linear system using different algorithms

Documentation

We use GitHub Wiki for organizing documentation. For the documentation available, see the homepage of our Wiki.

Code Structure

.
├── MatMul                         # Solve large matrix multiplication problem
│   ├── CUDA                       # Example provided by CUDA tutorial
│   ├── PyTorch&CuPy               # Cope with matrix multiplication using python libararies
│   └── cuBLAS                     # Solve large matrix multiplication using cuBLAS API
├── Iterative-Methods              # Implementation of iterative methods
│   ├── Basic                      # initialize solvers
│   ├── ConjugateGradient          # Conjugate Gradient method (both CPU and GPU)
│   ├── Jacobi                     # Jacobi method (both CPU and GPU)
│   ├── Gauss-Seidel               # Gauss Seidel method (both CPU and GPU)
│   ├── Sparse-Matrix-CG-Solver    # Solve sparse linear system using cuBLAS and cuSOLVER
│   └── Utils                      # Utils, helps to read matrix data from given file
├── Non-iterative-Methods          # Implementation of iterative methods
│   ├── Eigen                      # Gaussian Elimination, LU, SVD methods using Eigen
│   ├── Gaussian-elimination       # Gaussian Elimination method
│   ├── LU-Decomposition           # LU Decomposition method
│   └── svd-solver                 # SVD Decomposition method
├── project                        # A Node.js app for testing these algorithms
├── Report                         # Documentation for this project
├── README.md
├── LICENSE
└── images

License

This project is licensed under the Apache2.0 License - see the LICENSE.md file for details.