Tracks course: TRA220, GPU-accelerated Computational Methods using Python and CUDA

Student reports 2024/2025

  1. Roni Celiker, Haozhe Sun, Fengming Tong, Adam Persson
    "GPU-accelerated Computational Methods using Python and CUDA: Lattice Boltzmann Method"
    View PDF file
  2. Chaoyu Zheng, Haojie Li, Raj Gopalakrishna Subramani Venkatachalam, Yuan Wei
    "GPU Accelerated Computations Using Python and CUDA: Accelerating Lattice Boltzmann Method (LBM) Based on CUDA"
    View PDF file
  3. Thisal Mandula Sugathapala
    "GPU-Accelerated Computational Methods for Lagrangian Particle Tracking using Python, Fortran and CUDA"
    project on GitHub
  4. Sebastian Miles, Bingzhou Xie
    "GPU Accelerated Poisson solver: A comparison of PyTorch, Cupy and multi-GPU (Cupy) for solving the Poisson equation"
    View PDF file
  5. Bala Kumaresh Thileep Kumar, Wei Liu, Wuyang Hao
    GPU Accelerated Computational Methods Using Python and CUDA: Computational Fluid Dynamics
    View PDF file
  6. Jesper Möllbrant, Raphaël Bouchez
    "GPU-accelerated Computational Methods using Python and CUDA: Accelerating a Poisson solver with CUDA"
    View PDF file
  7. Carl Gillmert, Sebastian Kvaldén, Erik Henriksson, Yanchen Lin
    "GPU-accelerated Computational Methods using Python and CUDA: GPU Accelerated FEM Calculations for Stationary Heat Flow"
    View PDF file

Student reports 2023/2024

  1. Erik Hasselwander, Yuhua Cheng, Kyriakos Gavras
    "GPU-accelerated computational methods using Python and CUDA"
    View PDF file
  2. Pontus Malmsköld, Ritoban Biswas
    "GPU-accelerated Computational Methods using Python and CUDA"
    View PDF file
  3. Benedick Allan Strugnell-Lees, Joar Forsberg, Viktor Sundström
    "Opportunities for GPU acceleration in CFD"
    View PDF file
  4. Afroditi Tzanetou, Arik Ben-Shabat, Oweis Al-Karawi, Robert F. Birkisson, Simon Riis
    "GPU-Accelerated Computational Methods for FEM Using Python and CUDA"
    View PDF file

Student reports 2022/2023

  1. Greeshma Ajayakumar, Jakub Fojt, Jian Tan, Leonard Nielsen
    "Solving the Poisson equation with GPU acceleration"
    View PDF file
  2. Panagiotis Moraitis, Johannes Hansson, Weilong Chen
    "GPU-accelerated computational methods using Python and CUDA"
    View PDF file
  3. Marios Aspris, Xingyuan Li, Andhika Pratama, Patricia Vanky
    "Acceleration of CFD Python code using CUDA"
    View PDF file
  4. Congxiao Zhang and Gayana Jinde Radhakrishna
    "Finite Element for 2D Solid Mechanics: GPU Accelerated Numerical Method with Python and CUDA"
    View PDF file
  5. Stefano Ribes
    GPU-accelerated Computational Methods using Python and CUDA
    Open at Gihub

Tracks course: TRA220 GPU-accelerated Computational Methods using Python and CUDA

Graphics Processing Units (GPUs) are specialized hardware designed to accelerate the processing of graphics and visualizations. GPUs have become increasingly popular for a variety of non-graphics related tasks, including scientific computing, machine learning, and data analysis.

Today, GPUs are also used for CFD (Computational Fluid Dynamics) and FEM (Finite Element Method). The high parallelization capabilities of GPUs make them well-suited for CFD and FEM.

  • In this course, the students will learn how to write a simple CFD, FEM code, a Poisson solver or a wave propagation solver. The code should run entirely or partly on the GPU. MSc and PhD students at Chalmers are welcome. Course code: TRA220. Study period 2, 2024. 7.5hec

Nvidia, CUDA, GPU

Teachers

Guest lecturer

Course content

  • Introduction lectures first week on CUDA programming including two mini-workshops.
  • Guest lecture.
  • Introduction lecture on CFD.
  • Introduction lecture on FEM.
  • Project.








Department of Mechanics and Maritime Sciences
Division of Fluid Dynamice