Tracks course: TRA220, GPU-accelerated Computational Methods using Python and CUDA
Student reports 2023/2024
- Erik Hasselwander, Yuhua Cheng, Kyriakos Gavras
"GPU-accelerated computational methods using Python and CUDA"
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- Pontus Malmsköld, Ritoban Biswas
"GPU-accelerated Computational Methods using Python and CUDA"
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- Benedick Allan Strugnell-Lees, Joar Forsberg, Viktor Sundström
"Opportunities for GPU acceleration in CFD"
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- Afroditi Tzanetou, Arik Ben-Shabat, Oweis Al-Karawi, Robert F. Birkisson, Simon Riis
"GPU-Accelerated Computational Methods for FEM Using Python and CUDA"
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Student reports 2022/2023
- Greeshma Ajayakumar, Jakub Fojt, Jian Tan, Leonard Nielsen
"Solving the Poisson equation with GPU acceleration"
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- Panagiotis Moraitis, Johannes Hansson, Weilong Chen
"GPU-accelerated computational methods
using Python and CUDA"
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- Marios Aspris, Xingyuan Li, Andhika Pratama, Patricia Vanky
"Acceleration of CFD Python code using CUDA"
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- Congxiao Zhang and Gayana Jinde Radhakrishna
"Finite Element for 2D Solid Mechanics:
GPU Accelerated Numerical Method with Python and CUDA"
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- Stefano Ribes
GPU-accelerated Computational Methods using Python
and CUDA
Open at Gihub
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, 2023. 7.5hec
Nvidia, CUDA, GPU
Teachers
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