The PMPP Book: Programming Massively Parallel Processors: A Hands-on Approach (Amazon link)

• Profiling and Integrating CUDA kernels in PyTorch
• Date: 2024-01-13, Speaker: Mark Saroufim
• Notebook and slides in lecture1 folder

• Recap Ch. 1-3 from the PMPP book
• Date: 2024-01-20, Speaker: Andreas Koepf
• Slides: The powerpoint file lecture2/cuda_mode_lecture2.pptx can be found in the root directory of this repository. Alternatively here as Google docs presentation.
• Examples: Please make sure PyTorch (2.1.2) and cuda-toolkit (nvcc compiler) are installed.
  • lecture2/vector_addition: Classic CUDA C example, to compile use make in the vector_addition directory.
  • lecture2/rgb_to_grayscale: Example uses PyTorch's torch.utils.cpp_extension.load_inline feature to compile a custom RGB to grayscale kernel and uses it to convert input image to grayscale and which is saved in as output.png. Run in the lecture2/rgb_to_grayscale folder python rgb_to_grayscale.py.
  • lecture2/mean_filter: This example also uses the PyTorch's cpp_extension.load_inline feature to compile a mean filter kernel. The kernel read pixel values in the surrounding (square area) of a pixel and computes the average value for each RGB channel individualy. The result is saved to output.png. Run in the lecture2/mean_filter folder python mean_filter.py.

• Title: Getting Started With CUDA
• Date: 2024-01-27, Speaker: Jeremy Howard
• Notebook: See the lecture3 folder, or run the Colab version

• Title: Intro to Compute and Memory Architecture
• Date: 2024-02-03, Speaker: Thomas Viehmann
• Notebook and slides in the lecture4 folder.