Skip to content

README.md

Latest commit

 

History

History
190 lines (111 loc) · 5.55 KB

README.md

File metadata and controls

190 lines (111 loc) · 5.55 KB

Accelerating Scientific & AI Workflows with CUDA & Triton

Most researchers and engineers already have heavy Python code-simulations, numerical solvers, matrix workloads, data transforms, ML ops—that run slowly on CPUs. This course teaches you to accelerate your own real project using GPU programming with CUDA and Triton.

Over 6 weeks, we will go from GPU intuition → CUDA fundamentals → memory optimization → real kernels → Triton → a final mini-project, where you implement and benchmark a GPU-accelerated version of a problem you personally care about.

Goals:

By the end of this course, you will be able to:

  • Understand when and why GPUs outperform CPUs
  • Map your compute to the CUDA execution model (threads, blocks, grids)
  • Use the GPU memory hierarchy effectively (global, shared, constant memory)
  • Write and optimize CUDA kernels for math-heavy workloads
  • Use Triton to write concise, high-performance GPU kernels in Python
  • Profile your compute and quantify speedups vs CPU implementations
  • Accelerate a real Python-based research problem—or your own project

Who This Course Is For

  • Researchers (CS, physics, engineering, biology, economics, etc.)
  • Engineers and developers with compute-heavy workloads
  • ML practitioners building custom layers or operations
  • Anyone who wants their Python code to run 10×–100× faster

Prerequisites:

  • Python and NumPy (required)
  • Some C/C++ helpful but not required
  • Basic linear algebra knowledge

Course Structure (6 Weeks)

Each week contains:

  • Concepts & readings from NVIDIA docs and academic lectures
  • Hands-on coding assignments
  • Weekly submission
  • Progress towards the mini-project

Weekly breakdown:

  1. GPU Intuition & Compute Foundations
  2. Parallel Thinking & CUDA Basics
  3. Memory Hierarchy & Performance Optimization
  4. Real Kernels: GEMM, softmax, compute patterns
  5. Modern GPU Programming with Triton
  6. Mini-Project: Accelerate Your Own Research Code

Each week will have its own markdown file under weekX.md.

Final Mini-Project

You will:

  1. Choose a real compute-heavy Python task from your research or interests
  2. Profile the CPU implementation
  3. Rewrite the bottleneck(s) using CUDA and/or Triton
  4. Benchmark and visualize the speedup
  5. Submit a short write-up or slide deck summarizing the problem, GPU approach, and results

Examples of suitable problems:

  • PDE or simulation step (finite-difference, Monte-Carlo, particle update)
  • Numerical algorithms (scan, reduction, iterative solvers)
  • Custom ML ops (attention block, loss function, activation)
  • Data transformations (pairwise distances, feature extraction, filtering)

You finish the course with:

  • A GPU-accelerated version of something meaningful
  • A clean codebase
  • Techniquees you can use in research papers or engineering work

Tools You Will Use

  • CUDA Toolkit and the official CUDA Programming Guide
  • Python + PyTorch/NumPy
  • Triton for high-level GPU programming
  • NVCC, Nsight Systems, Nsight Compute, and timing via torch.cuda.Event
  • Git & GitHub for submissions

Key References

Getting Started

  1. Fork this repository
  2. Ensure access to an NVIDIA GPU (local or cloud)
  3. Install the CUDA Toolkit + Python environment
  4. Start with Week 1 → weeks/week1.md

Here is a clean, concise section you can paste directly into the root README.md. It only includes two options: your own NVIDIA laptop, or Google Colab — and gives setup steps for both.

GPU Access for This Course

To complete this course, you need access to an NVIDIA GPU. You have two simple options:

Option 1 — Use Your Own Laptop (Recommended if you have an NVIDIA GPU)

If your laptop/PC has an NVIDIA GPU (e.g., GTX/RTX series), you can run all CUDA and Triton code locally.

Setup steps:

  1. Install NVIDIA GPU drivers https://www.nvidia.com/Download/index.aspx

  2. Install the CUDA Toolkit (version 12.x or latest) https://developer.nvidia.com/cuda-downloads

  3. Verify CUDA is installed:

    nvcc --version

  4. Use a Python environment:

    conda create -n gpu python=3.10
conda activate gpu

  5. Install PyTorch with CUDA support (example for CUDA 12.1):

    pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121

If your GPU is recent (RTX 20-series or newer), you’ll be able to run everything comfortably.

Option 2 — Use Google Colab (No GPU Laptop Needed)

If you don’t have a machine with an NVIDIA GPU, you can still do 100% of the course on Google Colab.

  1. Open a new Colab notebook https://colab.research.google.com/

  2. Enable the GPU runtime:

    Runtime → Change runtime type → GPU

  3. Verify GPU access in a notebook cell:

    !nvidia-smi

Colab provides free access to NVIDIA T4/P100/V100 GPUs and works fully with CUDA and Triton.