Writing custom CUDA kernels provides direct control over GPU computation and insight into how PyTorch operations work at the hardware level. This tutorial walks through implementing LayerNorm using CUDA C++. I wrote this tutorial as a way to get back into CUDA after having being introduced to it when I was a Teaching Assistant for a Parallel Computing Course during my PhD.

The tutorial covers the complete implementation process: from basic CUDA kernel structure to PyTorch integration using the C++ extension API. Rather than using existing optimized implementations, we build LayerNorm from scratch to understand GPU memory access patterns, thread coordination, and numerical stability considerations.

In this tutorial, I get 2x speedup over PyTorch's LayerNorm. While the performance benefits of 2x faster LayerNorm are not significant (as compared to e.g. MatMul), the concepts are still useful for understanding how PyTorch operations work at the hardware level.

What you'll learn

  • CUDA kernel development fundamentals
  • GPU memory management and access patterns
  • Thread block coordination and reduction operations
  • PyTorch C++ extension integration
  • Performance profiling and optimization strategies

Prerequisites

Basic PyTorch, C++, and some neural net concepts. Don't need any CUDA kernel knowledge as hopefully the tutorial can teach you from the ground up.

Tutorial

The complete tutorial with code, explanations, and my rough benchmarks is available on GitHub:

https://github.com/avishkarsaha/tutorials/tree/main/layernorm_cuda

Really the goal of the tutorial was about how to think about GPU parallelism when translating mathematical operations to CUDA kernels.