Introduction to Triton Programming: A Practical Tutorial

A comprehensive scientific tutorial designed to provide a full learning path for Triton, a Python-based language and compiler for writing custom GPU kernels. The course covers programming models, language semantics, numerical behavior, and performance optimization, moving from basic vector addition to fused and tiled operators used in modern deep learning systems.

Master the art of high-performance GPU kernel engineering from first principles.

Author: EvoClass

Acknowledgments: Triton documentation and Triton GitHub repository.

1. Define Triton and its role in the deep learning software stack.
2. Distinguish Triton from CUDA, PyTorch eager code, and low-level GPU assembly.
3. Identify which workloads are suitable candidates for Triton and understand the relevance of kernel fusion and bottlenecks.
4. Perform a clean installation of the Triton environment and verify the software stack.
5. Implement a basic vector copy kernel to validate environment logic versus kernel logic.
6. Identify and categorize GPU bottlenecks to justify the use of PyTorch operator fusion.
7. Define a program instance and calculate the dimensions of a 1D launch grid using cdiv.
8. Perform pointer arithmetic to map specific program IDs (pid) to memory offsets.
9. Distinguish between PyTorch tensors (host-side metadata) and Triton tensors (compiler-level blocks).
10. Calculate the mapping between a Program ID (pid) and specific memory offsets using tl.arange.