NVIDIA cuTile Python Tutorial: Building Tiled GPU Kernels

This tutorial implements an advanced hands-on workflow for NVIDIA cuTile Python, a tile-based GPU programming interface for writing efficient CUDA-style kernels directly in Python. The process begins with preparing a Colab-friendly environment, checking the available GPU, driver, CUDA, and cuTile installations before running any kernel code. The tutorial then builds tiled examples for vector addition, matrix addition, and matrix multiplication, while keeping a PyTorch fallback.

This approach allows the notebook to remain executable even when Colab does not meet cuTile's latest runtime requirements. Through this approach, we understand how tiled programming works, how tensors are loaded, computed, stored, and validated, and how custom GPU kernels can be compared against standard PyTorch operations. Preparing the Colab environment involves installing the required Python packages and attempting to install cuTile Python.

The available runtime is inspected by checking Python, GPU, CUDA, and NVIDIA driver availability. A decision is made whether the notebook can use the real cuTile backend or should continue with the PyTorch fallback. Helper functions are defined to make the tutorial easier to run, test, and benchmark.

These functions synchronize GPU execution, measure runtime across multiple repeats, and organize benchmark results into readable tables. A correctness-checking function is added to compare each custom operation against the expected PyTorch output. The main cuTile kernels for vector addition, matrix addition, and matrix multiplication are defined when cuda.tile is available.

Tiled load, store, gather, scatter, and matrix-multiply operations are used to illustrate how GPU computation is structured in cuTile. These kernels are then wrapped inside Python functions that automatically fall back to PyTorch when the current runtime does not support cuTile. The actual examples for tiled vector addition, matrix addition, float32 matrix multiplication, and float16 matrix multiplication are run.

Random tensors are created, the tutorial functions are executed, and the results are compared with standard PyTorch operations. Tensor shapes and sample outputs are printed to confirm that every stage behaves as expected. The tutorial operations are benchmarked and their median runtimes are compared with those of equivalent PyTorch operations.

The benchmark results are visualized using a simple bar chart to make the performance comparison easier to understand. Practical next experiments are listed, such as tile-size tuning, precision comparison, operation fusion, and the study of advanced cuTile samples like attention. Why this matters: This tutorial provides a comprehensive workflow for cuTile Python, covering environment setup, kernel definition, execution, validation, and benchmarking.