A Coding Guide to NVIDIA's Tile-Based GPU Programming

In this tutorial, we explore TileGym GPU programming by building a practical Colab workflow that runs across different hardware conditions. We begin by probing the available CUDA environment, checking whether NVIDIA cuTile runs directly, and falling back to Triton when standard Colab GPUs lack the required cuTile stack. Through this setup, we learn the core tile-programming idea: instead of writing code for one thread at a time, we operate on entire data tiles, load them into the kernel, compute on them efficiently, and store the results back.

We use this model to implement vector addition, fused GELU, row-wise softmax, tiled matrix multiplication, and flash attention, while comparing each result against PyTorch for correctness and benchmarking. We begin by setting up the environment, importing the required libraries, and checking whether CUDA is available on the current runtime. We inspect the GPU capabilities, CUDA version, and PyTorch setup to determine whether the real cuTile backend is usable.

We then select the active execution backend, explain the tile programming model, and store reference cuTile kernel source strings for comparison. We define the Triton implementations for vector addition, fused GELU, row softmax, tiled matrix multiplication, and flash attention. We express each operation using tile-level loads, computations, reductions, dot products, and stores, so that the GPU can handle blocks of data efficiently.

We also provide pure PyTorch fallback functions so the tutorial still runs when Triton or a supported GPU backend is unavailable. We build benchmarking and correctness-checking utilities that compare each custom kernel against a PyTorch reference implementation. We then run the vector-addition kernel and verify that the tile-based output matches the standard PyTorch addition.

We also test the fused GELU kernel, demonstrating how multiplication, bias addition, and GELU activation are combined into a single efficient pass. We run the row-wise softmax kernel and compare it against PyTorch's softmax to verify numerical correctness. We then perform tiled matrix multiplication, multiplying matrix blocks and accumulating along the K dimension.

We benchmark these kernels against PyTorch to observe how tile-based execution performs on the active backend. We finish with the flash attention kernel, which applies online softmax to compute attention without materializing the full attention matrix. We compare its output to PyTorch's scaled dot-product attention and benchmark runtime performance when a GPU backend is available.

We close the tutorial by summarizing the backend we used and the main tile programming concepts we learned. The ability to efficiently program GPUs is crucial for developers working on AI and machine learning applications. This tutorial demonstrates a practical approach to tile-based GPU programming using NVIDIA's cuTile and Triton kernels.