A Coding Guide to NVIDIA’s Tile-Based GPU Programming: From cuTile and Triton Kernels to Flash Attention
A tutorial demonstrates tile-based GPU programming on NVIDIA GPUs, using cuTile or Triton as backends depending on hardware and CUDA version. It implements vector addition, matrix multiplication, and Flash Attention, verifying correctness against PyTorch. The guide explains the tile model—operating on data tiles rather than individual threads—and provides code examples for both cuTile and Triton kernels.
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This tutorial’s choice of backends—cuTile for CUDA ≥11.8 and Triton for older stacks—mirrors NVIDIA’s controlled fragmentation strategy, ensuring legacy support without ceding control; the tile model itself is solid, but its performance ceiling on GV100-class GPUs (no L2 atomics) caps Flash Attention gains, so the “verifying correctness” claim doesn’t guarantee optimal throughput.