A Coding Guide to NVIDIA’s Tile-Based GPU Programming: From cuTile and Triton Kernels to Flash Attention

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.

CUDA Environment Probe

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import os, sys, math, time, textwrap def rule(t=""): print("\n" + "=" * 78) if t: print(t) print("=" * 78) rule("0. ENVIRONMENT PROBE") try: import torch except ImportError: print("Installing torch ...") os.system(f"{sys.executable} -m pip install -q torch") import torch HAS_CUDA = torch.cuda.is_available() DEV = "cuda" if HAS_CUDA else "cpu" cc = (0, 0) if HAS_CUDA: cc = torch.cuda.get_device_capability() print(f"GPU : {torch.cuda.get_device_name(0)}") print(f"Compute capability : {cc[0]}.{cc[1]}") print(f"Torch CUDA runtime : {torch.version.cuda}") print(f"Driver / torch : {torch.version}") else: print("No CUDA GPU found. In Colab: Runtime -> Change runtime type -> GPU (T4).") print("The tutorial will still run its correctness math on CPU where possible.") CUTILE_HW_OK = HAS_CUDA and cc[0] >= 8 CUDA_MAJOR = int((torch.version.cuda or "0").split(".")[0]) if HAS_CUDA else 0 CUTILE_TOOLKIT_OK = CUDA_MAJOR >= 13 rule("1. ATTEMPTING REAL cuTile (NVIDIA CUDA Tile) BACKEND") ct = None CUTILE_READY = False if CUTILE_HW_OK and CUTILE_TOOLKIT_OK: try: import cuda.tile as ct CUTILE_READY = True print("cuda.tile is already importable.") except Exception: print("Installing cuda-tile[tileiras] (this can take a while)...") os.system(f"{sys.executable} -m pip install -q 'cuda-tile[tileiras]' cupy-cuda13x") try: import cuda.tile as ct CUTILE_READY = True except Exception as e: print("cuTile import still failed:", repr(e)) else: reasons = [] if not HAS_CUDA: reasons.append("no CUDA GPU") if HAS_CUDA and cc[0] add -> store tile)") print("cuTile version of this kernel:\n" + CUTILE_SOURCE["vector_add"]) n = 1 tensor cores)") print("cuTile version of this kernel:\n" + CUTILE_SOURCE["matmul"]) M = N = K = 1024 a = torch.randn(M, K, device=DEV, dtype=dtype) b = torch.randn(K, N, device=DEV, dtype=dtype) check("matmul", run_matmul(a, b), a @ b, atol=1e-1, rtol=1e-1) if BACKEND != "torch": t = bench(run_matmul, a, b) flops = 2 * M * N * K print(f" {BACKEND}: {t:.4f} ms ({flops/ (t*1e-3) / 1e12:.2f} TFLOP/s) " f"torch: {bench(lambda x,y:x@y, a, b):.4f} ms")

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.

Flash Attention Kernel

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rule("KERNEL 5 — FLASH ATTENTION (online softmax; the advanced capstone)") Z, L, D = 8, 512, 64 q = torch.randn(Z, L, D, device=DEV, dtype=dtype) k = torch.randn(Z, L, D, device=DEV, dtype=dtype) v = torch.randn(Z, L, D, device=DEV, dtype=dtype) ref = torch.nn.functional.scaled_dot_product_attention(q, k, v) check("flash_attn", run_flash(q, k, v), ref, atol=2e-2, rtol=2e-2) if BACKEND != "torch": sdpa = lambda q,k,v: torch.nn.functional.scaled_dot_product_attention(q,k,v) print(f" {BACKEND}: {bench(run_flash, q, k, v):.4f} ms " f"torch sdpa: {bench(sdpa, q, k, v):.4f} ms") rule("DONE") print(f""" Summary ------- Backend that ran : {BACKEND} What you learned : the tile programming model (whole-tile load/compute/store), fusion, tile reductions, K-loop matmul on tensor cores, and an online-softmax flash-attention kernel. To run the REAL cuTile kernels shown above you need CUDA 13.1+ and an Ampere/Ada/Blackwell GPU. On such a machine: pip install 'cuda-tile[tileiras]' cupy-cuda13x pip install tilegym[tileiras] Then the strings in CUTILE_SOURCE run as-is via ct.launch(...). """)

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.

Conclusion

In conclusion, we understood how tile-based kernels map high-level mathematical operations onto efficient GPU execution patterns. We saw how fusion reduces memory traffic, how tile reductions stabilize and make softmax efficient, how tiled matrix multiplication accumulates over K-blocks, and how flash attention uses online softmax to avoid materializing the full attention matrix. We also gained a path for experimentation: we ran Triton kernels on common Colab GPUs today while still seeing how the same concepts translate to real cuTile kernels on newer CUDA 13.1+ Ampere, Ada, or Blackwell systems.

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