GPU Compilation with MLIR (www.stephendiehl.com)

This post continues a hands‑on series showing how to compile high‑level MLIR tensor ops (softmax, attention, etc.) down to Nvidia GPUs by building a small MLIR→GPU toolchain. The author provides practical setup advice (CUDA toolkit installation, how to verify nvcc and nvidia‑smi), a ready-made Docker image with MLIR’s CUDA runner enabled (ghcr.io/sdiehl/docker-mlir-cuda:main) to avoid rebuilding MLIR, and full CMake build flags for compiling llvm-project with MLIR CUDA support (e.g. -DMLIR_ENABLE_CUDA_RUNNER=ON, -DMLIR_ENABLE_NVPTXCOMPILER=ON and CMAKE_CUDA_ARCHITECTURES). This lets compiler engineers generate device code for specific SMs and test MLIR translations without manually reconfiguring complex toolchains. Technically the writeup walks through the CUDA compilation pipeline and runtime semantics that matter for MLIR codegen: nvcc → PTX (architecture‑neutral IR) → CUBIN (device binaries) → FATBIN (multi‑arch bundle) with runtime selection or PTX JIT fallback. It shows kernel launch syntax (<<<blocks, threads>>>), thread/block indexing (blockIdx/threadIdx/blockDim), and an illustrative square kernel with its PTX and SASS disassemblies to highlight how high‑level tensor kernels map to per‑thread parallel loops and memory ops. The significance: enabling MLIR→CUDA lowers transformer primitives to efficient, architecture‑aware GPU code, improving inference/training throughput and giving compiler developers control over PTX/CUBIN generation and cross‑generation compatibility.