Tutorialmlircudanvptxdocker
MLIR Targets Nvidia GPUs With CUDA Compiler
8.0
A hands-on guide shows how to lower MLIR tensor operations to CUDA and run them on Nvidia GPUs, providing Docker images and step-by-step build instructions (including CUDA toolkit versions 12.1–12.8 and nvcc). It explains the CUDA compilation chain (nvcc→PTX→CUBIN→FATBIN), kernel launch semantics, and how to compile LLVM/MLIR with the CUDA runner to produce GPU binaries for performance testing.
Key Points
- 1Provides reproducible MLIR-to-CUDA workflow with Docker image and install/build steps for CUDA toolkits
- 2Explains nvcc compilation stages from PTX to CUBIN/FATBIN and driver CUDA version importance
- 3Enables compiling MLIR with CUDA runner to generate PTX/CUBIN binaries for GPU performance optimization
Scoring Rationale
Practical, reproducible setup and Docker image enable immediate experimentation, limited novelty beyond tooling and tutorial-level guidance.
Sources
Public references used for this report.
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