NVIDIA/cutile-python
cuTile is a programming model for writing parallel kernels for NVIDIA GPUs
What it solves
cuTile Python provides a way to write high-performance GPU kernels using Python, allowing developers to express parallel computations on NVIDIA GPUs without needing to write low-level CUDA C++ code.
How it works
It acts as a programming language for NVIDIA GPUs that generates kernels based on Tile IR (Intermediate Representation). It allows users to define kernels using a @ct.kernel decorator and manage data movement between memory levels using load and store operations on tiles of data.
Who it’s for
Developers who want to program NVIDIA GPUs for high-performance computing and AI workloads, specifically those using Blackwell, Ampere, or Ada GPU architectures.
Highlights
- Python-based GPU Programming: Write GPU kernels directly in Python.
- Tile-based Memory Management: Explicit control over loading and storing data tiles for optimized performance.
- Tile IR Integration: Leverages Tile IR to generate efficient GPU kernels.
- Experimental Autotuning: Includes an experimental package for autotuning kernel launches.
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