mojo-gpu-fundamentals

To port CUDA kernels to Mojo for GPU programming, map CUDA concepts to Mojo equivalents using kernel definitions and TileTensor abstractions. This enables portable parallel code execution across NVIDIA, AMD, and Apple GPUs without relying on CUDA syntax.

Mojo equivalents for CUDA shared memory and synchronization involve using Mojo's GPU primitives and TileTensor abstractions. These provide structured patterns for tiling and managing shared memory within 1D and 2D kernels.

Mojo GPU programming supports running kernels on AMD and Apple GPUs. By replacing CUDA-centric notions with standard Mojo GPU primitives, developers can write portable code that executes correctly across these different hardware platforms.

You write a 1D Mojo kernel template by utilizing Mojo's kernel definitions to copy input buffers to output. This minimal template establishes the foundational parallel structure for more complex GPU porting scenarios.

The best way to manage memory and tiling in Mojo GPU kernels is through TileTensor abstractions. They offer practical examples and patterns for handling 2D kernels and shared memory, translating complex CUDA memory concepts into portable Mojo code.