mojo-gpu-fundamentals

Mojo GPU kernels are plain Mojo functions that rely on Mojo's native abstractions for parallelism rather than CUDA syntax. You define kernel logic directly in Mojo, avoiding common conflations with CUDA patterns to write correct, portable GPU code.

Mojo GPU programming supports cross-vendor targeting across NVIDIA, AMD, and Apple GPUs. This allows you to write portable kernels that execute on supported accelerators without being locked into a single vendor's hardware architecture.

Memory management and synchronization in Mojo GPU kernels are handled through Mojo's built-in abstractions, utilizing barriers to coordinate parallel execution. This approach supports patterns from simple kernels to tiled matmul operations efficiently.

Mojo GPU code runs inefficiently when developers conflate Mojo syntax with traditional CUDA patterns, leading to misconceptions. Correcting these GPU programming misconceptions by using Mojo's native abstractions for memory management and barriers resolves the inefficiency.

You can implement tiled matmul in Mojo by applying the kernel patterns provided for memory management and synchronization. This includes using barriers for coordination to execute efficient parallel computation on supported GPU hardware.