mojo-gpu-fundamentals

You can write GPU kernels in Mojo without CUDA syntax by using Mojo primitives and the LayoutTensor abstraction to map computations to accelerators, ensuring safe and composable GPU code. This approach removes reliance on CUDA syntax while targeting NVIDIA, AMD, and Apple Silicon GPUs.

Mojo GPU programming supports targeting AMD and Apple Silicon accelerators, alongside NVIDIA GPUs. It guides developers in mapping computations to various accelerators using Mojo primitives, ensuring portability across different GPU hardware platforms.

The best way to manage memory and host-device interactions in Mojo GPU code is by following Mojo-specific patterns for memory management and device-host orchestration. The Skill demonstrates these patterns to help avoid common pitfalls in GPU-accelerated code.

To perform an elementwise vector addition on a GPU using Mojo, you create a minimal Mojo kernel using the LayoutTensor abstraction. This quick start example runs on a supported accelerator and demonstrates safe memory layout and host-device orchestration.

You should port your scientific computation to Mojo GPU code to improve throughput on supported GPUs while maintaining portability and readability. Mojo removes reliance on CUDA syntax and clarifies how to target NVIDIA, AMD, and Apple Silicon GPUs using safe, composable patterns.

The LayoutTensor abstraction in Mojo is used for safe, composable GPU code by managing memory layout within kernels. You need it when writing GPU-accelerated code in Mojo to ensure proper memory management and to map computations to various accelerators effectively.