mojo-gpu-fundamentals

Map CUDA-like GPU patterns to Mojo semantics for kernels and memory management.

Updated Aug 20, 2022

Search Tags:#synchronization #gpu #kernel #portability #mojo #layouttensor

Ready to run

npx skills add https://github.com/Neroro64/dotfiles --skill mojo-gpu-fundamentals-neroro64

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: mojo-gpu-fundamentals
Source: https://github.com/Neroro64/dotfiles/tree/main/configs/omp/agent/skill_store/mojo-gpu-fundamentals
Command: npx skills add https://github.com/Neroro64/dotfiles --skill mojo-gpu-fundamentals-neroro64

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Mojo GPU programming has no CUDA syntax. This skill provides precise Mojo equivalents and patterns to write GPU code correctly for multiple accelerators, reducing misinterpretations and mistakes.

Core Features & Use Cases

Mojo GPU syntax mapping: explains how CUDA-like patterns translate to Mojo semantics (kernels, enqueuing, memory, synchronization).
Architecture-aware guidance: covers differences across NVIDIA, AMD, and Apple GPUs for portability.
Practical workflows: provides end-to-end examples for kernels, layouts, shared memory, and host-device interaction.

Quick Start

Create a minimal Mojo GPU kernel that doubles an input array using the conventions in this guide.

Frequently Asked Questions about mojo-gpu-fundamentals

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I write GPU kernels in Mojo without using CUDA syntax?▼

To write GPU kernels in Mojo without using CUDA syntax, map CUDA-like patterns to Mojo-native primitives and LayoutTensor usage, ensuring correct kernel definitions, memory management, and host-device interaction across supported accelerators.

What is the best way to manage memory and synchronization for Mojo GPU programming?▼

The best way to manage memory and synchronization in Mojo GPU programming is by applying Mojo-native primitives and LayoutTensor workflows, which provide concrete mappings for device memory allocation and thread synchronization across NVIDIA, AMD, and Apple GPUs.

Does Mojo GPU code support cross-architecture portability for AMD and Apple GPUs?▼

Yes, Mojo GPU code supports cross-architecture portability across NVIDIA, AMD, and Apple GPUs by using Mojo-native primitives and LayoutTensor, providing architecture-aware guidance to ensure correct execution on multiple accelerators.

How do I map shared memory and host-device interaction patterns from CUDA to Mojo?▼

You map shared memory and host-device interaction patterns from CUDA to Mojo by translating them into Mojo semantics, utilizing LayoutTensor for data layouts and Mojo-native primitives for enqueuing kernels and managing shared memory.

Why should I use LayoutTensor for GPU programming in Mojo?▼

You should use LayoutTensor for GPU programming in Mojo because it satisfies requirements for Mojo-native primitives and cross-architecture portability, providing concrete examples and mappings for managing memory layouts across NVIDIA, AMD, and Apple GPUs.

What are the limitations of porting CUDA patterns directly to Mojo GPU code?▼

The limitation of porting CUDA patterns directly to Mojo GPU code is that Mojo has no CUDA syntax, so you must avoid CUDA syntax entirely and translate patterns into Mojo semantics and LayoutTensor usage to prevent misinterpretations.