From CUDA to Multi-GPU: A Curated GitHub Repo for GPU Engineering
tom_doerr · x · 2026-08-12
Developer goabiaryan has open-sourced awesome-gpu-engineering on GitHub, a curated repository designed to help engineers systematically master GPU engineering for AI systems.
The repo covers core dimensions ranging from low-level architecture to large-scale distributed training:
- Foundational Books: Includes canonical texts on CUDA and parallel computing, such as Programming Massively Parallel Processors.
- Programming Frameworks: Catalogs major compute stacks including NVIDIA CUDA, AMD ROCm, Intel oneAPI, and Vulkan Compute.
- Systems & Optimization: Features resources on kernel programming, memory hierarchy optimization, and multi-GPU coordination for AI acceleration.
This serves as a highly technical index and learning guide for AI engineers looking to understand compute internals, optimize performance, or build large-scale training clusters.
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