@DanKornas: GPU engineering is too broad to learn from random tabs. Awesome GPU Engineering is a curated GitHub list of resources f…

GPU engineering is too broad to learn from random tabs. Awesome GPU Engineering is a curated GitHub list of resources for learning GPU engineering across architecture, kernel programming, optimization, distributed systems, and AI acceleration. It helps you build a cleaner learning map by grouping books, frameworks, profilers, systems tools, courses, papers, and interview topics in one scan-friendly place. Key features: • Learning path coverage – starts with foundational books like Programming Massively Parallel Processors and CUDA by Example • Framework map – links CUDA, ROCm, OpenCL, SYCL / oneAPI, Vulkan Compute, Metal, and Mojo • Performance toolbox – points to Nsight, CUTLASS, TensorRT, Triton, and the Roofline Model • Multi-GPU + AI systems – collects NCCL, vLLM, Accelerate, TensorRT-LLM, Horovod, DeepSpeed, and Megatron-LM • Study material + interview prep – includes courses, papers, learning tools, and GPU systems design topics It’s open-source (CC BY 4.0 license). Link in the reply