@techNmak: GPU Engineering from Scratch Found this excellent free, open-source learning resource that explains GPU architecture an…

GPU Engineering from Scratch 🚀 Found this excellent free, open-source learning resource that explains GPU architecture and performance engineering from the ground up. It's called Before GPU Engineering, a structured digital notebook with 56 chapters listed across four phases. What makes it useful is the approach: instead of jumping straight into CUDA or Triton, it builds an understanding of how GPUs actually execute programs, move data, and handle parallel workloads. What's inside: → CPU vs GPU architecture and parallel computing → Warps, SIMT, threads, and thread blocks → Registers, shared memory, caches, and HBM → Compute-bound vs memory-bound workloads → Kernel launches, synchronization, and race conditions → Memory coalescing and access patterns → Occupancy, latency hiding, and register spilling → Arithmetic intensity and memory bottlenecks → Tiling, matrix multiplication, and GPU performance → Profiling and identifying kernel bottlenecks The explanations connect hardware concepts to problems encountered in AI workloads, including attention and efficient matrix operations. It's primarily a reading resource rather than a hands-on programming course, and some later chapters are still being written. Perfect for AI/ML engineers and software developers who want to understand the hardware behind LLM training and inference before diving deeper into CUDA, Triton, and GPU kernel optimization. Completely free. Here's the GitHub Repo: https://github.com/BEFORE-GPU-ENGINEERING/BEFORE-GPU-ENGINEERING…