@pvergadia: NVIDIA CUDA turned the GPU into a general-purpose compute engine. Before NVIDIA CUDA, scientists had to launder their linear algebra through graphics metaphors just to get GPU acceleration. So why does it work? 🏗️ Scalability: CUDA organizes work into Grids, Blocks, and Threads. Because blocks execute independently, your code scales automatically from a consumer laptop to an enterprise H100 rack. ⚠️ The Warp and The Memory Wall: The hardware executes 32 threads in lockstep (a Warp). Branching logic here kills performance. Compete is bottlenecked by the Memory Wall. The true craft of CUDA is managing the memory hierarchy keeping hot data in fast shared memory and coalescing accesses. 🏰 The Moat: NVIDIA built cuDNN, TensorRT, NCCL, and Nsight. They built the entire logistics network. Check out the attached sketchnote and the blog for a deeper visual guide on how the architecture, execution model, and memory hierarchy all fit together. #NVIDIA #CUDA #MachineLearning #AIInfrastructure #GPUArchitecture

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Monday 13 April 2026 15:51:01 GMT