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What happens when a GPU reads memory

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Fergus Finn

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What happens when a GPU reads memory is Fergus Finn's long technical article on the Doubleword AI blog that traces a single CUDA global load (LDG) on an NVIDIA RTX 4090. Finn follows the instruction from warp issue through coalescing, L1 and TLB behavior, crossbar traversal, L2, and DRAM access patterns.

The post pairs microbenchmark results with SASS-level inspection so readers can see how latency and bandwidth accumulate along the path. Finn discusses why seemingly innocent address patterns stall, how cache lines interact with warp-wide loads, and what optimization levers remain once kernels are already coalesced.

You need a solid grasp of CUDA warps, memory spaces, and profiler basics before diving in. The material is advanced and hardware-specific, though the mental model transfers qualitatively to other NVIDIA generations.

The article is freely readable on blog.doubleword.ai as a single page with diagrams. It complements official CUDA tuning guides by narrating one load in forensic detail rather than listing best-practice bullet points.

Finn's GPU memory deep dives have been shared on Hacker News and among CUDA performance engineers; this LDG walkthrough is regarded as unusually concrete for teaching hardware-aware kernel tuning.