shared-memory

Why naive matmul on a GPU is bandwidth-starved, and how tiling with __shared__ memory reduces global memory traffic by a factor of the tile size. The classic optimisation, with the kernel that demonstrates it.

The five memory spaces a CUDA kernel can see and why they have wildly different speeds. Global vs shared vs constant vs local vs registers, coalesced access, bank conflicts, and a cheat-sheet table you'll actually reference.