cuda

Trace a grid into blocks and warps, then distinguish resident work from ready work. Derive an occupancy limit without treating it as a speed target.

Compare the same 1,024 items under mixed and grouped lane assignments. Derive issue-work waste, then include regrouping and memory costs before claiming a speedup.

Derive a block-level occupancy limit, then compare the extra residency with spill traffic and lost reuse. Use a matched experiment rather than a target percentage.

Calculate the data a kernel could reuse, then test whether it stays in L2. Distinguish capacity, cache-policy hints and measured traffic from a guarantee of residency.

Map a non-divisible array onto blocks, guard the final 189 threads, and separate kernel traffic from host transfers without inventing measured timings.

Start with the operation, supported hardware and performance target. Compare ordinary Triton, lower-level Triton interfaces and CUDA using a measured gap that is large enough to justify maintaining custom code.

A canceled request returns its buffers to a pool while device work is still outstanding. Trace ownership through two streams and design a safe reclamation path.

Separate compilation, module loading and execution before judging a faster kernel. Define the artifact and startup evidence needed to release a compiler upgrade.

Derive the ideal gain from filling a partial wave, then account for partial outputs, reduction order, workspace and the epilogue before selecting split-K.

Trace local counts into globally disjoint output ranges. Find the missing block carry and explain why a spin-wait barrier can deadlock the grid.

Review the input contract, adversarial cases, evaluator independence and compiled baseline before trusting a generated GPU kernel or its reported speedup.