#GPU Kernels

Five entries on this site carry the GPU Kernels tag: four papers and one post, dated 2026. Explore the full list of related work below.

Papers

Operator-Aware Mixed-Precision Tolerance Calibration for Tensor Kernels
Paper · 2026 · arXiv · cited by 1 Derives per-operator, per-dtype tolerances from 8,076 test runs in the gpuemu corpus; calibrated tolerances are far stricter than hand-picked ones and raise bug-detection recall from 73.2% to 82.4%.
Static PTX Metrics Track Structural Kernel Regressions but Miss Semantic Ones
Paper · 2026 · arXiv · cited by 0 Pairs static PTX metrics with measured runtime on five GPU classes: structural kernel bugs are visible in the static signal, semantic bugs that swap a constant compile to identical PTX.
Test-Input Generation for Tensor Programs: What Actually Finds Kernel Bugs
Paper · 2026 · arXiv · cited by 0 Seven test-input generation strategies evaluated on the 26-op gpuemu corpus, reporting bug recall and control false-positive rate.
The Correctness Illusion in LLM-Generated GPU Kernels
Paper · 2026 · arXiv · cited by 6 Fixed-shape allclose checks used by LLM-kernel benchmarks certify buggy kernels as correct. Under op-schema-aware seeded fuzzing with an fp64 reference and per-(op, dtype) tolerances, a 26-op corpus shows 10 of 10 seeded illusions caught and 16 of 16 controls.

Why GPU Kernel Benchmarks Miss Real Bugs
Post · 2026 Passing a correctness benchmark and being correct in production are different claims for LLM-generated GPU kernels — the gap comes down to which inputs get sampled.