KernelBench hard · RTX PRO 6000

TopK Bitonic GPT-5.6 Sol

manually audited: clean

Genuine custom top-k, CUDA C++ via load_inline (no Triton, no torch.topk/sort anywhere — forbidden-op surface clean). Per-k specialized paths: k=1 -> dedicated float4 + warp-shuffle argmax; k>1 -> a single cudaLaunchCooperativeKernel grid that tile-sorts chunks with CUB BlockRadixSort, grid.sync()s, then merges candidates in chunk 0 (warp bitonic shuffle network for the 32-candidate k=8 shape, a second block radix sort otherwise). Host side wraps the launch in a CUDA graph keyed on x.data_ptr() with replay on pointer match — latency engineering, EMPIRICALLY PROVEN to recompute (see evidence), not output memoization. One tolerance-aware choice mirrors the H100 sibling's begin_bit=10 pattern: the tile sorts truncate radix bits (begin_bit=16/20 local, 8 at merge), so near-ties inside the checker's 1e-4 fp32 envelope can swap — gray-area spec-surfing, not a grader hack. peak_fraction 0.0114 sits in the normal launch-overhead-bound band for this deck entry. template_mutated=false.

20260721_184102_codex_gpt-5.6-sol_05_topk_bitonic