DiogenesChen122/Dr.Sparse-GPT56Luna-eval-b200-rl562-spgemm
Dr.Sparse — GPT-5.6 Luna SpGEMM eval on the 562-matrix pool Snapshot state: complete (generated 2026-09-19 08:03 UTC) phase results explore (3 branches x 5 iterations) 1686 / 1686 exploit (top-2 branches x 10 iterations) 1124 / 1124 matrices covered 562 / 562 What this is Tree-search SpGEMM kernel optimization over the 562 matrices of KinGeorge/Dr.Sparse-RL-train-562, run on NVIDIA B200 (sm_100) and scored against cuSPARSE. Model:… See the full description on the dataset page: https://huggingface.co/datasets/DiogenesChen122/Dr.Sparse-GPT56Luna-eval-b200-rl562-spgemm.
Dr.Sparse — GPT-5.6 Luna SpGEMM eval on the 562-matrix pool
Snapshot state: complete (generated 2026-09-19 08:03 UTC)
What this is
Tree-search SpGEMM kernel optimization over the 562 matrices of KinGeorge/Dr.Sparse-RL-train-562, run on NVIDIA B200 (sm_100) and scored against cuSPARSE.
- Model:
openai/gpt-5.6-lunavia OpenRouter,reasoning: {effort: high}on every call. - Search: per matrix, 3 explore branches of 5 iterations, then the top 2 branches get 10 more iterations. 2,810 GPU elements total, 30 concurrent, one B200 per element so no benchmark shares a GPU.
- Scoring: compile ->
compute-sanitizer memcheck-> benchmark vs cuSPARSE -> NCU on correct kernels.
Layout (inside the tarball)
rl562_b200_GPT56Luna_openrouter/spgemm/<level>/SpGEMM_<matrix>/
tree_search_state.json # the 3 planned strategies
branch_<i>_<label>/ # per-branch workspace: kernel_iter_*.cu, chat_iter_*.json, NCU
branch_<i>_result.json # explore result (status, best_speedup, iterations)
branch_<i>_exploit_result.json # exploit result for the top-2 branches
branch_array/gpt56luna-rl562-spgemm-42497038/
explore_manifest.csv exploit_manifest.csv config.yaml
plan_logs/ explore_logs/ exploit_logs/ master-*.logUnpack with tar -I zstd -xf rl562_b200_GPT56Luna_spgemm.tar.zst.
