AI SummaryQuery a structured, cross-referenced knowledge base of GPU kernel optimization for NVIDIA Blackwell (SM100) and Hopper (SM90). The repository update date is recorded in ; run for current corpus counts. Trigger this skill when the user asks about: Do NOT use this skill for:
Install
Copy this and paste it into Claude Code, Cursor, or any AI assistant:
I want to install the "KernelWiki" skill in my project. Please run this command in my terminal: # Install skill into your project mkdir -p .claude/skills/KernelWiki && curl --retry 3 --retry-delay 2 --retry-all-errors -o .claude/skills/KernelWiki/SKILL.md "https://raw.githubusercontent.com/mit-han-lab/KernelWiki/master/SKILL.md" Then restart Claude Code (or reload the window in Cursor) so the skill is picked up.
Description
Use when the user asks about optimizing NVIDIA Blackwell (SM100, B200) or Hopper (SM90, H100) GPU kernels — tcgen05/TMEM/CLC/NVFP4/2-SM cooperative, warp specialization, FlashAttention-4, DeepGEMM, FlashMLA, MoE, grouped GEMM, CuTe-DSL/PTX/Triton on Blackwell, or wants concrete PR references from CUTLASS/SGLang/vLLM/FlashInfer/PyTorch. Do NOT use for generic CUDA Q&A that is not Blackwell/Hopper-specific, host-side framework integration, or distributed systems (DeepEP/EPLB/DualPipe).
Path 5: Primer, schema, examples
Companion docs under references/: • references/primer.md — topic map: hardware features, techniques, symptoms, canonical page IDs. Read this first when the question is broad. • references/schema.md — condensed frontmatter schema, confidence rules, reproducibility ladder, controlled vocabulary, canonical aliases. • references/examples.md — 10 worked query patterns mapping user questions → command sequences → synthesis.
KernelWiki — Blackwell & Hopper Kernel Optimization Wiki
Query a structured, cross-referenced knowledge base of GPU kernel optimization for NVIDIA Blackwell (SM100) and Hopper (SM90). The repository update date is recorded in README.md; run python3 scripts/repo_status.py for current corpus counts.
When To Use This Skill
Trigger this skill when the user asks about: • Blackwell/SM100 kernel programming — tcgen05.mma, TMEM, CLC, 2-SM cooperative, NVFP4, FP8/FP4 block scaling, PDL/GDC • Kernel implementations — FlashAttention-4, DeepGEMM, FlashMLA, NSA, GatedDeltaNet, NVFP4 GEMM/GEMV, fused MoE, gated dual GEMM • Performance patterns — low SM utilization, memory-bound, register pressure, compute-bound, tail effects, pipeline stalls • DSLs for Blackwell — CuTe DSL, CUDA C++ with PTX inline, Triton on Blackwell • Hopper → Blackwell migration — wgmma → tcgen05, register → TMEM accumulators • PR references — "how did vLLM/SGLang/FlashInfer/CUTLASS/PyTorch implement X for SM100?" • Competition solutions — GPU Mode NVFP4 hackathon, FlashInfer MLSys 2026 submissions Do NOT use this skill for: • Generic CUDA questions unrelated to Blackwell/Hopper tensor cores • Host-side framework integration (model loading, request routing, scheduling policy) • Distributed systems topics — DeepEP, EPLB, DualPipe are out of scope
How To Query
All commands below run from the skill directory (the clone root — the directory this SKILL.md lives in). The scripts auto-resolve the wiki root; no environment variable required.
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