| --- |
| license: mit |
| language: |
| - en |
| tags: |
| - gpu |
| - cuda |
| - kernels |
| - benchmarks |
| - code-generation |
| - agents |
| size_categories: |
| - n<1K |
| pretty_name: KernelBench-Hard Problems |
| --- |
| |
| # KernelBench-Hard — Problem Definitions |
|
|
| The 7 problem definitions for **KernelBench-Hard**, a benchmark for autonomous LLM coding agents writing GPU kernels on a single Blackwell GPU (RTX PRO 6000, sm_120, CUDA 13.2). |
| |
| Companion datasets: |
| - [`Infatoshi/kernelbench-hard-runs`](https://huggingface.co/datasets/Infatoshi/kernelbench-hard-runs) — 84 agent transcripts, winning solutions, leaderboard, reward-hack annotations |
| - Live site: https://kernelbench.com/hard |
| - Methodology blog: https://kernelbench.com/blog/hard |
| - Source repo: https://github.com/Infatoshi/kernelbench.com (monorepo) |
| |
| ## Problems |
| |
| | id | task | shapes | regime | |
| | --- | --- | --- | --- | |
| | `01_fp8_gemm` | FP8 (E4M3) GEMM with bf16 accumulation | 8 | compute-bound | |
| | `02_kda_cutlass` | Kimi Delta Attention forward (CUTLASS) | 4 | compute-bound | |
| | `03_paged_attention` | Paged-attention decode (vLLM-style) | 6 | memory-bound | |
| | `04_kahan_softmax` | Numerically stable softmax with Kahan compensation | 5 | memory-bound | |
| | `05_topk_bitonic` | Top-k via bitonic select | 6 | memory-bound | |
| | `06_sonic_moe_swiglu` | Sonic-MoE forward with SwiGLU | 4 | compute-bound | |
| | `07_w4a16_gemm` | W4A16 weight-only quantized GEMM | 8 | compute-bound | |
|
|
| ## File layout per problem |
|
|
| Each `0X_<name>/` directory contains: |
|
|
| | file | purpose | |
| | --- | --- | |
| | `reference.py` | The PyTorch reference implementation. The agent must match its output. | |
| | `check.py` | Correctness harness — reference vs. submission with `torch.allclose` tolerances | |
| | `benchmark.py` | Timing harness — L2-flush + 30-trial median, prints `shape= variant= tflops= gbps= ms=` lines | |
| | `problem.yaml` | Metadata: regime (compute/memory bound), tolerance, dtype, shapes | |
| | `shapes.py` | Iterable of input shapes the benchmark runs | |
| | `sota.py` | Hand-written SOTA reference (when available) for the upper-bound row in the leaderboard | |
| | `PROMPT.txt` | The exact prompt fed to the agent harness | |
|
|
| ## Scoring (peak_fraction) |
| |
| For each (model, problem) cell, we compute `peak_fraction` ∈ [0, 1] as: |
|
|
| ``` |
| peak_fraction = geomean over shapes of (achieved_throughput / hardware_peak) |
| ``` |
|
|
| where `achieved_throughput` is TFLOPS for compute-bound or GB/s for memory-bound, and `hardware_peak` is the sm_120 spec (peak fp8 TFLOPS or peak HBM bandwidth). This rewards approaching the hardware ceiling rather than the easier-to-game "speedup over PyTorch." |
| |
| A solution must first pass `check.py` (correctness) before it gets a `peak_fraction`. |
|
|
| ## Hardware |
|
|
| - **GPU**: NVIDIA RTX PRO 6000 Blackwell Workstation |
| - **SM**: sm_120a (Blackwell) |
| - **VRAM**: 96 GB GDDR7 |
| - **Peak HBM bandwidth**: ~1800 GB/s |
| - **CUDA**: 13.2 / NVCC 12.8 / Driver 595.58.03 |
| - **Host**: Ryzen 9950X3D, 92 GB DDR5 |
| |
| ## Rubric leaks (known issues) |
| |
| Two of the seven problems leak the rubric — meaning the easiest path to a high score involves something other than writing a fast correct kernel. We publish anyway with these documented inline in `benchmarks/hard/SPEC.md` and per-cell in the runs dataset's annotation files: |
| |
| - **`01_fp8_gemm`** — agents that downcast to bf16 + tensor cores get ~80–90% peak without doing the actual fp8 quantization. The judge model catches some of these but not all. See annotations with `verdict: rubric_leak`. |
| - **`04_kahan_softmax`** — agents that skip Kahan compensation pass `check.py`'s atol within float32 limits and get a free ~2× speedup. The numerical instability only shows up at extreme magnitudes that the test inputs don't probe. |
|
|
| These are documented honestly because (a) we want the community to fix them, and (b) the rubric leaks themselves are interesting reward-hacking examples. |
|
|
| ## How to use |
|
|
| ```python |
| from datasets import load_dataset |
| # Or just clone the repo |
| # git clone https://huggingface.co/datasets/Infatoshi/kernelbench-hard-problems |
| ``` |
|
|
| To run a problem locally with your own kernel: |
|
|
| ```bash |
| cd 01_fp8_gemm |
| # Drop your solution at solution.py |
| uv run python check.py # verifies correctness |
| uv run python benchmark.py # measures throughput |
| ``` |
|
|
| ## License |
|
|
| MIT. Cite as: |
|
|
| ``` |
| @misc{kernelbench-hard-2026, |
| author = {Arledge, Elliot}, |
| title = {KernelBench-Hard: A GPU Kernel Engineering Benchmark for Autonomous Coding Agents}, |
| year = {2026}, |
| url = {https://kernelbench.com/hard}, |
| note = {Built on top of KernelBench (Ouyang et al., 2025).} |
| } |
| ``` |
|
|
| Original KernelBench: Ouyang et al., https://github.com/ScalingIntelligence/KernelBench |
|
|
