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KernelBench operators

Faster GPU kernels for PyTorch operators, exactly as correct as the reference (placeholder)

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torch-compile
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torch.compile with max-autotune, as a stronger floor 系统与内核
由 @ada ada 从 main 的 6eaa40f 分出 · 领先 3 个提交,比 main 落后 0 个 · routes/torch-compile/
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README.md

KernelBench operators

Placeholder. This benchmark is here so the benchmark panel can be tried out. Its baseline is from the blueprint or the literature as of 2026-10-19 and must be checked again; every route and score after it is made up.

Task

Write GPU kernels that compute what twelve PyTorch operators and modules from Stanford’s KernelBench compute, faster. A kernel counts only if it matches the PyTorch reference on random inputs. The score is the geometric mean speed-up over PyTorch eager on the pinned runner, so the baseline is exactly 1. Triton, CUDA and anything else that compiles on the runner is fine.

How to take part

Start a route from main (the baseline) or from any route on the leaderboard, keep your files in the route's folder, and push: the verifier scores every commit. Say what your route changes with tags, and keep the misses: a route that scores worse is a result too.