pith:AARY37WB
PerfCodeBench: Benchmarking LLMs for System-Level High-Performance Code Optimization
Current LLMs produce code that is functionally correct but far from expert-optimized on system-level performance tasks.
arxiv:2605.15222 v1 · 2026-05-13 · cs.SE · cs.CL · cs.PL
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Claims
Our evaluation on a broad set of state-of-the-art LLMs shows a clear gap between model-generated code and expert-optimized implementations. The gap is especially large on tasks involving parallelism and GPU operations.
The selected tasks accurately capture realistic system-level implementation choices, hardware-aware optimizations, and performance bottlenecks that matter in practice.
PerfCodeBench reveals that state-of-the-art LLMs produce functionally correct but significantly slower code than expert-optimized versions on system-level tasks, especially those involving parallelism and GPUs.
References
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| First computed | 2026-05-20T00:00:47.041876Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
00238dfec195e6fb32dd9d677260f085f8cad29fd1b6df629cba53839b9cfbfb
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/AARY37WBSXTPWMW5TVTXEYHQQX \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 00238dfec195e6fb32dd9d677260f085f8cad29fd1b6df629cba53839b9cfbfb
Canonical record JSON
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