PerfCodeBench: Benchmarking LLMs for System-Level High-Performance Code Optimization
Large language models (LLMs) can often generate functionally correct code, but their ability to produce efficient implementations for performance-critical systems tasks remains limited. Existing code benchmarks mainly emphasize correctness or algorithmic problem solving, while realistic systems-level optimization is still underexplored. To address this gap, we introduce PerfCodeBench, an executable benchmark for evaluating LLMs on high-performance code optimization. The tasks require system-level implementation choices, hardware-aware optimization, and careful handling of performance bottlenecks. Each task includes executable correctness checks, a baseline implementation, and a reference ...
| Authors | Huihao Jing, Wenbin Hu, Shaojin Chen et al. |
|---|---|
| Primary category | cs.SE |
| Published | 13 May 2026 |
| Last updated at source | 14 July 2026 |
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