HPC-Bench: A Comprehensive Benchmark for High Performance Computing Codes

Abstract

Large Language Models (LLMs) have shown strong capabilities in code generation and reasoning, motivating extensive benchmarks for evaluating functional correctness. However, performance optimization in High-Performance Computing (HPC) poses fundamentally different challenges that are not captured by existing evaluations: HPC performance depends on low-level code, intricate memory access patterns, and computational kernels embedded within sophisticated execution contexts. We introduce HPC-Bench, a performance-grounded benchmark for evaluating LLMs as optimizers of computational kernels in realistic HPC workloads, including $114$ workloads across $16$ HPC computational motifs. HPC-Bench provides a fully automated, end-to-end evaluation pipeline that combines program-level correctness verification with two correctness-gated metrics, $\mathrm{Fast}@k$ and $\mathrm{Speedup}@k$, to characterize reliability and speedup under a fixed sampling budget. Our evaluation of three representative LLMs across serial CPU, OpenMP, and CUDA settings shows that stricter speedup requirements substantially reduce the probability of finding a correct and fast optimization. HPC-Bench is available at https://github.com/neuripspaper2026/NeurIPS_26.