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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.13772 |
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| _version_ | 1866912281336479744 |
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| author | Cui, Bowen Ramesh, Tejas Hernandez, Oscar Zhou, Keren |
| author_facet | Cui, Bowen Ramesh, Tejas Hernandez, Oscar Zhou, Keren |
| contents | Large Language Models (LLMs) have emerged as powerful tools for software development tasks such as code completion, translation, and optimization. However, their ability to generate efficient and correct code, particularly in complex High-Performance Computing (HPC) contexts, has remained underexplored. To address this gap, this paper presents a comprehensive benchmark suite encompassing multiple critical HPC computational motifs to evaluate the performance of code optimized by state-of-the-art LLMs, including OpenAI o1, Claude-3.5, and Llama-3.2. In addition to analyzing basic computational kernels, we developed an agent system that integrates LLMs to assess their effectiveness in real HPC applications. Our evaluation focused on key criteria such as execution time, correctness, and understanding of HPC-specific concepts. We also compared the results with those achieved using traditional HPC optimization tools. Based on the findings, we recognized the strengths of LLMs in understanding human instructions and performing automated code transformations. However, we also identified significant limitations, including their tendency to generate incorrect code and their challenges in comprehending complex control and data flows in sophisticated HPC code. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_13772 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Do Large Language Models Understand Performance Optimization? Cui, Bowen Ramesh, Tejas Hernandez, Oscar Zhou, Keren Distributed, Parallel, and Cluster Computing Software Engineering Large Language Models (LLMs) have emerged as powerful tools for software development tasks such as code completion, translation, and optimization. However, their ability to generate efficient and correct code, particularly in complex High-Performance Computing (HPC) contexts, has remained underexplored. To address this gap, this paper presents a comprehensive benchmark suite encompassing multiple critical HPC computational motifs to evaluate the performance of code optimized by state-of-the-art LLMs, including OpenAI o1, Claude-3.5, and Llama-3.2. In addition to analyzing basic computational kernels, we developed an agent system that integrates LLMs to assess their effectiveness in real HPC applications. Our evaluation focused on key criteria such as execution time, correctness, and understanding of HPC-specific concepts. We also compared the results with those achieved using traditional HPC optimization tools. Based on the findings, we recognized the strengths of LLMs in understanding human instructions and performing automated code transformations. However, we also identified significant limitations, including their tendency to generate incorrect code and their challenges in comprehending complex control and data flows in sophisticated HPC code. |
| title | Do Large Language Models Understand Performance Optimization? |
| topic | Distributed, Parallel, and Cluster Computing Software Engineering |
| url | https://arxiv.org/abs/2503.13772 |