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| Hauptverfasser: | , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2505.03906 |
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| _version_ | 1866915358032527360 |
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| author | Rahman, Asif Cvetkovic, Veljko Reece, Kathleen Walters, Aidan Hassan, Yasir Tummeti, Aneesh Torres, Bryan Cooney, Denise Ellis, Margaret Nikolopoulos, Dimitrios S. |
| author_facet | Rahman, Asif Cvetkovic, Veljko Reece, Kathleen Walters, Aidan Hassan, Yasir Tummeti, Aneesh Torres, Bryan Cooney, Denise Ellis, Margaret Nikolopoulos, Dimitrios S. |
| contents | Large language models (LLMs) have transformed software development through code generation capabilities, yet their effectiveness for high-performance computing (HPC) remains limited. HPC code requires specialized optimizations for parallelism, memory efficiency, and architecture-specific considerations that general-purpose LLMs often overlook. We present MARCO (Multi-Agent Reactive Code Optimizer), a novel framework that enhances LLM-generated code for HPC through a specialized multi-agent architecture. MARCO employs separate agents for code generation and performance evaluation, connected by a feedback loop that progressively refines optimizations. A key innovation is MARCO's web-search component that retrieves real-time optimization techniques from recent conference proceedings and research publications, bridging the knowledge gap in pre-trained LLMs. Our extensive evaluation on the LeetCode 75 problem set demonstrates that MARCO achieves a 14.6\% average runtime reduction compared to Claude 3.5 Sonnet alone, while the integration of the web-search component yields a 30.9\% performance improvement over the base MARCO system. These results highlight the potential of multi-agent systems to address the specialized requirements of high-performance code generation, offering a cost-effective alternative to domain-specific model fine-tuning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_03906 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MARCO: Multi-Agent Code Optimization with Real-Time Knowledge Integration for High-Performance Computing Rahman, Asif Cvetkovic, Veljko Reece, Kathleen Walters, Aidan Hassan, Yasir Tummeti, Aneesh Torres, Bryan Cooney, Denise Ellis, Margaret Nikolopoulos, Dimitrios S. Distributed, Parallel, and Cluster Computing Machine Learning Software Engineering Large language models (LLMs) have transformed software development through code generation capabilities, yet their effectiveness for high-performance computing (HPC) remains limited. HPC code requires specialized optimizations for parallelism, memory efficiency, and architecture-specific considerations that general-purpose LLMs often overlook. We present MARCO (Multi-Agent Reactive Code Optimizer), a novel framework that enhances LLM-generated code for HPC through a specialized multi-agent architecture. MARCO employs separate agents for code generation and performance evaluation, connected by a feedback loop that progressively refines optimizations. A key innovation is MARCO's web-search component that retrieves real-time optimization techniques from recent conference proceedings and research publications, bridging the knowledge gap in pre-trained LLMs. Our extensive evaluation on the LeetCode 75 problem set demonstrates that MARCO achieves a 14.6\% average runtime reduction compared to Claude 3.5 Sonnet alone, while the integration of the web-search component yields a 30.9\% performance improvement over the base MARCO system. These results highlight the potential of multi-agent systems to address the specialized requirements of high-performance code generation, offering a cost-effective alternative to domain-specific model fine-tuning. |
| title | MARCO: Multi-Agent Code Optimization with Real-Time Knowledge Integration for High-Performance Computing |
| topic | Distributed, Parallel, and Cluster Computing Machine Learning Software Engineering |
| url | https://arxiv.org/abs/2505.03906 |