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.
View on arXiv@article{rahman2025_2505.03906,
title={ MARCO: A Multi-Agent System for Optimizing HPC Code Generation Using Large Language Models },
author={ Asif Rahman and Veljko Cvetkovic and Kathleen Reece and Aidan Walters and Yasir Hassan and Aneesh Tummeti and Bryan Torres and Denise Cooney and Margaret Ellis and Dimitrios S. Nikolopoulos },
journal={arXiv preprint arXiv:2505.03906},
year={ 2025 }
}