In supercomputing, efficient and optimized code generation is essential to leverage high-performance systems effectively. We propose Agentic Retrieval-Augmented Code Synthesis (ARCS), an advanced framework for accurate, robust, and efficient code generation, completion, and translation. ARCS integrates Retrieval-Augmented Generation (RAG) with Chain-of-Thought (CoT) reasoning to systematically break down and iteratively refine complex programming tasks. An agent-based RAG mechanism retrieves relevant code snippets, while real-time execution feedback drives the synthesis of candidate solutions. This process is formalized as a state-action search tree optimization, balancing code correctness with editing efficiency. Evaluations on the Geeks4Geeks and HumanEval benchmarks demonstrate that ARCS significantly outperforms traditional prompting methods in translation and generation quality. By enabling scalable and precise code synthesis, ARCS offers transformative potential for automating and optimizing code development in supercomputing applications, enhancing computational resource utilization.

本研究针对超级计算中高效代码生成的难题，提出一种名为ARCS的先进框架，旨在准确、稳健且高效地进行代码生成、补全和翻译。通过结合检索增强生成与推理链的方法，ARCS系统性地拆解复杂编程任务，实现了显著优于传统方法的代码翻译和生成质量，展示了在超级计算应用中自动化和优化代码开发的巨大潜力。

ARCS：带有迭代优化的代理检索增强代码合成