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[2406.11935] A Problem-Oriented Perspective and Anchor Verification for Code Optimization
Summary
This paper explores the use of Large Language Models (LLMs) for code optimization, proposing a problem-oriented approach and an anchor verification framework to enhance performance and correctness.
Why It Matters
As LLMs become increasingly integrated into programming tasks, understanding their potential for code optimization is crucial. This research addresses a significant gap by proposing innovative methods that could lead to more efficient programming practices and better software performance.
Key Takeaways
- LLMs have untapped potential for optimizing code beyond mere generation.
- A problem-oriented approach can integrate diverse solutions from multiple programmers.
- The proposed anchor verification framework helps mitigate the 'optimization tax' in code performance.
- This research enhances both the correctness of optimizations and execution speed.
- Understanding trade-offs between correctness and efficiency is vital for effective code optimization.
Computer Science > Programming Languages arXiv:2406.11935 (cs) [Submitted on 17 Jun 2024 (v1), last revised 24 Feb 2026 (this version, v3)] Title:A Problem-Oriented Perspective and Anchor Verification for Code Optimization Authors:Tong Ye, Tengfei Ma, Xuhong Zhang, Hang Yu, Jianwei Yin, Wenhai Wang View a PDF of the paper titled A Problem-Oriented Perspective and Anchor Verification for Code Optimization, by Tong Ye and 5 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs) have shown remarkable capabilities in solving various programming tasks, such as code generation. However, their potential for code optimization, particularly in performance enhancement, remains largely unexplored. This paper investigates the capabilities of LLMs in optimizing code for minimal execution time, addressing a critical gap in current research. The recently proposed code optimization methods construct program optimization pairs based on iterative submissions from the same programmer for the same problem. However, this approach confines LLMs to local performance improvements, neglecting global algorithmic innovation. To overcome this limitation, we adopt a completely different perspective by reconstructing the optimization pairs into a problem-oriented approach. This allows for the integration of various ideas from multiple programmers tackling the same problem. Furthermore, we observe that code optimization presents greater challenges compared to code generation, o...