Title: ECO: Enhanced Code Optimization via Performance-Aware Prompting for Code-LLMs
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Code optimization, performance-aware, code-llm

Abstract: Code runtime optimization$\textemdash$the task of rewriting a given code to a faster one$\textemdash$remains challenging,
as it requires reasoning about performance trade-offs involving algorithmic and structural choices.
Recent approaches employ code-LLMs with slow-fast code pairs provided as optimization guidance, but such pair-based methods obscure the causal factors of performance gains and often lead to superficial pattern imitation rather than genuine performance reasoning.
We introduce ECO, a performance-aware prompting framework for code optimization.
ECO first distills runtime optimization instructions (ROIs) from reference slow-fast code pairs; 
Each ROI describes root causes of inefficiency and the rationales that drive performance improvements.
For a given input code, ECO in parallel employs (i) a symbolic advisor to produce a bottleneck diagnosis tailored to the code, and (ii) an ROI retriever to return related ROIs.
These two outputs are then composed into a performance-aware prompt, providing actionable guidance for code-LLMs.
ECO's prompts are model-agnostic, require no fine-tuning, and can be easily prepended to any code-LLM prompt.
Our empirical studies highlight that ECO prompting significantly improves code-LLMs' ability to generate efficient code, achieving speedups of up to 7.81$\times$ while minimizing correctness loss.