ECO: Enhanced Code Optimization via Performance-Aware Prompting for Code-LLMs

• URL: http://arxiv.org/abs/2510.10517v1
• Date: Sun, 12 Oct 2025 09:29:24 GMT
• Title: ECO: Enhanced Code Optimization via Performance-Aware Prompting for Code-LLMs
• Authors: Su-Hyeon Kim, Joonghyuk Hahn, Sooyoung Cha, Yo-Sub Han,
• Abstract summary: ECO is a performance-aware prompting framework for code optimization.<n>Our empirical studies highlight that ECO prompting significantly improves code-LLMs' ability to generate efficient code.
• Score: 10.020128936428078
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Code runtime optimization-the task of rewriting a given code to a faster one-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 7.81x while minimizing correctness loss.

Related papers

• MaxCode: A Max-Reward Reinforcement Learning Framework for Automated Code Optimization [44.27213441671799]
  Large Language Models (LLMs) demonstrate strong capabilities in general coding tasks but encounter two key challenges when optimizing code.<n>In this work, we explore inference-time search algorithms that guide the LLM to discover better solutions.<n>Our approach, called MaxCode unifies existing search methods under a max-reward reinforcement learning framework.
  arXiv  Detail & Related papers  (2026-01-09T02:21:28Z)
• FasterPy: An LLM-based Code Execution Efficiency Optimization Framework [11.766544835516974]
  Code often suffers from performance bugs.<n>Traditional rule-based methods rely on manually designing and maintaining rules for specific performance bugs.<n>We propose FasterPy, a framework that adapts Large Language Models to optimize the execution efficiency of Python code.
  arXiv  Detail & Related papers  (2025-12-28T07:43:08Z)
• PerfCoder: Large Language Models for Interpretable Code Performance Optimization [15.79612555952707]
  PerfCoder is a family of large language models (LLMs) designed to generate performance-enhanced code from source code.<n>PerfCoder is fine-tuned on a curated collection of real-world optimization trajectories with human-readable annotations.<n>PerfCoder surpasses all existing models in both runtime speedup and effective optimization rate.
  arXiv  Detail & Related papers  (2025-12-16T02:30:04Z)
• LLM4EFFI: Leveraging Large Language Models to Enhance Code Efficiency and Correctness [38.399282089600284]
  Large Language Models (LLMs) have demonstrated impressive performance in code generation.<n>tool: ulineLarge ulineLanguage ulineModel for Code ulineEfficiency is a novel framework that enables LLMs to generate code that balances both efficiency and correctness.
  arXiv  Detail & Related papers  (2025-02-17T07:01:18Z)
• PerfCodeGen: Improving Performance of LLM Generated Code with Execution Feedback [78.89596149768458]
  Large Language Models (LLMs) are widely adopted for assisting in software development tasks.<n>We propose PerfCodeGen, a training-free framework that enhances the performance of LLM-generated code.
  arXiv  Detail & Related papers  (2024-11-18T06:22:38Z)
• CodeDPO: Aligning Code Models with Self Generated and Verified Source Code [52.70310361822519]
  We propose CodeDPO, a framework that integrates preference learning into code generation to improve two key code preference factors: code correctness and efficiency.<n>CodeDPO employs a novel dataset construction method, utilizing a self-generation-and-validation mechanism that simultaneously generates and evaluates code and test cases.
  arXiv  Detail & Related papers  (2024-10-08T01:36:15Z)
• A Reinforcement Learning Environment for Automatic Code Optimization in the MLIR Compiler [0.10923877073891444]
  We introduce the first RL environment for the MLIR compiler, dedicated to facilitating MLIR compiler research. We also propose a novel formulation of the action space as a product of simpler action subspaces, enabling more efficient and effective optimizations.
  arXiv  Detail & Related papers  (2024-09-17T10:49:45Z)
• RTLRewriter: Methodologies for Large Models aided RTL Code Optimization [21.61206887869307]
  This paper introduces RTLRewriter, an innovative framework that leverages large models to optimize RTL code. A circuit partition pipeline is utilized for fast synthesis and efficient rewriting. A specialized search engine is designed to identify useful optimization guides, algorithms, and code snippets.
  arXiv  Detail & Related papers  (2024-09-04T09:59:37Z)
• Should AI Optimize Your Code? A Comparative Study of Classical Optimizing Compilers Versus Current Large Language Models [0.0]
  Large Language Models (LLMs) raise intriguing questions about the potential of these AI approaches to revolutionize code optimization.<n>This work aims to answer an essential question for the compiler community: "Can AI-driven models revolutionize the way we approach code optimization?"<n>We present a comparative analysis between three classical optimizing compilers and two recent large language models.
  arXiv  Detail & Related papers  (2024-06-17T23:26:41Z)
• A Problem-Oriented Perspective and Anchor Verification for Code Optimization [43.28045750932116]
  Large language models (LLMs) have shown remarkable capabilities in solving various programming tasks.<n>This paper investigates the capabilities of LLMs in optimizing code for minimal execution time.
  arXiv  Detail & Related papers  (2024-06-17T16:10:10Z)
• Factor Graph Optimization of Error-Correcting Codes for Belief Propagation Decoding [62.25533750469467]
  Low-Density Parity-Check (LDPC) codes possess several advantages over other families of codes. The proposed approach is shown to outperform the decoding performance of existing popular codes by orders of magnitude.
  arXiv  Detail & Related papers  (2024-06-09T12:08:56Z)
• StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback [58.20547418182074]
  We introduce StepCoder, a novel framework for code generation, consisting of two main components. CCCS addresses the exploration challenge by breaking the long sequences code generation task into a Curriculum of Code Completion Subtasks. FGO only optimize the model by masking the unexecuted code segments to provide Fine-Grained Optimization. Our method improves the ability to explore the output space and outperforms state-of-the-art approaches in corresponding benchmarks.
  arXiv  Detail & Related papers  (2024-02-02T13:14:31Z)
• Fast Chain-of-Thought: A Glance of Future from Parallel Decoding Leads to Answers Faster [61.83949316226113]
  FastCoT is a model-agnostic framework based on parallel decoding. We show that FastCoT saves inference time by nearly 20% with only a negligible performance drop compared to the regular approach.
  arXiv  Detail & Related papers  (2023-11-14T15:56:18Z)
• Practical Conformer: Optimizing size, speed and flops of Conformer for on-Device and cloud ASR [67.63332492134332]
  We design an optimized conformer that is small enough to meet on-device restrictions and has fast inference on TPUs. Our proposed encoder can double as a strong standalone encoder in on device, and as the first part of a high-performance ASR pipeline.
  arXiv  Detail & Related papers  (2023-03-31T23:30:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.