Large Language Models for Energy-Efficient Code: Emerging Results and Future Directions

• URL: http://arxiv.org/abs/2410.09241v1
• Date: Fri, 11 Oct 2024 20:35:40 GMT
• Title: Large Language Models for Energy-Efficient Code: Emerging Results and Future Directions
• Authors: Huiyun Peng, Arjun Gupte, Nicholas John Eliopoulos, Chien Chou Ho, Rishi Mantri, Leo Deng, Wenxin Jiang, Yung-Hsiang Lu, Konstantin Läufer, George K. Thiruvathukal, James C. Davis,
• Abstract summary: We propose a novel application of large language models (LLMs) as codes for energy efficiency. We describe and evaluate a prototype, finding that over 6 small programs our system can improve energy efficiency in 3 of them, up to 2x better than compiler optimizations alone.
• Score: 2.848398051763324
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Energy-efficient software helps improve mobile device experiences and reduce the carbon footprint of data centers. However, energy goals are often de-prioritized in order to meet other requirements. We take inspiration from recent work exploring the use of large language models (LLMs) for different software engineering activities. We propose a novel application of LLMs: as code optimizers for energy efficiency. We describe and evaluate a prototype, finding that over 6 small programs our system can improve energy efficiency in 3 of them, up to 2x better than compiler optimizations alone. From our experience, we identify some of the challenges of energy-efficient LLM code optimization and propose a research agenda.

Related papers

• Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization [46.33639431414019]
  Large Language Models generate functionally correct solutions but often fall short in code efficiency.<n>We introduce a novel test-time iterative optimization framework to address this.
  arXiv  Detail & Related papers  (2025-05-29T12:14:29Z)
• Evaluating the Energy-Efficiency of the Code Generated by LLMs [2.1983110147455482]
  This paper investigates the energy efficiency of the code generated by 20 popular Large Language Models for 878 programming problems.<n>Among the studied LLMs, DeepSeek-v3 and GPT-4o generate the most energy-efficient code.<n>For specific algorithmic groups such as dynamic programming, backtracking, and bit manipulation, LLM-generated code can consume up to 450 times more energy than human-generated canonical solutions.
  arXiv  Detail & Related papers  (2025-05-23T18:13:27Z)
• Energy Considerations of Large Language Model Inference and Efficiency Optimizations [28.55549828393871]
  As large language models (LLMs) scale in size and adoption, their computational and environmental costs continue to rise. We systematically analyze the energy implications of common inference efficiency optimizations across diverse NLP and AI workloads. Our findings reveal that the proper application of relevant inference efficiency optimizations can reduce total energy use by up to 73% from unoptimized baselines.
  arXiv  Detail & Related papers  (2025-04-24T15:45:05Z)
• Can We Make Code Green? Understanding Trade-Offs in LLMs vs. Human Code Optimizations [45.243401722182554]
  Large language models (LLMs) claim to assist developers in optimizing code for performance and energy efficiency. This work focuses on software written in Matlab-widely used in both academia and industry for scientific and engineering applications. We analyze energy-focused optimization on 400 scripts across 100 top GitHub repositories.
  arXiv  Detail & Related papers  (2025-03-26T00:27:29Z)
• AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code [45.77395425799378]
  This paper presents the first study analyzing the energy efficiency and performance of LLM-generated code for three programming languages Python, Java, and C++. Our results show that the models are much more successful in generating Python and Java than C++ code.
  arXiv  Detail & Related papers  (2025-02-04T15:32:34Z)
• Advancing Generative Artificial Intelligence and Large Language Models for Demand Side Management with Internet of Electric Vehicles [52.43886862287498]
  This paper explores the integration of large language models (LLMs) into energy management. We propose an innovative solution that enhances LLMs with retrieval-augmented generation for automatic problem formulation, code generation, and customizing optimization. We present a case study to demonstrate the effectiveness of our proposed solution in charging scheduling and optimization for electric vehicles.
  arXiv  Detail & Related papers  (2025-01-26T14:31:03Z)
• Prompt engineering and its implications on the energy consumption of Large Language Models [4.791072577881446]
  Large language models (LLMs) in software engineering pose severe challenges regarding computational resources, data centers, and carbon emissions. In this paper, we investigate how prompt engineering techniques (PETs) can impact the carbon emission of the Llama 3 model for the code generation task.
  arXiv  Detail & Related papers  (2025-01-10T11:49:31Z)
• Can Large-Language Models Help us Better Understand and Teach the Development of Energy-Efficient Software? [2.8812501020074968]
  Energy-efficient software engineering techniques are often absent from undergraduate curricula. We propose to develop a learning module for energy-efficient software, suitable for incorporation into an undergraduate software engineering class.
  arXiv  Detail & Related papers  (2024-10-30T01:09:32Z)
• Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System [75.25394449773052]
  Large Language Model (LLM) based multi-agent systems (MAS) show remarkable potential in collaborative problem-solving. Yet they still face critical challenges: low communication efficiency, poor scalability, and a lack of effective parameter-updating optimization methods. We present Optima, a novel framework that addresses these issues by significantly enhancing both communication efficiency and task effectiveness.
  arXiv  Detail & Related papers  (2024-10-10T17:00:06Z)
• Measuring Code Efficiency Optimization Capabilities with ACEOB [7.4056083791645495]
  We conduct an in-depth analysis of "code patterns" in the model training dataset, meticulously exploring human-written code. We introduce the Automatic Code Efficiency Optimization Benchmark (ACEOB), which consists of 95,359 pairs of efficient-inefficient code. To our knowledge, ACEOB is the first dataset specifically targeting Python code efficiency optimization.
  arXiv  Detail & Related papers  (2024-08-23T10:10:37Z)
• Iterative or Innovative? A Problem-Oriented Perspective for Code Optimization [81.88668100203913]
  Large language models (LLMs) have demonstrated strong capabilities in solving a wide range of programming tasks. In this paper, we explore code optimization with a focus on performance enhancement, specifically aiming to optimize code for minimal execution time.
  arXiv  Detail & Related papers  (2024-06-17T16:10:10Z)
• A Controlled Experiment on the Energy Efficiency of the Source Code Generated by Code Llama [4.937787069991124]
  83% of software developers use Large Language Models (LLMs) to generate code. This paper assesses the energy efficiency of Code Llama with respect to human-written source code.
  arXiv  Detail & Related papers  (2024-05-06T16:32:29Z)
• Towards Coarse-to-Fine Evaluation of Inference Efficiency for Large Language Models [95.96734086126469]
  Large language models (LLMs) can serve as the assistant to help users accomplish their jobs, and also support the development of advanced applications. For the wide application of LLMs, the inference efficiency is an essential concern, which has been widely studied in existing work. We perform a detailed coarse-to-fine analysis of the inference performance of various code libraries.
  arXiv  Detail & Related papers  (2024-04-17T15:57:50Z)
• On Evaluating the Efficiency of Source Code Generated by LLMs [31.8121544062256]
  More efficient code can lead to higher performance and execution efficiency of programs and software completed by LLM-assisted programming. First, we evaluate the efficiency of the code generated by LLMs on two benchmarks, HumanEval and MBPP. Then, we choose a set of programming problems from the online judge platform LeetCode to conduct a more difficult evaluation.
  arXiv  Detail & Related papers  (2024-04-09T05:59:39Z)
• LLM-Assisted Code Cleaning For Training Accurate Code Generators [53.087019724256606]
  We investigate data quality for code and find that making the code more structured and readable leads to improved code generation performance of the system. We build a novel data-cleaning pipeline that uses these principles to transform existing programs. We evaluate our approach on two challenging algorithmic code generation benchmarks and find that fine-tuning CodeLLaMa-7B improves the performance by up to 30% compared to fine-tuning on the original dataset.
  arXiv  Detail & Related papers  (2023-11-25T02:45:50Z)
• A Metaheuristic-based Machine Learning Approach for Energy Prediction in Mobile App Development [1.933681537640272]
  This paper proposes a histogram-based gradient boosting classification machine (HGBC), boosted by a metaheuristic approach, for energy prediction in mobile App development. Our finding shows that a success-history-based parameter adaption for differential evolution with linear population size (L-SHADE) offers the best performance.
  arXiv  Detail & Related papers  (2023-06-16T16:01:50Z)
• Effective Pre-Training Objectives for Transformer-based Autoencoders [97.99741848756302]
  We study trade-offs between efficiency, cost and accuracy of Transformer encoders. We combine features of common objectives and create new effective pre-training approaches.
  arXiv  Detail & Related papers  (2022-10-24T18:39:44Z)
• Learning Implicit Priors for Motion Optimization [105.11889448885226]
  Energy-based Models (EBM) represent expressive probability density distributions. We present a set of required modeling and algorithmic choices to adapt EBMs into motion optimization.
  arXiv  Detail & Related papers  (2022-04-11T19:14:54Z)

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.