Unveiling the Energy Vampires: A Methodology for Debugging Software Energy Consumption
- URL: http://arxiv.org/abs/2412.10063v1
- Date: Fri, 13 Dec 2024 11:49:19 GMT
- Title: Unveiling the Energy Vampires: A Methodology for Debugging Software Energy Consumption
- Authors: Enrique Barba Roque, Luis Cruz, Thomas Durieux,
- Abstract summary: This paper presents an energy debug methodology for identifying and isolating energy consumption hotspots in software systems.
Our analysis reveals significant energy consumption differences between Alpine and Ubuntu distributions.
By isolating and benchmarking memcpy, we confirm it as the primary cause of the energy discrepancy.
- Score: 5.602876058122268
- License:
- Abstract: Energy consumption in software systems is becoming increasingly important, especially in large-scale deployments. However, debugging energy-related issues remains challenging due to the lack of specialized tools. This paper presents an energy debugging methodology for identifying and isolating energy consumption hotspots in software systems. We demonstrate the methodology's effectiveness through a case study of Redis, a popular in-memory database. Our analysis reveals significant energy consumption differences between Alpine and Ubuntu distributions, with Alpine consuming up to 20.2% more power in certain operations. We trace this difference to the implementation of the memcpy function in different C standard libraries (musl vs. glibc). By isolating and benchmarking memcpy, we confirm it as the primary cause of the energy discrepancy. Our findings highlight the importance of considering energy efficiency in software dependencies and demonstrate the capability to assist developers in identifying and addressing energy-related issues. This work contributes to the growing field of sustainable software engineering by providing a systematic approach to energy debugging and using it to unveil unexpected energy behaviors in Alpine.
Related papers
- 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) - How do Practitioners Perceive Energy Consumption on Stack Overflow? [3.000496428347787]
We conduct an empirical analysis of Stack Overflow (SO) questions concerning energy consumption.
These questions reflect real-world energy-related predicaments faced by practitioners in their daily development activities.
Our observations raise awareness among practitioners about the impact of energy consumption on developing software systems.
arXiv Detail & Related papers (2024-09-28T03:28:52Z) - Learning Iterative Reasoning through Energy Diffusion [90.24765095498392]
We introduce iterative reasoning through energy diffusion (IRED), a novel framework for learning to reason for a variety of tasks.
IRED learns energy functions to represent the constraints between input conditions and desired outputs.
We show IRED outperforms existing methods in continuous-space reasoning, discrete-space reasoning, and planning tasks.
arXiv Detail & Related papers (2024-06-17T03:36:47Z) - Enhancing Energy-Awareness in Deep Learning through Fine-Grained Energy
Measurement [11.37120215795946]
This paper introduces FECoM (Fine-grained Energy Consumption Meter), a framework for fine-grained Deep Learning energy consumption measurement.
FECoM addresses the challenges of measuring energy consumption at fine-grained level by using static instrumentation and considering various factors, including computational load stability and temperature.
arXiv Detail & Related papers (2023-08-23T17:32:06Z) - Energy Transformer [64.22957136952725]
Our work combines aspects of three promising paradigms in machine learning, namely, attention mechanism, energy-based models, and associative memory.
We propose a novel architecture, called the Energy Transformer (or ET for short), that uses a sequence of attention layers that are purposely designed to minimize a specifically engineered energy function.
arXiv Detail & Related papers (2023-02-14T18:51:22Z) - Compute and Energy Consumption Trends in Deep Learning Inference [67.32875669386488]
We study relevant models in the areas of computer vision and natural language processing.
For a sustained increase in performance we see a much softer growth in energy consumption than previously anticipated.
arXiv Detail & Related papers (2021-09-12T09:40:18Z) - Memory-Aware Partitioning of Machine Learning Applications for Optimal
Energy Use in Batteryless Systems [17.072240411944914]
We present Julienning: an automated method for optimizing the total energy cost of batteryless applications.
Our method can reduce the required energy storage by over 94% while only incurring a 0.12% energy overhead.
arXiv Detail & Related papers (2021-08-05T09:49:42Z) - Design and Comparison of Reward Functions in Reinforcement Learning for
Energy Management of Sensor Nodes [0.0]
Interest in remote monitoring has grown thanks to recent advancements in Internet-of-Things (IoT) paradigms.
New applications have emerged, using small devices called sensor nodes capable of collecting data from the environment and processing it.
Battery technologies have not improved fast enough to cope with these increasing needs.
Miniature energy harvesting devices have emerged to complement traditional energy sources.
arXiv Detail & Related papers (2021-06-02T12:23:47Z) - Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A
Multi-Agent Deep Reinforcement Learning Approach [82.6692222294594]
We study a risk-aware energy scheduling problem for a microgrid-powered MEC network.
We derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based advantage actor-critic (A3C) algorithm with shared neural networks.
arXiv Detail & Related papers (2020-02-21T02:14:38Z) - Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable
Edge Computing Systems [87.4519172058185]
An effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied.
A novel multi-agent meta-reinforcement learning (MAMRL) framework is proposed to solve the formulated problem.
Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost.
arXiv Detail & Related papers (2020-02-20T04:58:07Z) - Towards the Systematic Reporting of the Energy and Carbon Footprints of
Machine Learning [68.37641996188133]
We introduce a framework for tracking realtime energy consumption and carbon emissions.
We create a leaderboard for energy efficient reinforcement learning algorithms.
We propose strategies for mitigation of carbon emissions and reduction of energy consumption.
arXiv Detail & Related papers (2020-01-31T05:12:59Z)
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.