Chasing Carbon: The Elusive Environmental Footprint of Computing
- URL: http://arxiv.org/abs/2011.02839v1
- Date: Wed, 28 Oct 2020 18:15:22 GMT
- Title: Chasing Carbon: The Elusive Environmental Footprint of Computing
- Authors: Udit Gupta, Young Geun Kim, Sylvia Lee, Jordan Tse, Hsien-Hsin S. Lee,
Gu-Yeon Wei, David Brooks, Carole-Jean Wu
- Abstract summary: We analyze the environmental effects of computing in terms of carbon emissions.
Most emissions related to modern mobile and data-center equipment come from hardware manufacturing and infrastructure.
- Score: 11.992632765006087
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Given recent algorithm, software, and hardware innovation, computing has
enabled a plethora of new applications. As computing becomes increasingly
ubiquitous, however, so does its environmental impact. This paper brings the
issue to the attention of computer-systems researchers. Our analysis, built on
industry-reported characterization, quantifies the environmental effects of
computing in terms of carbon emissions. Broadly, carbon emissions have two
sources: operational energy consumption, and hardware manufacturing and
infrastructure. Although carbon emissions from the former are decreasing thanks
to algorithmic, software, and hardware innovations that boost performance and
power efficiency, the overall carbon footprint of computer systems continues to
grow. This work quantifies the carbon output of computer systems to show that
most emissions related to modern mobile and data-center equipment come from
hardware manufacturing and infrastructure. We therefore outline future
directions for minimizing the environmental impact of computing systems.
Related papers
- The Sunk Carbon Fallacy: Rethinking Carbon Footprint Metrics for Effective Carbon-Aware Scheduling [2.562727244613512]
We evaluate carbon-aware job scheduling and placement on a given set of servers for a number of carbon accounting metrics.
We study the factors that affect the added carbon cost of such suboptimal decision-making.
arXiv Detail & Related papers (2024-10-19T12:23:59Z) - Carbon Connect: An Ecosystem for Sustainable Computing [22.998262140061733]
Carbon Connect envisions coordinated research thrusts that produce design and management strategies for sustainable computer systems.
We will require accurate models for carbon accounting in computing technology.
New hardware design and management strategies must be cognizant of economic policy and regulatory landscape.
arXiv Detail & Related papers (2024-05-22T17:33:51Z) - Generative AI for Low-Carbon Artificial Intelligence of Things with Large Language Models [67.0243099823109]
Generative AI (GAI) holds immense potential to reduce carbon emissions of Artificial Intelligence of Things (AIoT)
In this article, we explore the potential of GAI for carbon emissions reduction and propose a novel GAI-enabled solution for low-carbon AIoT.
We propose a Large Language Model (LLM)-enabled carbon emission optimization framework, in which we design pluggable LLM and Retrieval Augmented Generation (RAG) modules.
arXiv Detail & Related papers (2024-04-28T05:46:28Z) - Profiling the carbon footprint of performance bugs [2.7282382992043885]
Green information and communication technology is a paradigm creating a sustainable and environmentally friendly computing field.
In this paper, we undertake the problem of performance bugs that, until recently, have never been studied so profoundly.
arXiv Detail & Related papers (2024-01-03T15:15:00Z) - On the Opportunities of Green Computing: A Survey [80.21955522431168]
Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades.
The needs for high computing power brings higher carbon emission and undermines research fairness.
To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
arXiv Detail & Related papers (2023-11-01T11:16:41Z) - Counting Carbon: A Survey of Factors Influencing the Emissions of
Machine Learning [77.62876532784759]
Machine learning (ML) requires using energy to carry out computations during the model training process.
The generation of this energy comes with an environmental cost in terms of greenhouse gas emissions, depending on quantity used and the energy source.
We present a survey of the carbon emissions of 95 ML models across time and different tasks in natural language processing and computer vision.
arXiv Detail & Related papers (2023-02-16T18:35:00Z) - Measuring the Carbon Intensity of AI in Cloud Instances [91.28501520271972]
We provide a framework for measuring software carbon intensity, and propose to measure operational carbon emissions.
We evaluate a suite of approaches for reducing emissions on the Microsoft Azure cloud compute platform.
arXiv Detail & Related papers (2022-06-10T17:04:04Z) - Future Computer Systems and Networking Research in the Netherlands: A
Manifesto [137.47124933818066]
We draw attention to CompSys as a vital part of ICT.
Each of the Top Sectors of the Dutch Economy, each route in the National Research Agenda, and each of the UN Sustainable Development Goals pose challenges that cannot be addressed without CompSys advances.
arXiv Detail & Related papers (2022-05-26T11:02:29Z) - Green Algorithms: Quantifying the carbon footprint of computation [0.0]
We present a framework to estimate the carbon footprint of any computational task in a standardised and reliable way.
Metrics to interpret and contextualise greenhouse gas emissions are defined, including the equivalent distance travelled by car or plane.
We develop a freely available online tool, Green Algorithms, which enables a user to estimate and report the carbon footprint of their computation.
arXiv Detail & Related papers (2020-07-15T11:05:33Z) - 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.