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Artur Skowroński
The State of the Green IT
At the beginning of 2024
Do you remember GDPR
2
What are we talking about when we talk about Green Technology?
Carbon Footprint
Taking into account all possible sources of
greenhouse gas emissions (e.g. methane, carbon
dioxide, nitrous oxide, etc.) and converting these
different gases to a single unit of measurement so
that they can be added up.
LCA Approach (Life Cycle Assessment)
A standard measure that is often used for this
purpose is carbon dioxide equivalents (CO2eq).
They are calculated by comparing the global
warming potential (GWP) of different gases.
greenhouse gases to that for carbon dioxide (CO2)
LCA Approach (Life Cycle Assessment)
LCA Approach (Life Cycle Assessment)
1 CH4 25 CO2eq
GWP
All right, but which emissions should we really count?
"Transferring" environmental impacts
AKA creative accounting
Greenhouse Gas Protocol
Greenhouse Gas Protocol
The GHG Protocol is a set of standards and
guidelines for measuring, managing and reporting
greenhouse gas emissions by companies and
organisations. It was developed by World Resources
Institute (WRI) and World Business Council for
Sustainable Development (WBCSD).
Scope 1 - Direct emissions
These are greenhouse gas
emissions that come directly from
sources owned or controlled by the
company.
Examples include exhaust fumes
emitted from the chimneys of a
factory it owns or the burning of
fuel by company cars.
Scope 2 - Indirect Emissions
These include emissions from the
production of electricity, heat or
steam that the company buys and
consumes.
Although these emissions do not
come directly from the company's
operations, they are related to the
company, as this energy is needed
for its operations.
Scope 3 - Other indirect emissions
This is the broadest category and
includes all other greenhouse gas
emissions that result from a
company's operations but come
from sources the company does
not own or control.
This could be, for example, the
production of materials that the
company buys from suppliers, or
how customers use the products
the company sells. It is also, for
example, aircraft emissions.
How does Software
fi
t into this?
Carbon Footprint
GHG Scope 2 3
Private Cloud Energia Hardware
Public Cloud - Energia + Hardware
Hybrid Cloud Energia + Hardware Energia + Hardware
Front End
Energia + Hardware
Użytkownika
Green Software Foundation
Green Software Foundation
Green Software Foundation
Software Carbon Intensity speci
fi
cation
The Software Carbon Index Speci
fi
cation (SCI) is a methodology
developed by Green Software Foundation's Standards Working
Group to assess software applications for sustainability and
encourage action to eliminate emissions.
Software Carbon Intensity speci
fi
cation
While the GHG protocol calculates
total emissions, SCI is about
calculating the emission factor.
SCI is more like measuring fuel
consumption per mile, while the
GHG protocol is more like the total
carbon footprint of a car
manufacturer.
Software Carbon Intensity speci
fi
cation
Rather than dividing software
carbon emissions into Scope 1-3, it
divides them into operational
emissions (carbon emissions from
running the software) and
embedded emissions (carbon
emissions from the physical
resources required to run the
software).
It is therefore based on intensity
rather than total quantity.
https://w3c.github.io/sustyweb/
https://w3c.github.io/sustyweb/
Cloud Computing
Carbon Neutral in 2007
To offset emissions that could not
be reduced immediately, Google
invested in external projects that
absorb CO2 or reduce emissions
elsewhere.
These projects included
fi
nancing
wind farms, solar installations, the
purchase of carbon credits and
forestry initiatives that bind CO2
through tree photosynthesis.
100% renewable energy sources in 2017
Zero Emission in 24-hour operation
This does not mean that every
operation of the company was
powered directly by renewable
sources, but the company
purchased enough renewable
energy (or renewable energy
credits) to offset its total energy
consumption.
100% renewable energy sources in 2017
Carbon Free in 2030
Zero Emissions 1h
Not only would the energy used by
Google come from 100% renewable
sources, but all of the company's
processes, products and
operations would produce no
carbon dioxide or other
greenhouse gas emissions.
Programming languages
Energy (J) = Power (W) x Time(s)
Execution Time != Energy Ef
fi
ciency
Skills of the engineering team
JIT Compilers Lifecycle of machines
Energy for debugging
AI
433,196 kWh i 24.69 ton CO2eq
40 single-family houses / per year
5.5 average cars / per year
Model
Choosing ef
fi
cient architectures for
machine learning models while
developing ML quality, such as
sparse models versus dense
models, can improve performance
by around 5-10 times.
Machine
Using processors optimised for ML
training, such as TPUs or new GPUs
(e.g. V100 or A100), compared to
general purpose processors, can
improve perform
Mechanisation
Cloud computing instead of local
computing improves the energy
ef
fi
ciency of data centres, reducing
energy costs by a factor of 1.4-2.
Map
Cloud computing allows ML
practitioners to select the location
with the cleanest energy, further
reducing the overall carbon
footprint by factors of 5-105.
Thoughts with which I would like to leave you
Climate change is a real problem
“Premature optimization is the root of all evil." Sir Tony Hoare
“Premature optimization is the root of all evil." Sir Tony Hoare
Performance
Czas Developerów jest więcej wart niż maszyny
Developers time more valuable than servers time?
Performance
Czas Developerów jest więcej wart niż maszyny
In practice, what we can do - write ef
fi
cient applications
Performance
Czas Developerów jest więcej wart niż maszyny
Jevons paradox
Czas Developerów jest więcej wart niż maszyny
Materials used in the presentation
• Energy Ef
fi
ciency across Programming Languages
• Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model
• Google: Operating on 24/7 Carbon-Free Energy by 2030
• The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink
• 2023 State of Green Software
• GHG Protocol
• Web Sustainability Guidelines (WSG) 1.0
• Software Carbon Intensity (SCI) Speci
fi
cation Project
• The real climate and transformative impact of ICT: A critique of estimates, trends, and
regulations
@ArturSkowronski
Thank You!

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The State of the Green IT at the beginning of 2024

  • 1. Artur Skowroński The State of the Green IT At the beginning of 2024
  • 2.
  • 3.
  • 4.
  • 5.
  • 7.
  • 8.
  • 9.
  • 10. What are we talking about when we talk about Green Technology?
  • 12. Taking into account all possible sources of greenhouse gas emissions (e.g. methane, carbon dioxide, nitrous oxide, etc.) and converting these different gases to a single unit of measurement so that they can be added up. LCA Approach (Life Cycle Assessment)
  • 13. A standard measure that is often used for this purpose is carbon dioxide equivalents (CO2eq). They are calculated by comparing the global warming potential (GWP) of different gases. greenhouse gases to that for carbon dioxide (CO2) LCA Approach (Life Cycle Assessment)
  • 14. LCA Approach (Life Cycle Assessment) 1 CH4 25 CO2eq GWP
  • 15. All right, but which emissions should we really count?
  • 17.
  • 19. Greenhouse Gas Protocol The GHG Protocol is a set of standards and guidelines for measuring, managing and reporting greenhouse gas emissions by companies and organisations. It was developed by World Resources Institute (WRI) and World Business Council for Sustainable Development (WBCSD).
  • 20. Scope 1 - Direct emissions These are greenhouse gas emissions that come directly from sources owned or controlled by the company. Examples include exhaust fumes emitted from the chimneys of a factory it owns or the burning of fuel by company cars.
  • 21. Scope 2 - Indirect Emissions These include emissions from the production of electricity, heat or steam that the company buys and consumes. Although these emissions do not come directly from the company's operations, they are related to the company, as this energy is needed for its operations.
  • 22. Scope 3 - Other indirect emissions This is the broadest category and includes all other greenhouse gas emissions that result from a company's operations but come from sources the company does not own or control. This could be, for example, the production of materials that the company buys from suppliers, or how customers use the products the company sells. It is also, for example, aircraft emissions.
  • 24. Carbon Footprint GHG Scope 2 3 Private Cloud Energia Hardware Public Cloud - Energia + Hardware Hybrid Cloud Energia + Hardware Energia + Hardware Front End Energia + Hardware Użytkownika
  • 28. Software Carbon Intensity speci fi cation The Software Carbon Index Speci fi cation (SCI) is a methodology developed by Green Software Foundation's Standards Working Group to assess software applications for sustainability and encourage action to eliminate emissions.
  • 29. Software Carbon Intensity speci fi cation While the GHG protocol calculates total emissions, SCI is about calculating the emission factor. SCI is more like measuring fuel consumption per mile, while the GHG protocol is more like the total carbon footprint of a car manufacturer.
  • 30. Software Carbon Intensity speci fi cation Rather than dividing software carbon emissions into Scope 1-3, it divides them into operational emissions (carbon emissions from running the software) and embedded emissions (carbon emissions from the physical resources required to run the software). It is therefore based on intensity rather than total quantity.
  • 31.
  • 34.
  • 36.
  • 37.
  • 38.
  • 39.
  • 40. Carbon Neutral in 2007 To offset emissions that could not be reduced immediately, Google invested in external projects that absorb CO2 or reduce emissions elsewhere. These projects included fi nancing wind farms, solar installations, the purchase of carbon credits and forestry initiatives that bind CO2 through tree photosynthesis.
  • 41. 100% renewable energy sources in 2017 Zero Emission in 24-hour operation This does not mean that every operation of the company was powered directly by renewable sources, but the company purchased enough renewable energy (or renewable energy credits) to offset its total energy consumption.
  • 42. 100% renewable energy sources in 2017
  • 43. Carbon Free in 2030 Zero Emissions 1h Not only would the energy used by Google come from 100% renewable sources, but all of the company's processes, products and operations would produce no carbon dioxide or other greenhouse gas emissions.
  • 45.
  • 46.
  • 47. Energy (J) = Power (W) x Time(s) Execution Time != Energy Ef fi ciency
  • 48.
  • 49. Skills of the engineering team JIT Compilers Lifecycle of machines Energy for debugging
  • 50. AI
  • 51.
  • 52. 433,196 kWh i 24.69 ton CO2eq 40 single-family houses / per year 5.5 average cars / per year
  • 53.
  • 54. Model Choosing ef fi cient architectures for machine learning models while developing ML quality, such as sparse models versus dense models, can improve performance by around 5-10 times.
  • 55. Machine Using processors optimised for ML training, such as TPUs or new GPUs (e.g. V100 or A100), compared to general purpose processors, can improve perform
  • 56. Mechanisation Cloud computing instead of local computing improves the energy ef fi ciency of data centres, reducing energy costs by a factor of 1.4-2.
  • 57. Map Cloud computing allows ML practitioners to select the location with the cleanest energy, further reducing the overall carbon footprint by factors of 5-105.
  • 58.
  • 59.
  • 60. Thoughts with which I would like to leave you
  • 61. Climate change is a real problem
  • 62. “Premature optimization is the root of all evil." Sir Tony Hoare
  • 63. “Premature optimization is the root of all evil." Sir Tony Hoare
  • 64. Performance Czas Developerów jest więcej wart niż maszyny Developers time more valuable than servers time?
  • 65. Performance Czas Developerów jest więcej wart niż maszyny In practice, what we can do - write ef fi cient applications
  • 66. Performance Czas Developerów jest więcej wart niż maszyny Jevons paradox
  • 67. Czas Developerów jest więcej wart niż maszyny
  • 68. Materials used in the presentation • Energy Ef fi ciency across Programming Languages • Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model • Google: Operating on 24/7 Carbon-Free Energy by 2030 • The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink • 2023 State of Green Software • GHG Protocol • Web Sustainability Guidelines (WSG) 1.0 • Software Carbon Intensity (SCI) Speci fi cation Project • The real climate and transformative impact of ICT: A critique of estimates, trends, and regulations