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Klassifizierung: Intern
Green IT - MMIGIT
Green IT as a driver for
future viability
The Earth, The Climate and Everything
Green Software Development Meetup
06.06.2024
Klassifizierung: Intern
metafinanz Contacts for GreenIT
Ghazal Aakel Eric Jochum
Expert GreenIT Expert GreenIT
+ 49 89 3605316444 + 49 89 3605316205
Ghazal.Aakel@metafinanz.de Eric.Jochum@metafinanz.de
Klassifizierung: Intern
Green IT —
New thinking
and acting
Green IT as a driver for future viability
Klassifizierung: Intern
• Digitalization refers to the process of converting
analogue information, data and processes into
digital formats.
• Digitalization has far-reaching effects on society and
the economy and is seen as a driver of innovation,
increased efficiency and growth.
• This is not possible without energy!
Green IT — New thinking and acting
Digitalization
Green IT is the key to
sustainable digitalization and
future viability!
Klassifizierung: Intern
Green IT — New thinking and acting
MMIGIT (Maturity Model Integrated Green IT) –
Solutions Landscape Green IT
Initial Optimizing
Managed
Defined
Repeatable
Paper
(not organized)
Technic
(defined & documented)
Blueprint
(standardized)
KPIs
(monitored)
Self optimizing
Dynamic Software Services and Resource Allocation
AI Energy Optimization
Green Data Management
Heuristics for Hyperscale Hardware Management
Domain-specific Hardware I Code I Pipeline
Green Coding
Green Architecture
Future Hardware Architecture
Strategies for Awareness Creation
Sustainable ICT Skills Training
Conscious Software Developer and Software Consumer
Design for Reuse
Green Energy Resources
Energy Aware Software Solutions
Distributed energy Landscapes
Strategic Geolocation of Digital Infrastructure
Cloudification
Flexible Distributed and Disaggregated Data Storage
Hardware Breakthroughs
Location Shifting
Measuring Energy Consumption of IT Applications
Time Shifting
Demand Shaping
Technical Solutions
Paradigm Shift
Environmental Solutions
Social Solutions
06.06.2024 Green IT: MMIGIT & ALGIT
Klassifizierung: Intern
Concrete
Approaches
Green IT as a driver for future viability
Klassifizierung: Intern
Klassifizierung: Intern
Green IT: MMIGIT & ALGIT
Green
Development
Guidelines
Green
Data
Mgmt
Green
Archit
ecture
Green
Codin
g
Development
Lifecycle
EC**
Data
Cloud - Production
ECM
App A
App B
App C…
Power
Consumption
In Production
CBDMS
Storage
/ Release
App X
Power Consumption Measurement
metafinanz GreenIT/RETIT
GreenIT KPI
PC/LoC
Source Code Scan
CAST Imaging
Quick Energy Consumption
Improvement (QECI)
Success
Stories
Go Live
ALGIT: The Assembly Line of Green IT
Source Code Scan
CAST Highlight
GreenIT
GitHub
Order the applications by
potential of improvement
06.06.2024
Klassifizierung: Intern
Klassifizierung: Intern
ALGIT: The Assembly Line of Green IT
Show Case Using Generative AI in Code Optimization
Green IT: MMIGIT & ALGIT
Model used: Generative Pre-trained Transformer
Setting the Context for the AI model to Optimize Code
• prompt the AI model to be familiar with the programming language and requiring its assistance.
• introduce the code to the AI model
• Instruct the model with Green Deficiencies rules (Guidelines) and best examples to improve its performance and accuracy.
The Goal is to use specialist LLMs which are aware of the context
06.06.2024
Klassifizierung: Intern
Klassifizierung: Intern
ALGIT: The Assembly Line of Green IT
Show Case Using Generative AI in Code Optimization
Green IT: MMIGIT & ALGIT
Findings Categories
Avoid instantiations inside loops
Avoid string concatenation in loops
Avoid nested loops
Prefer comparison-to-0 in loop conditions
Avoid calling a function in a condition loop
Avoid primitive type wrapper instantiation
Avoid Programs not using explicitly OPEN and CLOSE for files or streams
Scan Report & QECI
Program Size and CAST Highlight
Findings
Numbers
Program Size (#LoC Lines of Code) 115.214
Number of Findings by CAST
Highlight
233
Number of LoC / Finding 494 (every 494 LoC 1 Finding)
RESULTS Original QECI
API Call CPU [s] Energy [mWh] CPU [s] Energy [mWh] Savings [%] Absolute [mWh]
api_v1_private_catalog__id_ 3,34 61,23 2,71 49,68 18,86 11,55
api_v1_private_catalog 2,99 54,82 2,53 46,38 15,38 8,43
api_v1_category_product__ProductId_ 2,11 38,68 1,66 30,43 21,33 8,25
api_v1_category__id__manufacturer_ 2,09 38,32 1,68 30,80 19,62 7,52
api_v1_category__friendlyUrl_ 2,08 38,13 1,66 30,43 20,19 7,70
api_v1_category 1,39 25,48 1,17 21,45 15,83 4,03
06.06.2024
Klassifizierung: Intern
Klassifizierung: Intern
ALGIT – QECI- Example: Avoid Nested Loops
06.06.2024 Green IT: MMIGIT & ALGIT
• Mastertextformat bearbeiten
• Zweite Ebene
• Dritte Ebene
• Vierte Ebene
• Fünfte Ebene
https://github.com/marton-eifert/shopizer_showcase/compare/3.2.5-actual...3.2.5-actual-QECI-fix--ATTEMPT-1-only-avoid-nested-loops-1-of-6-a#diff-1e8015b8c0587993f78ad512df20fc9e4c33d1c860a4b73e46ae804f7f7ee28d
Klassifizierung: Intern
Klassifizierung: Intern
ALGIT – QECI- Example: Avoid Nested Loops
06.06.2024 Green IT: MMIGIT & ALGIT
• Mastertextformat bearbeiten
• Zweite Ebene
• Dritte Ebene
• Vierte Ebene
• Fünfte Ebene
https://github.com/marton-eifert/shopizer_showcase/compare/3.2.5-actual...3.2.5-actual-QECI-fix--ATTEMPT-1-only-avoid-nested-loops-1-of-6-a#diff-1e8015b8c0587993f78ad512df20fc9e4c33d1c860a4b73e46ae804f7f7ee28d
Klassifizierung: Intern
Klassifizierung: Intern
https://github.com/marton-eifert/shopizer_showcase/compare/3.2.5-actual...3.2.5-actual-QECI-fix--ATTEMPT-1-only-avoid-nested-loops-1-of-6-a#diff-1e8015b8c0587993f78ad512df20fc9e4c33d1c860a4b73e46ae804f7f7ee28d
ALGIT – QECI- Example: Avoid Nested Loops
06.06.2024 Green IT: MMIGIT & ALGIT
Klassifizierung: Intern
Leopoldstraße 146
80804 München
Theodor-Heuss-Str. 30
70174 Stuttgart
Wiesenhüttenplatz 25
60329 Frankfurt am
Main
metafinanz
Informationssysteme
GmbH
Vielen Dank
Klassifizierung: Intern
Energy Savings
with AI?
Green IT as a driver for future viability
Klassifizierung: Intern
Green AI
Red AI vs Green AI
Green IT: MMIGIT & ALGIT
Red AI leads to a surprisingly large
carbon footprint, and makes it difficult for
academics, students, and researchers to
engage in deep learning research.
The computational costs of state-of-the art AI research
has increased 300,000x in recent years. This trend,
denoted Red AI, stems from the AI community’s focus on
accuracy while paying attention to efficiency.
Schmid, Thomas; Hildesheim, Wolfgang; Holoyad, Taras;
Schumacher, Kinga, 2021. The AI Methods, Capabilities and
Criticality Grid. A Three-Dimensional Classification Scheme for
Artificial Intelligence Applications. KI - Künstliche Intelligenz 35 (3), S.
425–440 DOI: 10.1007/s13218-021-00736-4
06.06.2024
Klassifizierung: Intern
Green AI
Red AI vs Green AI
Green IT: MMIGIT & ALGIT
The term Green AI refers to AI research that yields novel results while taking into account the computational cost,
encouraging a reduction in resources spent. Whereas Red AI has resulted in rapidly escalating computational (and thus
carbon) costs, Green AI promotes approaches that have favorable performance/efficiency trade-offs.
Trainings
Precise
Green AI
Generalist
vs
Specialist
Program-
ming
Language
Pre-
trained
programs
Specific
process-
sors
06.06.2024

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ALGIT - Assembly Line for Green IT - Numbers, Data, Facts

  • 1. Klassifizierung: Intern Green IT - MMIGIT Green IT as a driver for future viability The Earth, The Climate and Everything Green Software Development Meetup 06.06.2024
  • 2. Klassifizierung: Intern metafinanz Contacts for GreenIT Ghazal Aakel Eric Jochum Expert GreenIT Expert GreenIT + 49 89 3605316444 + 49 89 3605316205 Ghazal.Aakel@metafinanz.de Eric.Jochum@metafinanz.de
  • 3. Klassifizierung: Intern Green IT — New thinking and acting Green IT as a driver for future viability
  • 4. Klassifizierung: Intern • Digitalization refers to the process of converting analogue information, data and processes into digital formats. • Digitalization has far-reaching effects on society and the economy and is seen as a driver of innovation, increased efficiency and growth. • This is not possible without energy! Green IT — New thinking and acting Digitalization Green IT is the key to sustainable digitalization and future viability!
  • 5. Klassifizierung: Intern Green IT — New thinking and acting MMIGIT (Maturity Model Integrated Green IT) – Solutions Landscape Green IT Initial Optimizing Managed Defined Repeatable Paper (not organized) Technic (defined & documented) Blueprint (standardized) KPIs (monitored) Self optimizing Dynamic Software Services and Resource Allocation AI Energy Optimization Green Data Management Heuristics for Hyperscale Hardware Management Domain-specific Hardware I Code I Pipeline Green Coding Green Architecture Future Hardware Architecture Strategies for Awareness Creation Sustainable ICT Skills Training Conscious Software Developer and Software Consumer Design for Reuse Green Energy Resources Energy Aware Software Solutions Distributed energy Landscapes Strategic Geolocation of Digital Infrastructure Cloudification Flexible Distributed and Disaggregated Data Storage Hardware Breakthroughs Location Shifting Measuring Energy Consumption of IT Applications Time Shifting Demand Shaping Technical Solutions Paradigm Shift Environmental Solutions Social Solutions 06.06.2024 Green IT: MMIGIT & ALGIT
  • 6. Klassifizierung: Intern Concrete Approaches Green IT as a driver for future viability
  • 7. Klassifizierung: Intern Klassifizierung: Intern Green IT: MMIGIT & ALGIT Green Development Guidelines Green Data Mgmt Green Archit ecture Green Codin g Development Lifecycle EC** Data Cloud - Production ECM App A App B App C… Power Consumption In Production CBDMS Storage / Release App X Power Consumption Measurement metafinanz GreenIT/RETIT GreenIT KPI PC/LoC Source Code Scan CAST Imaging Quick Energy Consumption Improvement (QECI) Success Stories Go Live ALGIT: The Assembly Line of Green IT Source Code Scan CAST Highlight GreenIT GitHub Order the applications by potential of improvement 06.06.2024
  • 8. Klassifizierung: Intern Klassifizierung: Intern ALGIT: The Assembly Line of Green IT Show Case Using Generative AI in Code Optimization Green IT: MMIGIT & ALGIT Model used: Generative Pre-trained Transformer Setting the Context for the AI model to Optimize Code • prompt the AI model to be familiar with the programming language and requiring its assistance. • introduce the code to the AI model • Instruct the model with Green Deficiencies rules (Guidelines) and best examples to improve its performance and accuracy. The Goal is to use specialist LLMs which are aware of the context 06.06.2024
  • 9. Klassifizierung: Intern Klassifizierung: Intern ALGIT: The Assembly Line of Green IT Show Case Using Generative AI in Code Optimization Green IT: MMIGIT & ALGIT Findings Categories Avoid instantiations inside loops Avoid string concatenation in loops Avoid nested loops Prefer comparison-to-0 in loop conditions Avoid calling a function in a condition loop Avoid primitive type wrapper instantiation Avoid Programs not using explicitly OPEN and CLOSE for files or streams Scan Report & QECI Program Size and CAST Highlight Findings Numbers Program Size (#LoC Lines of Code) 115.214 Number of Findings by CAST Highlight 233 Number of LoC / Finding 494 (every 494 LoC 1 Finding) RESULTS Original QECI API Call CPU [s] Energy [mWh] CPU [s] Energy [mWh] Savings [%] Absolute [mWh] api_v1_private_catalog__id_ 3,34 61,23 2,71 49,68 18,86 11,55 api_v1_private_catalog 2,99 54,82 2,53 46,38 15,38 8,43 api_v1_category_product__ProductId_ 2,11 38,68 1,66 30,43 21,33 8,25 api_v1_category__id__manufacturer_ 2,09 38,32 1,68 30,80 19,62 7,52 api_v1_category__friendlyUrl_ 2,08 38,13 1,66 30,43 20,19 7,70 api_v1_category 1,39 25,48 1,17 21,45 15,83 4,03 06.06.2024
  • 10. Klassifizierung: Intern Klassifizierung: Intern ALGIT – QECI- Example: Avoid Nested Loops 06.06.2024 Green IT: MMIGIT & ALGIT • Mastertextformat bearbeiten • Zweite Ebene • Dritte Ebene • Vierte Ebene • Fünfte Ebene https://github.com/marton-eifert/shopizer_showcase/compare/3.2.5-actual...3.2.5-actual-QECI-fix--ATTEMPT-1-only-avoid-nested-loops-1-of-6-a#diff-1e8015b8c0587993f78ad512df20fc9e4c33d1c860a4b73e46ae804f7f7ee28d
  • 11. Klassifizierung: Intern Klassifizierung: Intern ALGIT – QECI- Example: Avoid Nested Loops 06.06.2024 Green IT: MMIGIT & ALGIT • Mastertextformat bearbeiten • Zweite Ebene • Dritte Ebene • Vierte Ebene • Fünfte Ebene https://github.com/marton-eifert/shopizer_showcase/compare/3.2.5-actual...3.2.5-actual-QECI-fix--ATTEMPT-1-only-avoid-nested-loops-1-of-6-a#diff-1e8015b8c0587993f78ad512df20fc9e4c33d1c860a4b73e46ae804f7f7ee28d
  • 13. Klassifizierung: Intern Leopoldstraße 146 80804 München Theodor-Heuss-Str. 30 70174 Stuttgart Wiesenhüttenplatz 25 60329 Frankfurt am Main metafinanz Informationssysteme GmbH Vielen Dank
  • 14. Klassifizierung: Intern Energy Savings with AI? Green IT as a driver for future viability
  • 15. Klassifizierung: Intern Green AI Red AI vs Green AI Green IT: MMIGIT & ALGIT Red AI leads to a surprisingly large carbon footprint, and makes it difficult for academics, students, and researchers to engage in deep learning research. The computational costs of state-of-the art AI research has increased 300,000x in recent years. This trend, denoted Red AI, stems from the AI community’s focus on accuracy while paying attention to efficiency. Schmid, Thomas; Hildesheim, Wolfgang; Holoyad, Taras; Schumacher, Kinga, 2021. The AI Methods, Capabilities and Criticality Grid. A Three-Dimensional Classification Scheme for Artificial Intelligence Applications. KI - Künstliche Intelligenz 35 (3), S. 425–440 DOI: 10.1007/s13218-021-00736-4 06.06.2024
  • 16. Klassifizierung: Intern Green AI Red AI vs Green AI Green IT: MMIGIT & ALGIT The term Green AI refers to AI research that yields novel results while taking into account the computational cost, encouraging a reduction in resources spent. Whereas Red AI has resulted in rapidly escalating computational (and thus carbon) costs, Green AI promotes approaches that have favorable performance/efficiency trade-offs. Trainings Precise Green AI Generalist vs Specialist Program- ming Language Pre- trained programs Specific process- sors 06.06.2024