The document discusses green computing and software optimization to reduce energy consumption. It notes that up to 50% of an organization's energy costs can be attributed to IT departments, and up to 90% of ICT hardware energy is due to software. The author describes analyzing and improving software energy efficiency through techniques like SPELL, which uses spectrum-based fault localization to identify probable energy hot spots in source code. Initial studies found SPELL helped developers reduce energy consumption by 26% on average while spending 43% less time optimizing. The author concludes by describing the Green Software Lab collaboration between several universities.

1. Green Computing as an Engineering
Discipline
Rui Pereira
Talk @ InfoBlender
ruipereira@di.uminho.pt
29/03/2017 greenlab.di.uminho.pt

2. Going Green
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+ =

3. Global energy system is unsustainable
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4. Green Computing
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 Caught the attention of many companies allowing them to save:
“close to 50% of the energy costs of an organization can be attributed to
the IT departments”
- [PICMET, 2009]
“up to 90% of energy used by ICT hardware can be attributed to software”
- [The Greenhouse Gas Protocol Report, 2013]

5. Green Software
 Reducing energy consumption through software analysis and optimization
 Problem:
 How to analyze
 How to interpret
 How to improve
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6. Green Software
 Problems (extended to programmers):
 How to analyze
 How to interpret
 How to improve
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Mining questions about software energy consumption
- [MSR’14]
Integrated energy-directed test suite optimization
- [ISTA’14]
Seeds: A software engineer’s energy-optimization decision
support framework - [ICSE’14]

7. Energy vs. Power
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 Power (w) – rate (or effort) at which that work is done
 Energy (J) – amount of work done
 Power can change constantly while Energy is the accumulation
Energy = Power x Seconds
Power
Energy
100W360,000 J = x 3,600s

8. Is faster greener?
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Understanding energy behaviors of thread management constructs
- [OOPSLA’14]
Energy-aware software: Challenges, opportunities and strategies
- [Journal of Computational Science’13]
Haskell in green land: Analyzing the energy behavior of a purely
functional language - [SANER’16]
An experimental survey of energy management across the stack
- [OOPSLA’14]
Power and energy implications of the number of threads used
- [PPMG’15]
Using the greenup, powerup, and speedup metrics to evaluate
software energy efficiency - [IGSC’15]
slower
Is faster greener?

9. 9
What is greener?

10. The influence of the JCF on overall energy consumption
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Results (25k pop)

11. The influence of the JCF on overall energy consumption
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Faster
Slower

12. The influence of the JCF on overall energy consumption
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13. Functional Java
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14. Functional Java
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15. Functional Java
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16. Functional Java
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17. Functional Java
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18. SPELL - Spectrum-based Energy Leak Localization
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19. Spectrum-based Fault Localization (SFL)
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20. Spectrum-based Fault Localization (SFL)
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Error vector
Oracle
Low
High
Energy
Energy
Energy
Energy
Problem: Energy cannot be defined as binary values

21. SPELL
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22. SPELL – initial studies
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 Developers found the information to be very useful
 Spent on average 43% less time
 Produced more energy efficient programs (26% less energy on average)
 SPELL is:
 Language independent
 Multi level analysis
 Multi hardware component analysis
 Points to probable hot spots in source code

23. SPELL – initial studies
23
 Developers found the information to be very useful
 Spent on average 43% less time
 Produced more energy efficient programs (26% less energy on average)
 SPELL is:
 Language independent
 Multi level analysis
 Multi hardware component analysis
 Points to probable hot spots in source code

24. Green Software Lab
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 3 Year FCT project
 University of California @ Irvine
 University of Kosice
 Ericson @ Budapest

25. Green Software Lab
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Find more info @ our page: http://greenlab.di.uminho.pt/
Questions?

26. Green Computing as an Engineering
Discipline
Rui Pereira
Talk @ InfoBlender
ruipereira@di.uminho.pt
29/03/2017 greenlab.di.uminho.pt