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# Energy Budget
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### We will try to answer
- _How much energy is a lot of energy?_
- _Does Information Computing Technology use a lot of energy?_
- _Where is the energy going?_
- _What does this mean for carbon production?_
- _What can we do about it?_
- _Will these things help?_
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## _How much energy is a lot of energy?_
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## Typical values of energy
| Energy (J) | Examples | Equ. gCO2 |
| :-------- | -------: |--------:|
| 1.0e0 | ??????????????????????????????????? | |
| 1.0e1 | ??????????????????????????????????? | |
| 1.0e2 | ??????????????????????????????????? | |
| 1.0e3 (kJ) | ??????????????????????????????????? | |
| 1.0e4 | ??????????????????????????????????? | |
| 1.0e5 | ??????????????????????????????????? | |
| 1.0e6 (MJ) | ??????????????????????????????????? | |
| 3.6e6 (1 kWh)| ??????????????????????????????????? | 305 |
| 1.0e7 | ??????????????????????????????????? | |
| 1.0e8 | ??????????????????????????????????? | |
| 1.0e9 (0.27 MWh) | ??????????????????????????????????? | | |
Note:
Do you have a feel for how much 1 Joule actually is?
Press down arrow to see the examples for different orders of magnitude.
==
## Typical values of energy
| Energy (J) | Examples | Equ. gCO2 |
| :-------- | -------: |--------:|
| 1.0e0 | Lift an apple to your mouth | |
| 1.0e1 | | |
| 1.0e2 | | |
| 1.0e3 (kJ) | Standby LED (0.3W) for 1 hour | |
| 1.0e4 | LED-based lightbulb (3W) for 1 hour | |
| 1.0e5 | 15 mn bike ride | |
| 1.0e6 (MJ) | ~ 2km drive | |
| 3.6e6 (1 kWh)| | 305 |
| 1.0e7 | Human energy need per day | |
| 1.0e8 | Averaged daily cons. of NL home | |
| 1.0e9 (0.27 MWh) | Round trip flight AMS-LON for 2 | |
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## _Does Information Computing Technology use a lot of energy?_
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### ICT under scrutiny
<div style="width: 40%; float: left; margin-top: 1%">
* Information Computing Technology (ICT)
* Predicted major increase in electricity demand:
- from 8% to 21% in 2030.
* Responsible for about 2% of global CO2 emmisions, on par with the aviation sector.
</div>
<div style="width: 60%; float: right">
<img src="media/ICT_EnergyConsumption_Jones_2018.png" width="100%")
</div>
Note:
On the graph:
- 4 components to ICT demand: network infra., consumer device (not including IoT-connected devices), data center and production from first three components (cradle-to-gate)
- this is an expected prediction, best and worst case scenario are 12% and 50%, resp.
As researchers we use devices (laptops, workstations), local/national clusters (e.g. Snellius) and cloud services (SURF Cloud, AWS, ...).
Our day2day work embedded in ICT.
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## Overall contribution of ICT
- Computing carbon footprint can be split into two main contributions:
- *Embodied*: from raw material extraction, to distribution
- *Usage*: Powering, memory, infrastructure
Note:
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### Data centers
<div style="width: 40%; float: right">
* Compute and/or storage
* Efficiency characterized by Power Usage Effectiveness (PUE)
- PUE = P_{total_facility} / P_{IT_facility}
* Quantifies overhead. Gives you e.g. how much cooling power you need per unit of compute
* Best data centers are now down to about 10% extra for cooling, but still large variability. Used to be around 100%.
</div>
<div style="width: 60%; float: left; margin-top: 1%">
</div>
Note:
- P_{IT_facility} in PUE not limited to CPU/GPU, also include network, memory storage, backups, ...
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## _Where is the energy going?_
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### What is using the energy?
<div style="width: 40%; float: left; margin-top: 1%">
* Devices are powered by electricity.
* Electrons themselves are used to perform the operations encoded in your softwares.
* Number of operations processors can crunch per second has continuously increased.
</div>
<div style="width: 60%; float: right">
</div>
Note:
Over the past 40 years, the number of operations processors
can crunch per second has continuously increased
Figure: consumer CPU performances over 40 years (relative). (Hennessy J. and Patterson D. A., Computer Architecture (5th edition))
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### Supercomputers are also doing more
Note:
This trend extends to supercomputers (e.g. Snellius) and data centers. Top500 records performances
of the world's (500) biggest computers on the same problem for over 30 years:
Initially growth faster than Moore's law, but slowing down past 2013. Switch to GPUs around 2019
kept the curve on track with Moore's law even though transitor/surface is increasing slower than Moore's law.
Figure: now showing GFLOPs, blue biggest supercomputer, red average of the 500.
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### CPU energy consumption: how does it relate to FLOPs ?
Increase in FLOPs mostly related to:
- improved manufacturing, more transistor/surface (Moore's law)
- low level instructions handling improvements
- increase in CPU clock rate (until mid-2000)
Note:
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### What does it mean for energy ?
- More transistors lead to more power, however smaller transistors need less voltage
- CPU have a baseline (idle) power consumption (P_0), due to current leakage, unless closing circuit totally
- Active power consumption of CPUs: ~ P_0 + C * V(f_c)^2 * f_c ~ f_c^3
f_c: clock rate
V: voltage, higher voltage needed with higher clockrate to transfer information faster
- Energy: power * time, time needed 1/f_c (fixed number of operations) -> energy ~ f_c^2
Note:
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### Computer performances: FLOPs/Watt
<div style="width: 40%; float: right">
* Raw FLOPs data are not an appropriate measure of how efficient a CPU (or GPU) is.
* The 10^8 increase in FLOPs does not translate to needing a nuclear power plant to run Snellius.
* Green500 ranks the Top500 supercomputer based on their power consumption since 2014.
Compared to Koomey's prediction: factor 2 improvement every 1.57 years.
</div>
<div style="width: 60%; float: left; margin-top: 1%">
</div>
Note:
Figure: now showing GFLOPs/Watts, compared to Koomey's prediction (CPU then GPU after 2019).
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## _What does this mean for carbon production?_
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## Energy Carbon intensity
- Carbon intensity has a large spatial and temporal variability.
- Extreme differences between countries
- Countries with very low carbon intensity (e.g. Norway): 20g per kWh
- Countries with high carbon intensity (e.g. Australia): 700g per kWh
- You can make a big difference by running the exact same thing on the same hardware, but in a different country
Note:
So far, we've talked about energy -> a proxy for CO2 emission, using the energy carbon intensity
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You can get an idea for real time carbon intensity in Europe here:
[https://app.electricitymaps.com/map](https://app.electricitymaps.com/map)
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# Typical footprint
- Carbon footprint of data centres anually is around 100 MT of CO2 equivalent
- That is the same as the entire US aviation in the same time.
- Not all these data centres are doing HPC
- About 500 Tonnes of CO2 estimated for training GPT3
- IPC says we should aim for 2 tonnes of CO2 per person per year to keep global warming in check.
- Not every model has such huge impacts, but we need to be mindful
Note:
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# Dutch specific energy mix
[Nowtricity](https://www.nowtricity.com/country/netherlands)
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## _What can we do about it?_
Note:
There are many tools and initiatives aimed at dealing with these issues
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## Estimating impact
- Loïc Lannelongue started a project called Green Algorithms:
- Made a calculator to estimate energy cost and carbon footprint of your algorithm
- Necessary for assessing how to make computing more environmentally sustainable
Note:
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## The GREENER framework
- A set of principles for green computing analogous to the FAIR principles for data
- GREENER:
- **G**overnance
- **R**esponsibility
- **E**stimations: Use calculators to estimate impact of the computing
- **N**ew collaboration
- **E**ducation: Need to include the notion that it has a carbon footprint during training of new researchers
- **R**esearch: Still don't know a lot about computing power usage
Note:
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## The online calculator
<div style="width: 60%; float: left; margin-top: 1%">
<img src="media/green-algorithms-calculator-example.png" />
</div>
<div style="width: 40%; float: right; margin-top: 1%">
- The Green Algorithms online calculator makes it quick and easy to estimate the carbon footprint
- Can be found here: <http://calculator.green-algorithms.org/>
- There is also a Green Algorithms tool for HPC
</div>
Note:
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## _Will these things help?_
Note:
Some practical considerations to discuss
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## Yet more paperwork?
- Is this just more work for researchers when filling out grant applications?
- All applications must estimate the environmental impact of their models.
- They did this in France and researchers still applied. The Green Algorithms Calculator was required to be used for the applications. Researchers accepted it was a fair request and still continued applying.
- If a project is cheap financially, but has a large carbon cost, there should be an explicit justification why
Note:
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### What is too much energy anyway?
- Do the potential benefits outweigh the environmental costs?
- **We should think of energy (or CO2) the same way we think of money**
- What matters is the _cost-benefit_ ratio
- Is €1M a lot? Not if it leads to curing a major disease
- Currently researchers are used to making the scientific case for the money they request
- They should also be able to make the case for the corresponding carbon footprint
- The energy and carbon cost can often be hidden or abstracted from the researcher's perspective
Note:
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