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# Optimizing Energy Use in Practice
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### What we will cover here
- What software development choices can reduce energy wastage
- How can we measure the actual energy usage of our code
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### Good software practice
Clean and maintainable software is essential
for sustainable/green software:
- Ease of use, reduce error during data settings
- Less bugs, avoid wasteful bugged runs
Note:
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### Software life cycle
- Thinking about long term sustainability, documentation.
- Reusable software is a key component of modern
software development, reducing the time/energy consuming
task of developing every component of complex softwares.
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### What language should I use?
- Typical conception of energy efficiency:
- C++ and Rust at the top
- Python and R at the bottom
- There is a lot of truth in this but there are a lot of important considerations
- **Many major numerical libraries in Python are not Python "under the surface"**
- The core is usually C++ or something like that.
- e.g. PyTorch, Tensorflow, numpy (and many others)
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### Use the right tool for each task
- Should I avoid "slow" and inefficient programming languages?
- Well, each has its purpose
- Python: great for stitching pieces together, easy prototyping
- C/C++/Fortran: better at crunching numbers fast
- Language benchmarks available in the literature can be misleading here
- Python/R for can be fine, it's more about the libraries you are using
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### What software should we optimize?
- **It is important that frequently used tools are as efficient as possible**
- Your single-use analysis scripts probably don't matter so much - just use the easiest language for the job
- Optimization is not free and costs development time (and energy) especially in lower level languages like C
- Generally there are not enough RSEs to do all the coding and optimizations, and researchers don't have time
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### Minimizing energy use
- Ultimately depends on the resources you use
- Number of CPU cores is clearly a large contributor
- Carbon footprint of memory is interesting - it doesn't matter how much you use, but how much is available. Don't request 10 times the memory you need on a server "just in case"
- Think about WHEN we run a job on a server. Energy mix different at different times.
- Can use tools such as the Carbon Aware Task Scheduler - tells you what is the best time to run in the next 48 hours.
- Most jobs have some flexibility - we don't care if we run it right now or in a few hours. Especially over the weekend.
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### Time to solution
- Energy is Power * time
- As a first approximation, more efficient (faster) software will be energy efficient
- It is possible to run on many CPUs although there is overhead from the parallelism.
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### Profile your code
- We recommend trying `CodeCarbon`
- More in this afternoon's hands-on workshop
- Also PMT (Power Measurement Toolkit), Rjoules, and many more
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### What if I can't/won't change the code?
- Perhaps you can profile the code but you cannot do much with the results
- e.g. You are not the developer, or don't feel experienced enough
- There are tools such as the Energy Aware Runtime
- Dynamically scales the CPU or GPU clock frequency up or down while the code is running
- When code is waiting a lot (for I/O or memory), frequency is scaled down
- Remember that Power ~ frequency^2
- Also covered in this afternoon's hands-on workshop (HPC-track)
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### A warning: Jevon's Paradox
```
In economics, the Jevons paradox occurs when technological progress increases
the efficiency with which a resource is used (reducing the amount necessary for any one use),
but the falling cost of use induces increases in demand enough that resource use is increased,
rather than reduced.
```
- [https://en.wikipedia.org/wiki/Jevons_paradox](https://en.wikipedia.org/wiki/Jevons_paradox)
* Are you thinking about this the right way?
* Is green computing really a purely technical issue?
* Is your footprint by computing really your biggest contribution to the climate crisis?
* What are other low hanging fruits you can tackle?
Note:
In practice, reducing energy use overall is more about organizational policy and value judgements about what the expected return is for a given amount of energy used.
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