--- title: Optimizing in Practice type: slides order: 1 --- # Optimizing Energy Use in Practice === ### What we will cover here - What software development choices can reduce energy wastage - How can we measure the actual energy usage of our code === ### 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: === ### 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. Note: === ### 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) Note: === ### 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 Note: === ### 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 Note: === ### 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. Note: === ### 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. Note: === ### Profile your code - We recommend trying `CodeCarbon` - More in this afternoon's hands-on workshop - Also PMT (Power Measurement Toolkit), Rjoules, and many more Note: === ### 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) Note: === ### 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. === www.esciencecenter.nl info@esciencecenter.nl 020 - 460 47 70

No Title

Learning Objectives