--- title: Optimizing in Practice type: slides order: 1 --- # Optimizing Energy Use in Practice === ## Good software practice Clean and maintainable software is essential to 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: === ## Right tool for each task Are there slow programming languages that should be avoided ? - each tool has it's purpose. Python: great for stitching pieces together, easy prototyping C/C++/Fortran: better at crunching number fast, but not so good at doing string manipulation, ... Benchmark available in the litterature can be missleading. -> Use the right tool for each task (could simply be the tool you're confortable with), blending languages when necessary.

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: === ## Energy usage Energy is power * time -> as first approximation, more efficient (faster) software will be energy efficient. A note on parallelism and scaling issue.

Note: === ## How to measure introducing PMT (Python) / RJoules (R) / EAR (language agnostic).

Note: === www.esciencecenter.nl info@esciencecenter.nl 020 - 460 47 70

Module: optimizing-energy-use

Learning Objectives