jakobnissen / Hardware_introduction
What scientific programmers must know about CPUs and RAM to write fast code.
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What scientists must know about hardware to write fast code
This blog post is hosted on the BioJulia website: https://biojulia.net/post/hardware/
This document is written as a Jupyter Notebook. (.ipynb format). If you can, I recommend running the code in Jupyter using an IJulia kernel so you can play around with it and learn. Alternatively, read it on BioJulia's website.
This notebook covers:
- Why you must minimize disk access
- What a CPU cache is, and how to use it effectively
- Memory alignment
- How to read assembly code and why you must do it
- Why you should reduce allocations
- SIMD vectorization
- Struct of arrays vs array of structs
- Special CPU instructions
- Function inlining
- Loop unrolling
- Branch prediction
- Multithreading
- Why GPUs are fast at some things and slow at others
How to contribute
Pull requests (PRs) are welcome. Please only modify the .jl file in your PR. Unlike the .ipynb file, .jl files are plain text files and play much more nicely with git, making it easier to review your PR.
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for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].