1. Introduction

1.1. Scientific Computing

• https://en.wikiversity.org/wiki/Scientific_computing

• https://www.nature.com/articles/d41586-024-00725-1

1.2. High Performance Scientific Computing

https://en.wikipedia.org/wiki/High-performance_computing?useskin=vector

https://hpsf.io/

• Performance versus time to solution

From: "https://www.researchgate.net/publication/336577121_BACKUS_Comprehensive_High-Performance_Research_Software_Engineering_Approach_for_Simulations_in_Supercomputing_Systems/figures?lo=1"

• CO2 Comsumption

From: "https://www.nature.com/articles/s41550-021-01342-y"

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<https://datascience.aero/green-programming-reducing-your-carbon-emissions-when-coding/ >

• Language use in a typical cluster

From: "https://cpufun.substack.com/p/is-fortran-a-dead-language"

• Moore law:

  • https://en.wikipedia.org/wiki/Moore%27s_law?useskin=vector

  • https://ourworldindata.org/grapher/transistors-per-microprocessor

Moore law

1.3. Training HPC

• https://www.archer2.ac.uk/training/ and https://www.epcc.ed.ac.uk/education-and-training/training-courses

• https://www.hlrs.de/training/2024/

• NLHPC courses https://www.nlhpc.cl/eventos/curso-introductorio-usuarios-nlhpc-mayo-2023/

• https://www.futurelearn.com/courses/supercomputing

• https://hpc.fau.de/

• https://www.fz-juelich.de/en/ias/jsc/education/training-courses

1.4. Computer Architecture

See 01-intro.pdf

Check:

• https://computers-are-fast.github.io/

• Branch education, computer hardware: https://www.youtube.com/watch?v=d86ws7mQYIg&t=0s