HPC application development learning path

If you want to develop HPC applications, you can consider following the following training sessions.

graph TD Best_practices_for_scientific_computing[Best practices for scientific computing] --> Version_control_with_git[Version control with Git] Best_practices_for_scientific_computing[Best practices for scientific computing] --> Linux_intro[Linux introduction] Linux_intro --> HPC_intro[HPC introduction] HPC_intro --> Containers_on_HPC[Containers on HPC] Best_practices_for_scientific_computing --> Programming[Programming] Programming --> Defensive_programming_and_debugging[Defensive programming and debugging] Defensive_programming_and_debugging --> Code_optimization[Code optimization] Programming --> Code_optimization Programming --> Generative_AI_in_software_engineering_and_data_analysis[Generative AI in software\nengineering and data analysis] HPC_intro --> Defensive_programming_and_debugging Code_optimization --> Parallel_computing[Parallel computing] Code_optimization --> GPU_computing[GPU computing] click Best_practices_for_scientific_computing "https://gjbex.github.io/Best-practices-for-scientific-computing/" "Best practices for scientific computing" click Version_control_with_git "https://gjbex.github.io/Version-control-with-git" "Version control with Git" click Linux_intro "https://gjbex.github.io/Training-sessions/linux_intro" "Linux introduction" click HPC_intro "https://gjbex.github.io/Training-sessions/hpc_intro" "HPC introduction" click Containers_on_HPC "https://gjbex.github.io/Containers-for-HPC/" "Containers on HPC" click Generative_ai_for_software_engineering_and_data_analysis "https://gjbex.github.io/Training-sessions/generative_ai_for_software_engineering_and_data_analysis" "Generative AI in software\nengineering and data analysis" click Programming "https://gjbex.github.io/Training-sessions/programming" "Programming" click Defensive_programming_and_debugging "https://gjbex.github.io/Defensive_programming_and_debugging/" "Defensive programming and debugging" click Code_optimization "https://gjbex.github.io/Code-optimization/" "Code optimization" click Parallel_computing "https://gjbex.github.io/Training-sessions/parallel_computing" "Parallel computing" click GPU_computing "https://gjbex.github.io/Training-sessions/gpu_computing" "GPU computing"

If you are new to software development in the context of scientific computing, you may want to start with "Best practices for scientific computing".

The next step is to fammliarize yourself with the basics of working on the Linux command line and the HPC infrastructure.

Of course, you coding skills should be up to par. For more information on this topic, see "Programming".

Version control is an essential part of software engineering. For more information on this topic, see "Version control with git".

Containers are useful tools in the context of software engineering both to create a complete, stable and portable development environment, but also as a means to distribute your software.

Writing robust code that has been well-tested is an essential part of software development. Having tests in place is also essential when trying to optimize your code. For more information on this topic, see "Defensive programming and debugging".

Effectively optimizing your code implies that you have a thorough understanding of the hardware you are using and how your code interacts with it. For more information on this topic, see "Code optimization".

Parallel or GPU computing is often required to take full advantage of modern compute nodes. For more information on this topic, see "Parallel computing" and "GPU computing".