Course Introduction to scientific computing (Master 2 EFM)#
Content#
During the first sessions, we will present an introduction on scientific computing and programming:
Computers to compute (CPU, memories, GPU, clusters)
Numbers in computers
Programming languages
Difference open-source / close-source
Operating systems and importance of Linux for scientific computing
Install a good environment, example of Conda
Versioning and Gitlab (https://gricad-gitlab.univ-grenoble-alpes.fr)
We will study some basics on Linux and Python for scientific computing. Acquire strong basis in Python to use it efficiently
Some sessions will be dedicated to practical exercises on
Computation of integrals,
Finite Difference methods,
Gradient descent and
Machine learning.
You will also have to work for this course on a “personal project” on an open subject (see Mini projects). One session will be dedicated to work on this project.
The teachers#
Pierre Augier: researcher at LEGI studying geophysical turbulence with experiments and numerical simulations. Maintainer of the FluidDyn project.
Enzo Le Bouedec: PhD at LEGI on “Pollution in the Grenoble valley: a weather-type approach”.
Other authors of some documents: Pierre Augier (LEGI), Cyrille Bonamy (LEGI), Eric Maldonado (Irstea), Franck Thollard (ISTerre), Loïc Huder (ISTerre)…
Setup the environment for this course#
One of the first step is to setup a good computing environment. However, it may be better to first read this introduction on Linux and Bash to understand what we are going to do.
The installation instructions are in this file: install.md. Note that you can copy/paste commands!
Clone this repository#
Clone the repository with Mercurial (and the extension hg-git, as explained here):
hg clone https://gricad-gitlab.univ-grenoble-alpes.fr/meige-legi/scientific-computing-m2-efm.git
or with ssh (so you need to create a ssh key and copy the public key on https://gricad-gitlab.univ-grenoble-alpes.fr):
hg clone git@gricad-gitlab.univ-grenoble-alpes.fr:meige-legi/scientific-computing-m2-efm.git
Go further#
If you feel that you need more advanced content, you can work on this Python HPC training (the associated slices are here).