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Please find below all the information necessary to participate.
The online course will be given from July 25 to July 27, 2023,
(Tuesday to Thursday) with lectures in the morning from 9:00 to
12:30 and exercise sessions at 16:00 in the afternoon. We provide
the slides and exercises as Jupyter notebooks that participants can
work on independently. In the exercise session, we will go through
solutions to some of the exercises and we also offer to answer
questions.
The tentative schedule is as follows:
------------|---------------|----------------------------------------
Date | Time | Topic
------------|---------------|----------------------------------------
Tue, 07/25 | 9:00 - 10:30 | 1 Introduction, Basic HPC
| 10:30 - 11:00 | Break
| 11:00 - 12:30 | 2 Numpy, Scipy
| |
| 16:00 - 17:00 | Exercise session
| |
| |
Wed, 07/26 | 9:00 - 10:30 | 3 Cython, Numba, Interfacing C/Fortran
| 10:30 - 11:00 | Break
| 11:00 - 12:30 | 4 GPU, Profiling, Software Engineering,
| 16:00 - 17:00 | Exercise session
| |
| |
Thu, 07/27 | 9:00 - 10:30 | 5 Parallel programming I: Shared Memory, Dask
| 10:30 - 11:00 | Break
| 11:00 - 12:30 | 6 Parallel programming II: MPI, Q&A
| |
| 16:00 - 17:00 | Exercise session
------------|---------------|----------------------------------------
The course will be given via Zoom, the connection details will be
sent to the participants by email in due time.
The virtual room will be opened about 15 minutes before the
lectures and exercises start.
The course material (i.e. Jupyter notebooks from the lectures and
exercises, and additional files) is available via Git.
The link will be sent to the participants by email in due time.
To run the code examples and do the exercises, a Python 3
installation including NumPy, SciPy, Numba, matplotlib, and Jupyter
is required. For the part on Cython and interfacing with C and
Fortran, a compiler such as `gcc` and `gfortran` is necessary. For
the last part on distributed-memory parallelization using MPI, a
working mpi4py installation is required.
For all participants, there will be an option to do the exercises
and experiments on Jupyter instances in a Cloud platform. Details
will be communicated separately in due time.
Locally on your laptop, we recommend to install the Anaconda Python
Distribution which contains most of the necessary packages.
The Python programming language has become very popular in
scientific computing for various reasons. Users not only implement
prototypes for numerical experiments on small scales, but also
develop parallel production codes, thereby partly replacing compiled
languages such as C, C++, and Fortran. However, when following this
approach it is crucial to pay special attention to performance. This
course teaches approaches to use Python efficiently and reasonably
in a HPC environment. The first lecture gives a whirlwind tour
through the Python programming language and the standard library. In
the following, the lectures strongly focus on performance-related
topics such as NumPy, Cython, Numba, compiled C- and Fortran
extensions, profiling of Python and compiled code, parallelism using
multiprocessing and mpi4py, parallel frameworks such as Dask, and
efficient IO with HDF5. In addition, we will cover topics more
related to software-engineering such as packaging, publishing,
testing, and the semi-automated generation of documentation.
Finally, basic visualization tasks using matplotlib and similar
packages are discussed.
The lectures will be given based on Jupyter notebooks and will
include many reusable code examples. For each topic, hands-on
exercises are available and will be discussed in separate sessions.
On the last day, there will be time for a general question & answer
session.