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Author: gjbex

docs/README.md

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+Although vanilla Python is fairly slow and hence not a good candidate,
+there are several options to significantly increase the efficiency of
+Python programs.
+
+
+## Learning outcomes
+
+When you complete this training you will
+
+  * understand and identify performance bottlenecks of Python;
+  * know some libraries that can help improve performance for scientific
+    computing such as numpy, numexpr and numba;
+  * be able to use Cython to improve your code's performance;
+  * be able to wrap C, C++ and Fortran code to use it from Python;
+  * understand the opportunities and pitfalls of multithreaded
+    programming with Python;
+  * be able to write distributed application using MPI;
+  * have an understanding of how frameworks for distributed
+    computing such as dask and pyspark work.
+
+
+## Schedule
+
+Total duration: 4 hours.
+
+  | Subject                                     | Duration |
+  |---------------------------------------------|----------|
+  | introduction and motivation                 |  5 min.  |
+  | performance and profiling                   | 25 min.  |
+  | libraries                                   | 10 min.  |
+  | Cython                                      | 60 min.  |
+  | coffee break                                | 10 min.  |
+  | interfacing with C/C++/Fortran              | 30 min.  |
+  | multithreaded programming                   | 10 min.  |
+  | MPI                                         | 45 min.  |
+  | dask                                        | 15 min.  |
+  | pyspark                                     | 20 min.  |
+  | wrap up                                     | 10 min.  |
+
+
+## Training materials
+
+Slides are available in the
+ [GitHub repository](https://github.com/gjbex/Python-for-HPC),
+as well as example code and hands-on material.
+
+
+## Target audience
+
+This training is for you if you need to use Python for computationally
+intensive scientific computing.
+
+
+## Prerequisites
+
+You will need experience programming in Python, using numpy, and have a passing
+familiarity with C/C++.  This is not a training that starts from scratch.
+
+If you plan to do Python programming in a Linux or HPC environment you should
+be familiar with these as well.
+
+
+## Trainer(s)
+
+  * Geert Jan Bex ([[email protected]](mailto:[email protected]))