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Week 2 summary

We hope that you have enjoyed the second week of Python in High Performance Computing!

This week, we have looked into NumPy arrays and how to use them for more efficient numerical calculations. By using fixed size, uniformly typed data arrays, it enables one to leverage the performance of optimised numerical libraries directly from Python.

By now, you should know how to create and manipulate NumPy arrays, how to do numerical calculations with them, and how to use simple vectorised operations. You should also understand the key differences, such as data layout in memory, between Python lists and NumPy arrays, as well as the memory consumption of temporary arrays and how to mitigate for it.

Next week, we will explore how to use static compiled code to speed things up in Python. Meanwhile, please discuss limitations of NumPy and answer the quiz in the remaining steps of this week.

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This article is from the free online course:

Python in High Performance Computing

Partnership for Advanced Computing in Europe (PRACE)