This session is about using native code with Python to ensure computationally intensive programs execute as fast as is possible.
Python is well known as a language that does not execute computationally intensive programs quickly. Traditionally, Cython and/or NumPy have been the tools to speed things up. There is interest in Numba as a new way forward. However C++14, D, Chapel, and possibly Rust, maybe Go, are there, are much easier to work with than you might think, and lead to very high performance and easily maintained code.
In this session we will look at a couple of example codes to provide signposts as to how we can take a more polyglot approach to the construction of computationally intensive systems.