Chapter 5. High-Performance Computing

In this chapter, we will cover the following topics:

• Using Python to write faster code

• Accelerating pure Python code with Numba and Just-In-Time compilation

• Accelerating array computations with NumExpr

• Wrapping a C library in Python with ctypes

• Accelerating Python code with Cython

• Optimizing Cython code by writing less Python and more C

• Releasing the GIL to take advantage of multi-core processors with Cython and OpenMP

• Writing massively parallel code for NVIDIA graphics cards (GPUs) with CUDA

• Distributing Python code across multiple cores with IPython

• Interacting with asynchronous parallel tasks in IPython

• Performing out-of-core computations on large arrays with Dask

• Trying the Julia programming language in the Jupyter Notebook