Chapter 5: Exploring Compilers
Python is a mature and widely used language, and there is great interest in improving its performance by compiling functions and methods directly to machine code rather than executing instructions in the interpreter. We have already seen a compiler example in Chapter 4, C Performance with Cython, where Python code is enhanced with types, compiled to efficient C code, and the interpreter calls are sidestepped.
In this chapter, we will explore two projects, Numba and PyPy, that approach compilation in a slightly different way. Numba is a library designed to compile small functions on the fly. Instead of transforming Python code to C, Numba analyzes and compiles Python functions directly to machine code. PyPy is a replacement interpreter that works by analyzing the code at runtime and optimizing the slow loops automatically.
These tools are called Just-In-Time (JIT) compilers because the compilation is performed at runtime rather than before running...
