Summary
Numba is a tool that compiles fast, specialized versions of Python functions at runtime. In this chapter, we learned how to compile, inspect, and analyze functions compiled by Numba. We also learned how to implement fast NumPy universal functions that are useful in a wide array of numerical applications. Finally, we implemented more complex data structures using the nb.jitclass decorator. Overall, Numba is built to accelerate numeric loops that are common in scientific computing. As we have seen, Numba works seamlessly with the popular NumPy library.
Tools such as PyPy allow us to run Python programs unchanged to obtain significant speed improvements. We demonstrated how to set up PyPy, and we assessed the performance improvements on our particle simulator application. We have also seen that, unlike Numba, PyPy doesn't operate on a function level but instead seeks to implement a more efficient interpreter for a whole Python program.
We also, briefly, described the...
