Make Python Code Faster With Numba - Analytics India Magazine

Make Python Code Faster With Numba - Analytics India Magazine

Make Python Code Faster With Numba. Numba is an open-source Just-In-Time compiler that enables Python developers to translate Python and NumPy code directly into machine code.

One of the major complaints that people, mostly die-hard C++ users, have with Python is that it’s slow. Yes, Python is a dynamically typed interpreted language and it is slow. Most people don’t know that Python can provide you direct access to your hardware to perform intensive calculations. Numba is an open-source Just-In-Time compiler that does exactly that. It enables Python developers to translate a subset of Python and NumPy code directly into machine code by using the LLVM  compiler in the backend. In addition to that, Numba offers a wide range of choices for parallelizing Python code for CPUs and GPUs with trivial code changes. There are a lot of ways to approach compiling Python; the approach Numba takes is to compile individual functions or a collection of functions just in time as you need them.

Numba takes the bytecode of your function and looks at the types of arguments you pass to it. The arguments, supported by Python objects, are translated into representations with no CPython dependencies. This process is called “unboxing”. Once Numba has these two things, it goes down an analysis pipeline to figure out the types of everything inside the function based on what’s passed in. It then generates an intermediate representation (IR) of what the function is doing, filling in all the data types and all that kind of stuff. LLVM is responsible for most of the hard work; it inlines functions, auto vectorize loops, does other low-level code optimization expected by a C compiler and generates the machine code. This machine code is cached so that the next time the function is run, Numba doesn’t need to go through this whole pipeline but instead skip to the end. 

An important thing to note is that Numba doesn’t interact with or change the interpreter. This means it can only optimize what’s locally possible in the function; for instance, it can’t go to other parts of your program and say that “_Oh, the operation would be a lot faster if this list was a NumPy array_”. Another thing Numba does is that it looks for built-in and NumPy methods and swap them out with its own implementation.

developers corner just-in-time compiler llvm making python fast numba optimizing python python multithreading

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