Numba Tutorial: Accelerating Python Code with JIT Compilation

Features like “paste mode” streamline large code block input, while commands such as clear and direct typing of help or cgi, crypt, and mailcap simplify usability. Java and Javascript engines are examples of JITs that have put really good care into warmup time, but you may find that languages built for academic uses have monstrous warmup times in favour of snazzy peak performances. The simplicity of this kind of jitting makes it easy for Julia to also supply AOT compilation. It also helps Julia to benchmark very well, definitely a tier above languages like Python and comparable to C.

A Deep Introduction to JIT Compilers: JITs are not very Just-in-time

There are applications which are infeasible with the overhead of interpretation and dynamicness, but they aren’t as common as you’d think (and often, solved by calling into machine code-compiled code selectively). Numba is a just-in-time compiler specifically designed for numerical and scientific Python code. It leverages the Low-Level Virtual Machine (LLVM) compiler infrastructure to generate optimized machine code dynamically. Numba is known for its ability to accelerate numerical computations, making it an excellent choice for tasks such as array processing, simulations, and data analysis.

At the end of the day, it’s a compiler too, which applies many optimizations like JIT compilers. Python, known for its simplicity and ease of use, has gained immense popularity among developers. In this article, we’ll explore the concept of JIT compilation, its benefits, and delve into popular Python JIT compilers such as PyPy and Numba.

Speed Up Your Python Code: Tips, Techniques, and JIT Compilation

• While these features remain experimental, they provide developers with new opportunities to explore enhanced performance in Python.
• Let’s now compare the performance of a regular Python function versus the JIT-compiled function.
• It operates by using a library of precompiled machine code templates for common operations, such as arithmetic or function calls.
• JIT is commonly used in modern web browsers to optimize the performance of JavaScript code.
• In all,the current implementation is made up of about 900 lines of build-time Pythoncode and 500 lines of runtime C code.
• Numba’s just-in-time compilation relies on low-level LLVM infrastructure, which may not support all Python features.

Another factor affecting Python’s performance is the Global Interpreter Lock (GIL). The GIL ensures thread safety by allowing only one thread to execute Python bytecode at a time. While this simplifies memory management, it can restrict parallel execution and limit the performance gains from multi-core processors. The copy-and-patch approach is easier to implement for CPython than other JIT-compiler techniques, but still generates high quality code. There is a dependency on LLVM but only when the CPython runtime is built, not for deployment. Platforms supported will include Windows, macOS for both x86 and ARM64, and Linux also for x86 and ARM64.

Ship high performance Python applications without the headache of binary compilation and packaging. Your source code remains pure Python while Numba handles the compilation at runtime. We test Numba continuously in more than 200 different platform configurations. Numba can automatically translate some loops pyjion into vector instructions for 2-4x speed improvements. Numba adapts to your CPU capabilities, whether your CPU supports SSE, AVX, or AVX-512.

So how does this JIT work?¶

It’s an idea only proposed recently in 2021 and designed as a fast algorithm for dynamic language runtimes. As we can see here, the compilation + execution and only execution times are very similar to each other now. There is some randomness in the results, which is causing their values to be different. But the main point here is that the compilation time has disappeared (leaving only the execution time).