Why PyPy Runs Faster Than CPython: Understanding JIT Compilation

Many developers claim that Python runs slowly, so this article introduces the PyPy interpreter, which can significantly improve execution speed.

For researchers, quickly turning ideas into code is crucial, and while Python excels at rapid prototyping, its execution speed is much slower than compiled languages like C or C++.

PyPy addresses this limitation by allowing Python code to run faster than even native C implementations in certain benchmarks.

Below is a sample script that sums numbers from 0 to 100,000,000 and prints the elapsed time; the code is shown unchanged inside a code block.

import time
from termcolor import colored

start = time.time()
number = 0
for i in range(100000000):
    number += i

print(colored("FINISHED", "green"))
print(f"Ellapsed time: {time.time() - start} s")

Running this script with the standard Python interpreter takes about 10 seconds, whereas PyPy completes it in roughly 0.22 seconds, even beating a comparable C implementation that runs in 0.32 seconds.

The speed advantage of PyPy comes from its use of just‑in‑time (JIT) compilation, which compiles frequently executed code paths to machine code at runtime.

In contrast, languages such as C, C++, Swift, Haskell, and Rust use ahead‑of‑time (AOT) compilation, translating source code to machine code before execution.

Interpreted languages like Python, JavaScript, and PHP keep source code unchanged and execute it line‑by‑line via an interpreter, which is slower than executing compiled machine code.

PyPy’s JIT compilation blends the flexibility of interpretation with the performance of ahead‑of‑time compilation, allowing it to generate machine code for hot code paths while retaining the dynamic nature of Python.

Consequently, PyPy can deliver Python execution speeds that rival or surpass compiled languages, making it a compelling choice for performance‑critical Python applications.