Why PyPy Runs Python Code Faster Than C: Understanding JIT Compilation

For researchers and developers, quickly turning ideas into runnable code is essential, and Python excels at this by allowing focus on concepts rather than low‑level details.

However, Python’s interpreted nature makes it considerably slower than compiled languages such as C or C++, prompting the need for a faster execution path after prototyping.

PyPy addresses this problem by enabling Python code to run faster than even native C implementations, thanks to its just‑in‑time (JIT) compilation strategy.

Benchmarking a simple loop that sums integers from 0 to 100,000,000 shows the default Python interpreter taking about 10 seconds, while PyPy completes the task in 0.22 seconds; a comparable C version runs in 0.32 seconds, demonstrating PyPy’s superior speed.

The speed advantage stems from PyPy’s JIT compiler, which translates frequently executed Python bytecode into machine code on the fly, unlike traditional interpreters that execute code line‑by‑line.

In contrast, ahead‑of‑time (AOT) compiled languages like C, C++, Swift, Rust, and Haskell convert source code into machine code before execution, resulting in fast runtime performance but less flexibility.

Interpreted languages such as Python, JavaScript, and PHP keep source code unchanged and rely on an interpreter to read and execute each line at runtime, offering portability and ease of development.

JIT compilation combines the benefits of AOT compilation and interpretation, allowing PyPy to achieve high performance while retaining the dynamic nature of interpreted languages.

<code>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")
</code>