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

Rapid prototyping is essential for researchers, and Python enables quick idea implementation without worrying about low‑level details.

However, Python’s execution speed is much slower than compiled languages such as C or C++, and manually converting Python code to C adds extra work.

PyPy addresses this problem by allowing Python code to run faster, often surpassing even native C performance.

<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>

The benchmark shows the default CPython interpreter takes about 10 seconds for the loop, while PyPy completes it in 0.22 seconds, beating a comparable C implementation that needs 0.32 seconds.

PyPy’s speed comes from Just‑In‑Time (JIT) compilation, which combines the flexibility of interpreted languages with the performance of ahead‑of‑time (AOT) compiled code.

In AOT compilation (used by C, C++, Swift, Rust, etc.), source code is transformed into machine code before execution, so the processor runs native instructions directly.

Interpreted languages like Python, JavaScript, and PHP keep source code unchanged; the interpreter reads and executes each line at runtime.

PyPy’s JIT first translates frequently executed Python code into machine code, then runs that compiled version, achieving the speed of AOT while retaining the dynamism of interpretation.