Exploring Python Performance: PyPy vs CPython vs Numba#
Python developers often face the decision of choosing between different implementations for their projects, especially when performance is a crucial factor. In this blog post, we delve into a comparative benchmarking study between PyPy, CPython, and Numba, focusing on calculating Pi value using a custom script.
Introduction#
The benchmarking setup involved running the same Python script on PyPy 7.3.16 with Python 3.10.14, CPython 3.12.3, and Numba 0.59.1 on a Macbook Pro M1 Max with 10 cores and 64GB of RAM.
Performance Results#
Note
Repository jpchauvel/pypy-test
PyPy Performance#
Pi value obtained: 3.1420208
Elapsed time: 2.2179 seconds
PyPy demonstrated decent performance in calculating Pi but was slightly slower compared to CPython and Numba.
CPython/Numba Performance#
Pi value obtained: 3.14182408
Elapsed time: 0.5820 seconds
CPython with Numba outperformed PyPy in both accuracy and speed, presenting the most efficient solution among the three test cases.
Final Thoughts#
While PyPy offers a balance between performance and Python compatibility, its execution time in this benchmark was marginally slower than CPython. For tasks prioritizing speed, CPython emerges as the preferred choice. Numba, leveraging JIT compilation, was noted as a potential contender for performance-critical applications.
In conclusion, the choice between PyPy, CPython, and Numba depends on the specific requirements of the project, with developers considering factors such as execution speed, accuracy, and compatibility with Python libraries.