Which is the fastest implementation of Python

The “fastest” implementation of Python depends on your definition of speed and the specific use case. Below are the main Python implementations and how they differ in performance:

1. CPython (Standard Implementation)

• What it is: Standard implementation of Python in C
• Performance:
  • CPython is the reference implementation, and the goal is compatibility with the language Python.
  • This makes it relatively slow compared to other implementations. The reason for this is because CPython doesn’t have Just-In-Time (JIT) compilation.
  • Use case: general purpose, majority of Python libraries work.
• Limitations:
  • Slow performance for computationally intense applications.
  • It’s not able to do true multi-threading on CPU-bound operations because of GIL.

2. PyPy

• What is it: A Just-In-Time compiler version of an alternative implementation.
• Performance:
  • Much faster for long-running computations and pure Python code, as the code is JIT-compiled.
  • Several times faster than CPython for computationally intensive tasks.
  • Suitable for: Applications requiring raw speed and don’t depend too much on native C extensions.
• Limitations:
  • Support for some C extensions is less robust, particularly for older versions of NumPy.
  • It has slightly higher memory usage because of the JIT.

3. Cython

• What it is: A language similar to Python, allowing you to compile Python code into C for performance.
• Performance:
  • It dramatically accelerates code by compiling it into native machine code.
  • Very effective if used to write computationally intensive loops or mathematical operations.
  • Best for: Performance-critical parts of the code that can be optimized with explicit type declarations.
• Limitations:
  • Additional effort required to write and maintain.
  • You have to use type annotations and sometimes rewrite your Python code to get the best performance.

4. Jython

• What is it: A Python implementation for the Java Virtual Machine (JVM).
• Performance:
  • It seamlessly integrates with Java libraries and applications.
  • Performance depends on JVM optimizations.
  • Best for: Interoperability with Java ecosystems.
• Limitations:
  • Slower than CPython in many cases because it lacks a GIL and supports fewer native C extensions.

5. IronPython

• What it is: Implementation of Python for the .NET framework.
• Performance:
  • Similar in intent to Jython but the libraries are those of .NET.
  • Best suited for: .NET interoperability.
• Limitations:
  • Does not support libraries as well as CPython.

6. MicroPython and CircuitPython

• What it is: Python implementations for microcontrollers and constrained devices.
• Performance:
  • Optimized for low memory and processing power devices.
  • Best for: Embedded systems and IoT applications.
• Limitations:
  • Limited feature set compared to CPython.
  • Not focused on execution speed.

Performance Comparison

Recommendations

1. General use: CPython.
2. High Performance: PyPy for pure Python code or Cython for targeted optimizations.
3. Java/.NET Ecosystems: Jython or IronPython.
4. IoT/Embedded Systems: MicroPython or CircuitPython.

For computationally intensive workloads, PyPy is often the fastest without requiring major code changes, while Cython is best for critical sections that can be rewritten with performance in mind.