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Python Implementations¶
Different Python implementations may have varying performance characteristics for the same operations.
Major Implementations¶
- CPython - Reference implementation (C-based)
- PyPy - JIT compiler (Python-based, fastest for many workloads)
- Jython - Java platform (Java interoperability)
- IronPython - .NET platform (.NET interoperability)
Quick Comparison¶
| Implementation | Engine | Performance | Use Case |
|---|---|---|---|
| CPython | Bytecode interpreter | Good | General purpose, standard |
| PyPy | JIT compiler | Excellent | Long-running, CPU-bound tasks |
| Jython | Java-based | Good | Java ecosystem integration |
| IronPython | .NET-based | Good | .NET ecosystem integration |
Complexity Guarantees¶
While standard operations have similar complexity across implementations, there are differences:
- CPython: Baseline, well-tested complexity guarantees
- PyPy: Same algorithmic complexity, may be faster in practice
- Jython: Uses Java collections, may have different characteristics
- IronPython: Uses .NET types, similar to CPython
Key Differences¶
Optimization Strategies¶
- CPython: Static optimizations, caching, string interning
- PyPy: Dynamic JIT optimization, guard-based specialization
- Jython: Java native performance, GC differences
- IronPython: .NET runtime benefits, library integration
Memory Management¶
- CPython: Reference counting + generational GC
- PyPy: Generational GC (no reference counting overhead)
- Jython: Java GC (pause times may vary)
- IronPython: .NET GC (similar to Jython)
Detailed Guides¶
See individual implementation pages for:
- Specific optimizations
- Performance characteristics
- Known differences in complexity
- When to use each implementation