Writing C in Cython

2014-10-21 · Source: Explosion · Developer tools and consulting for AI, Machine Learning and NLP - Explosion.ai · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Advanced, quick

Summary

The author describes their two-year practice of writing C-like code directly within Cython, diverging from the common approach of "Cythonizing" existing Python code with type declarations. This method involves utilizing "raw" C structs and arrays, occasionally incorporating C++ vectors, and employing a custom-developed thin wrapper for malloc/free. By adopting this direct C-level programming style, their Cython implementations consistently achieve performance "almost always exactly as fast as C/C++". This is attributed to the code's fundamental nature as C/C++, while still providing the flexibility to seamlessly integrate Python functionalities when required. The approach underscores Cython's capability as a direct C/C++ interface rather than solely a Python optimization tool.

Key takeaway

For software engineers needing C/C++ performance within Python projects, consider writing core components directly in Cython using C structs and arrays. This approach bypasses traditional Python overhead, delivering native speeds while maintaining Python's ecosystem access. Evaluate if direct C-level coding in Cython offers a simpler path to performance than complex Python optimizations or separate C/C++ modules.

Key insights

Cython enables direct C/C++ coding for native performance while retaining Python interoperability.

Principles

• Cython can be a C/C++ compiler.
• Direct C structures yield C/C++ speed.

Method

Write Cython using "raw" C structs, arrays, and C++ vectors, optionally with custom memory management wrappers like malloc/free.

In practice

• Implement performance-critical sections in Cython.
• Use C structs for memory-efficient data.
• Wrap C malloc/free for custom control.

Topics

• Cython
• C Programming
• C++ Integration
• Python Performance
• Memory Management

Best for: NLP Engineer, Computer Vision Engineer, Software Engineer, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Explosion · Developer tools and consulting for AI, Machine Learning and NLP - Explosion.ai.