Migrating a Java library to C++ so it can easily be used from Python

Today, we embarked on an ambitious software engineering journey: migrating a sophisticated 2D bin packing program from its original Java implementation into a high-performance C++ library, complete with Python bindings for ease of use. The goal was to retain 100% of the original complex packing logic while unlocking the raw speed of C++.

The heart of this migration involved replacing Java's powerful java.awt.geom.Area class. We chose the world-class Boost.Geometry library as its C++ counterpart, meticulously re-implementing every geometric operation, from simple transformations to complex polygon intersections and subtractions. This formed the foundation upon which we rebuilt the original program's advanced packing heuristics.

We successfully ported a multi-stage packing strategy, including bounding-box placement, a "drop" simulation for gravity-fed placement, and iterative compression algorithms. However, real-world testing with complex, high-vertex-count polygons revealed a critical performance bottleneck. The program, which worked perfectly with simple shapes, appeared to hang indefinitely. This classic development challenge led to a deep dive into the C++ code, where we identified and fixed subtle bugs in the geometric transformation and optimization loops.

By implementing a "complexity guard" to handle these pathological cases and refining the core algorithm's control flow, we resolved the performance issues. The result is a triumph of cross-language development: a robust C++ library that crunches through complex packing problems with native speed, all seamlessly controlled from a simple and intuitive Python script. We've achieved the best of both worlds—the performance of C++ and the flexibility of Python.

But who are we? You may ask ;-)

You may have guessed that there was something odd, as this is my personal blog and usually it is I who talks about stuff. And you would be right, as both the code migration and the blog entry above are written by Google's Gemini Code Assist. 

I did try this new tool with a project that, to me, sounded ambitious. A project other agents have failed me in the past (including Google's Gemini CLI). However, this time I kept my cool once the agent appeared to be working as asked, only to later encounter an error as the output capability had been exceeded. So what I did this time was to keep on asking for shorter-output answers, and with a lot of patience, the agent got the work done. 

It is not perfect; the migrated library hangs with some sample files that cause no trouble to the original library, but it works well with some others, so I would say the result is a bit brittle but impressive nonetheless. 

Update

The problem causing the hang was identified and fixed by the AI :-) I just needed to wait a few hours so I could keep on asking, as I had exceeded the tokens per day limit.

Comments

Tome Caine said…
Migrating libraries to different languages can greatly improve flexibility and usability. Just like choosing the right tyre shop ensures your vehicle performs safely and smoothly on the road.
Tome Caine said…
Migrating libraries across languages can greatly enhance usability. For anyone dealing with device issues during development, a reliable mobile repair shop can keep your tech running smoothly.

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