Green Coding: Human-AI Collaboration for Efficient Source Code Optimization

In the field of software development, a significant shift is started with the integration of AI in coding practices. Large Language Models (LLMs) like GPT-4 are now being used to refine the efficiency of source code. This is not just a tale of enhanced performance but also of sustainable coding practices.

When I participated in the Green Coding team in the Hackathon at Deutsche Telekom in September 2023, I had my share of skepticism. My initial trials with using LLMs for coding were not very successful. I noticed that while LLMs could handle simple code snippets, they struggled with more complex tasks. Hallucinations were common, such as invoking non-existent functions or discrepancies between class definitions and their usage. These experiences made me realize the current limitations of AI in handling intricate coding challenges.

Upon arriving at the Hackathon, I decided to experiment with the Python library Pillow. To my surprise, ChatGPT proposed optimizing a 12-line function into a single line. This was astounding, and the modified code even ran faster. However, the initial code was wrong; it failed to cover all cases accurately.

After several hours of iterative collaboration with ChatGPT, we refined the code. The final outcome was extraordinary: the optimized source code executed approximately 38 times faster than the original. This result was beyond my expectations and demonstrated the potential of AI-assisted coding, albeit with the necessity for human oversight and iterative refinement.

Following the remarkable optimization of the Pillow library code, two immediate questions arose: First, was this significant improvement a one-time discovery or could it be replicated? Second, what were the underlying reasons for the dramatic speed increase in the source code?

To address the first question, I selected two more functions for testing: another from the Pillow library and one from the well-optimized NumPy library. In both cases, significant improvements were achieved: an average threefold increase in one and up to a 1.2 times boost in the other. These results suggested that the initial success was not an isolated event, but part of a broader potential for AI-driven optimization in coding. These enhancements, after being submitted upstream, were accepted by the maintainers. This approval means that the optimizations are now part of the mainstream distributions, allowing all users of these libraries to benefit from the improvements.

To tackle the second question, I closely examined the differences in the optimized source code. This scrutiny led to the identification of five distinct differences. Each difference was analyzed meticulously, comparing various implementations to understand their impact on performance.

For example, in Python, the code "a = 1; b = 2" performs the same operation as "a, b = 1, 2", yet the latter is up to 10% faster. Such insights were eye-opening, even with my 30 years of experience in professional source code development. These findings could potentially be the foundation of a "Green-Coding Ruleset," a guide to more efficient and environmentally friendly coding practices.

This journey, from the Hackathon at Deutsche Telekom to the surprising revelations in code optimization, opens a new chapter in coding practices for me. It's not just about enhancing performance; it's about rethinking the approach to sustainable and efficient programming. As I contemplate the creation of a Green-Coding Ruleset, I eagerly welcome insights and experiences from others in the field. Have you encountered similar revelations in your coding endeavors? How do you envision the future of green coding?

“LLM Interactive Optimization of Open Source Python Libraries -- Case Studies and Generalization

https://arxiv.org/abs/2312.14949

#GreenCoding #CodeOptimization #SustainableCoding #AIandHumanCollaboration