LLMs and Code Optimization

The author recounts the challenge of optimizing a code that finds the difference between the smallest and largest numbers whose digits sum up to 30. Using a large language model (LLM), different approaches like numpy, parallelization, and transitioning to Rust were explored. Surprisingly, a simple, missed optimization was identified by manually checking the usefulness of numbers before computing digit sum. Introducing parallelization intelligently led to a 3.6x speedup, revealing the tradeoff between work and latency in parallelization. Notably, the LLM struggled to suggest improvements without specific instructions, missing the opportunity to switch to a faster rand implementation. The post highlights the complexities of optimizing code efficiently and the limitations of LLMs in algorithmic optimization.

https://wiredream.com/llm-optimizing-digit-diff/

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