abmfy

Thanks for commenting! Actually in this case, "the work being done" can be really fast because it can be done asynchronously. For context, here’s how this translates in a real-world application.

The original algorithm was provided by DeepSeek, and our optimized implementation achieves a 92× speedup over it. The 5x number is comparing with another baseline that is undisclosed yet.

When integrating EPLB into vLLM, I discovered—somewhat unexpectedly—that the open-source algorithm consumes nearly half of the total time of a rearrangement step, with the remaining time spent transferring weights across GPUs. To address this, I applied OpenEvolve to the algorithm, setting the primary objective to improve speed while maintaining the same balance factor. It performed remarkably well. With additional optimizations on the weight transferring, the overall overhead has now become almost negligible.

I spent 2~3 hours setting up, most of the time was spent on writing the evaluator

Actually I think the evaluator will be the most important part for the whole pipeline to work

It's directly usable, since it need to pass the evaluator first; also it contains clear comments about the intent

Thanks! In realistic workloads, the differences won’t be orders of magnitude.

I agree that this is a fairly simple problem. Experienced engineers—or anyone who has faced similar challenges—can quickly come up with such solutions. The key point, however, is that others might get stuck in their research simply because they don’t realize these quick solutions exist (“I don’t know what I don’t know”). AI helps bridge that gap by making expert-level knowledge accessible to every researcher, allowing them to focus more on exploring the truly unknown parts.