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In another case, ByteDance was able to optimize HTTP network latency on an NGINX server by optimizing the tuning of 16 kernel sysctl parameters. In its best scenario, the ML tuning gave the NGNIX network performance a 12% boost over expert manual tuning. Again, that’s a significant improvement.
Looks very promising
I’m sure that AI will be used to optimize software, hardware and configurations like this somewhere in the future.
Near future really. Supposedly the algorithms for chatgpt4 are going to be many levels above chatgpt3. I would imagine coding algorithms will follow suit as openai keeps updating their stuff.
I don’t want to remove my post, but I just learned about the openai situation, and I have low expectations for chatgpt4 now. Microsoft on the other hand may have some good stuff coming out. Hopefully it will be available to the public like chatgpt3, but my expectations on that are low considering the monthly subscription model Microsoft has adopted for the office suite.
True, but algorithms they used in this article are just a “level” higher than primitive statistics methods and even that is not mainstream in kernel parameter (and similar things) optimisation. So it might be some time before we see more advanced methods used for this stuff.
