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DeepMind has developed a system that generates efficient sorting algorithms optimized for small datasets, specifically for sorting between 2 to 8 elements. This advancement allows for the creation of library sorts that can replace generic sorting functions with highly specialized ones, such as "sort_7(data, comp(&a, &b));". The discussion highlights the historical context of sorting algorithms, emphasizing the shift from manual optimization at the assembly level to AI-generated solutions. The consensus is that while AI-generated sorts may offer marginal improvements, the practical benefits depend on the specific use case and the number of elements being sorted.
PREREQUISITESSoftware engineers, AI researchers, and performance optimization specialists interested in advanced sorting techniques and their practical applications in programming.
Some historical perspective:Tom.G said:
How often will the number of elements being sorted be known at compile time?.Scott said:if you are sorting exactly 7 elements
I am writing assembly code at the moment because I demand time critical control of external signals while all interrupts are disabled, and exceptions impossible..Scott said:Coding at the Assembly level was once a common optimization practice.
Sometimes it is very, very stable. The number of hours in a day hasn't changed since the invention of computers. The number of states in the US has been stable for more than half a century. Even the number of schools in the Big Ten is constant on the time scale of compilimg.PeterDonis said:How often will the number of elements being sorted be known at compile time?
An easy example that answers your question, but not the general point:PeterDonis said:How often will the number of elements being sorted be known at compile time?
And sometimes it does.Vanadium 50 said:It does no good to spend an hour trying to speed up a sort by 30 minutes. It may not even make sense to hire a team of programmers to speed up a sort by 30 minutes compared to just getting a faster computer.