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Comment by fragmede | Hacker News Reader

Anything at scale. Anything that uses a database. Anything that uses anything smarter than bubblesort to organize results. Anything that uses a tree (or a trie) internally to represent the data and make some sort of order from it. CS isn't just about "hey this thing is slower than this thing", it's also about making things possible in the first place. If you've used Google (or Facebook or Microsoft or Netflix), they're only possible due to deep computer science work. Which videos gets chosen to be stored on the edge cache? Computer science algorithm.

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I'm not sure how true this is. A lot of "algorithms" can be tested by using them or by running simulations for reasonable input ranges.

Well first off, i think there is an argument that creating & simulating algorithms is CS stuff anyways.

Second its often (not always) a lot more work to test scalability with benchmarks than it is to theoretically analyze.

Especially because people are often worried about both average case and worst case. The input that takes down your site is not the one you expect, and simulating realistic load is difficult.

It can also be pretty hard to compare benchmarks. You probably dont want to test every algorithm known to man yourself, but two separate benchmarks on diff hardware and diff conditions cannot be reasonably compared. More abstract analysis can (to an extent anyways)

Don't a lot of these algorithms come from TCS researchers themselves? They may publish about complexity but if that gives some people a better understanding of algorithms and leads to better ones being invented, it's a win.

You wouldn’t come up with the high-permorming algorithms without understanding why they would be high-performing in the first place.

Well, this did happen in machine learning. There was a theoretical argument that neural networks "should" terribly overfit according to statistical learning theory, since they have very large VC dimension, but they don't. Practice did race way ahead of theory in AI. Another example is the Chinchilla scaling law, which was discovered empirically instead of being derived from first principles.

That's different from complexity though. We actually understand why some algorithms have better complexity and results than others, while we mostly have no idea how intelligence works or even how to define it.

Now that I've had a chance to think about it, it's not even "at scale". Your smartphone, which makes liberal use of sqlite databases uses a tree data structure to organize and index the information in the db files. When you turn on the screen to your phone or laptop, and see a bunch of icons laid out, it's thanks to computer science algorithm work that you're not waiting minutes or days for all the icons to get loaded from storage organized with a filesystem smarter than FAT, arranged how you like them, and displayed to a screen with millions of pixels.

to a screen that

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