Comment by cogman10 | Hacker News Reader

Comment by cogman10 | Hacker News Reader

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> but those tools can only tell you what is happening now, not what to do to improve it.

They tell you what's happening now, but they also tell you if what you've done has had a positive impact.

> If you're judging things by big-O notation, then that's a different level above the real low-level tweaking (imho of course).

I completely agree. My point isn't that Big-Oh is low level, but rather that Big-Oh is often enough for most programming problems. Even in some of your examples like a compiler, game engine, or collection library, the big oh matters and if it's wrong, that can be a lot more important than shaving 0.1% on writing a function in a low level fashion. Big-Oh is gotten wrong a surprising amount of time even though it's 101 level stuff.

> At the smallest level there's a lot of theory building and experimentation as you try out different approaches, which is where the instinct and intuition starts to build. I never see any of that in discussions about performance engineering.

Oh because people get these things wrong all the time. That's why performance engineering stresses that you test, test, test and know what your testing and know why your testing could be wrong or corrupted. You should not trust your intuition because things change and it isn't always correct.

A good example of how easy it is to get measuring wrong. Imagine you start tweaking a function and you measure that your application became 1% faster. Was it the work you did on that function? Surprisingly, not always (at least not directly). Sometimes, it's the case that when you work on a function you re-align other functions as the machine code has to go it memory. It's possible that an undiscovered misaligned while loops was actually causing a large portion of your performance spill and by tweaking the function here, you aligned the while loop (or maybe a few of them). And, importantly, a new change somewhere else might re-unalign that same while loop.

You walk away thinking you've learn some low level lesson when in actuality your bit twiddling simply accidentally fixed something somewhere else.

This is why measuring is so important but also good measuring is even more important.

> I completely agree. My point isn't that Big-Oh is low level, but rather that Big-Oh is often enough for most programming problems.

For what I work on, the hidden constant is often more important than big O. For example, a hash map has better complexity than just searching through a vector. But if the vector is small enough it will best the hash nap for actual time. Just plain searching until you find the element will even beat binary search on a sorted vector for small enough vectors. The reasons are complex, to do with cache, prefetch, branch prediction and also just how many instructions your tight inner loop has. (And the specific reasons will vary between desktop class CPUs and microcontrollers. But both exhibit this pattern.)

You could argue that at that point why bother optimising at all (there aren't a lot of elements in the collection after all). But there are two distinct cases I have come across over the years where it still matters (and for what I work with, they represent the common cases):

* You need to look up in a small collection a lot (either lots of lookups into a few small collections or a few lookups each into lots of different small collections, I have seen both cases).

* Hard realtime code where predictable latency matters. Hashmap has a bad worst case, binary trees and binary searching has badly predictable memory access patterns. And in this case the collections are usually small anyway (there are only so many actuators and sensors your equipment has, and/or the embedded microcontroller doesn't have a lot of memory anyway).

> But if the vector is small enough it will best the hash nap for actual time.

This will all depend on the size and type of object stored in a vector.

If you have a relatively small and flat struct that you are storing, then sure that will likely win. But if you are working with a collection of pointers, then the hash map will (almost) always win.

> Hard realtime code where predictable latency matters. Hashmap has a bad worst case

The hash map worst case is a linear search. It will be a lookup + the search. This also depends on the implementation. You could, for example, use a robinhood hash map which trades insertion times for lookup times.

A bad hashmap implementation will store collisions in linked lists. A better one will try and put them in a b-tree. An even better one will use a vector for collisions. And the most cache friendly version stores everything in the table and does probing on collisions.

There are specific cases where vectors are better, but those are the exception and not the rule in my experience.

It all depends. I was thinking mainly of small keys that are at most a machine word. But yes I conceded that for large keys (extremely unusual for what I do, thus I didn't think of that case before) a hashmap wins.

Also, while on a desktop class CPU any pointer chasing you do tends to dominate, that is not the case on small and medium microcontrollers (which I work with a lot). They have very short pipelines and their memory is all internal SRAM most of the time. Here classic cycle counting is still king, so you need to ask yourself if it is quicker to compare the key than to calculate the hash and compare hashes.

Code size in general matters a lot on embedded. Even a simple hashmap will always have more code than a simple vector. And a state of the art implementation with probing and tombstones (such as swisstable in C++ and hashbrown in Rust) will be way too large to be usable.

> Even in some of your examples like a compiler, game engine, or collection library, the big oh matters

Sure, but for example - today - I am literally building and optimising high-performance collections for my open-source library. Big-O is irrelevant, because I have built pretty much all of the fundamental collection types, what I care about is lower than that: what happens when enumerating any one of those collection types. Big-O tells you the scale of the problem, but not the per-element cost, which is still important when you're building core data-structures.

I am concerned about cache-friendly memory-layouts, how to do collection compositions without unnecessary memory allocations, keeping enough guards in place to make the types safe whilst removing as many branches as possible, reducing memory copying as much as possible, and catching stupid shit the compiler or JIT does and try to work around it. Literally what happens per-instruction, per-iteration, not how to pick a big-O based data-structure: trying to make all data-structures as fast as possible.

Anyway, we seem to be talking past each other. You're talking about the basics, I'm talking about the original source of this thread which was that (apparently) LLMs are surprisingly bad at optimisation. Which, I am trying to highlight becomes almost voodoo at a low-enough level and that highly-optimised code looks progressively more strange and opaque (in the hunt for a few nanoseconds here and there), which for an LLM wouldn't look statistically significant. I think the basics of data-structure choice should be easily within the realms of an LLM's current capabilities.

> I think the basics of data-structure choice should be easily within the realms of an LLM's current capabilities.

Surprisingly, it isn't. I catch the output of LLMs breaking these rules all the time. Just as it is pretty common in general programmer code.

The greatest sin I often find isn't necessarily Big-Oh related but rather multiple traversal problems. Much like regular programmers, LLMs love to do multiple passes over the same list to extract data. For example

    let cats = pets.filter((p)->p.isCat());
    let dogs = pets.filter((p)->p.isDog());

A lot of programmers are oblivious to that sort of performance issue. It comes up a lot.

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