Thread

1. metalspot · 1790094725 · focus · HN ↗

   I have done a fair amount of low level performance optimization with Opus 5 and its reasoning is still very poor. Like why is CRC so slow and going through loops until I ask it if is using hardware instructions and it tells me it is using its own hand coded implementation poor. Reasoning about l1/l2/l3 cache hit ratios and their implications basically throwing darts at the wall, in the wrong room. If you give it a benchmark feedback loop then it might get there eventually but still massive alpha for low level systems engineers who instinctively know how this stuff works and can now automate 99% of the grind.

   1. bee_rider · 1790098891 · focus · HN ↗

      > Reasoning about l1/l2/l3 cache hit ratios and their implications basically throwing darts at the wall, in the wrong room. If you give it a benchmark feedback loop then it might get there eventually but still massive alpha for low level systems engineers who instinctively know how this stuff works and can now automate 99% of the grind.

      I suspect a lot of the training set for this sort of thing is people online speculating about cache performance incorrectly.

      1. louthy · 1790103979 · focus · HN ↗

         Speaking as somebody who is a performance geek, my knowledge came from relentless experimenting over the years (starting in the 8 bit era). Beyond the basics, I haven’t seen much on high-performance engineering online. To learn, you need to do the hard yards and I think performance tweaking becomes almost instinctive rather than something driven by a hard set of rules.

         At a low enough level, every performance tweak becomes unique and bespoke.

         Of course, you could find people online talking about how to write high-performance code, but beyond a few basic techniques, their advice may not work for you — nobody can write a generalist article about performance engineering that will definitely solve the problem you have right now.

         Arguably, there are fewer patterns for an LLM to infer as highly optimised code tends to become more and more opaque in the search for a nanosecond here or there.

         1. cogman10 · 1790104976 · focus · HN ↗

            I don't disagree, but IMO, a lot of code doesn't get to the point where those very low level techniques drive performance. Like, yes, if you are doing some heavy floating point math then that's where you end up needing it. However, in a lot of code finding hot paths and often simply switching out a O(n^2) for an O(n log n) or faster.

            Getting and using tools to find hotpaths is generally the most important performance tweaking skill.

            1. louthy · 1790106114 · focus · HN ↗

               I mean, sure, but it really does depend on what you're doing. If you're working on a library with collection-types and you want to make each iteration as fast as possible, then roll up your sleeves. If you're writing a compiler and you want your language's source-code to finish compiling this week, roll up the sleeves. If you're working on a game-engine and you want to draw more than everyone else, roll up the sleeves...

               There are plenty of real-world reasons why you'd want to get knee deep in this stuff. I wasn't suggesting not using tools (I've literally spent the day buried in JetBrains' memory and tracing tools!), but those tools can only tell you what is happening now, not what to do to improve it.

               Profiling is, of course, essential. But performance tweaking can be quite a laborious process: if you're judging things by big-O notation, then that's a different level above the real low-level tweaking (imho of course). Picking the correct data-structures is all in the 101 of performance engineering. That's in the literature. But it's all too basic and simplistic. Most performance minded engineers wouldn't need a profiling tool to know which data-structure to use.

               At the smallest level there's a lot of mental 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.

               1. cogman10 · 1790107777 · focus · HN ↗

                  > 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.

                  1. louthy · 1790108945 · focus · HN ↗

                     > 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.

                     1. cogman10 · 1790109393 · focus · HN ↗

                        > 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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