I work with another team at work that uses lots of message passing, C++ virtual methods in deeply nested class hierarchies, large DOMs and off-the-shelf C++ STL containers... on a STM32MP1, which has an in-order Cortex-A7 and a pittance of cache.
On the other hand, in my team I wrote my custom C++ STL containers that allocate inline, SAX-style processing libraries and an object pool with links that use index+generations like 2000s video games.
Mechanical sympathy feels like a lost art to people I demonstrate it to... and I had to demonstrate to the first team with benchmark numbers that I would not be the bottleneck between us by orders of magnitudes.
I work in the games industry with exceptionally skilled system programmers that can do that too. They're getting on in age, and there's fewer programmers in the younger generations that are interested in any of it. Technology is sadly increasingly homogenising around Unreal Engine 5
> Technology is sadly increasingly homogenising around Unreal Engine 5
I kinda get the commodification of games engines and Unreal. I am sure a lot of us saw the gaming revolution unfold, watching gaming companies one-up each other in graphics and interactivity. But once you get to the point of UE5, Unity, or Godot there is little need or value to make a game engine from scratch anymore.
Don't get me wrong, if you want to write an engine, go for it. But if you just want to make a game then an existing engine is good enough.
The people working for Unreal/Unity/Godot. Nobody writes search engines any more - except for Google et al. Nobody writes advertising systems - except for all the advertising businesses.
A popular refrain in gaming circles when Oblivion Remastered released was “it’s awesome if you have the hardware, but it’s Unreal so of course it runs like shit”. So it’s far from obvious that customer sentiment agrees with you. Having seen what Decima can do, I don’t think I agree that Unreal is the pinnacle of game tech either.
Not unique to gamedev; during the boom at the turn of the millennium the bar to entry to programming was dropped on the floor to fill demand.
The vast majority of programmers today don't even understand the basics of how-computer-work - not just microarchitecural considerations but even how to interface with the kernel efficiently / understand the performance considerations of their target platform.
Ask your average programmer how to say, simply perform I/O efficiently as possible on NT given some workload characteristics. One needs to first understand this before they can begin making concessions in favour of other demands (abstraction, flexibility, ect) and very few programmers can even get beyond the starting line.
Around the time Mantle/Vulkan-type APIs entered the scene, I ended up hearing a lot of "yes, these APIs much more complicated to use, but fewer people need to touch D3D/your graphics API anymore with these freely available engines." Was my impression correct?
I’ve had code review pushback against reorganizing data structures to be more cache-friendly on the grounds that it was “premature optimization” even though I pre-emotively justified the decision with benchmark results indicating that the performance difference was large and business relevant, and addressed a real performance bottleneck that I observed during testing.
This was on a team and at a time where Clean Code was very popular. I somewhat wonder if the problem is that, in the 2010s, many people became convinced that certain aesthetic standards were both objectively, provably correct, and more important than anything else.
I’m not who you were replying to, but there was a thread here a couple of years ago in which many people chimed in with examples. I added my two cents here, but the whole discussion is much more worth reading than my gripes: https://news.ycombinator.com/item?id=43166836
From what I understand, it is mostly the very rigid and prescriptive attitude which the average reader gets from his book, which, if you listen to him talk, was not his intent. He even published a new 2025 edition of “Clean Code” to try and fix this, by making the text less self-assured. So while he may or may not be responsible, he did not intend it.
Absolute nonsense, people claiming that on here are usually hiding their deep political biases and as liberal lefties dont like it when someone in their space calls a spade a spade, especially someone with a lot more reach and respect. Slightly different playbook than calling everything you disagree with racist. Yawn spells are spent.
That’s just pattern matching. Most people don’t do this level of prep so they assume you didn’t either. There’s usually no ill intent, and people are maybe a little embarrassed when you point it out, but t if you are tactful it’s fine.
Early in my career, I reduced the processing time in a web app to 1/30 by removing a few levels of abstraction and just using native python types. "Hey, I made it 30x faster!" Didn't receive the applause I expected, from my fellow dev.
There's not enough context for me (or other commenters) to really evaluate this, but every change has risk, and some projects aren't in a position to be accepting that risk.
A "real performance bottleneck that I observed during testing" is a little bit unclear whether it occurred outside of test conditions, or whether speeding up that particular bit improves anything anyone cares about.
Well, MCUs tend to have a buffer for execute in place from flash, but often they just have SRAM as main memory which can be accessed with a fixed latency and there is not much point in having a cache.
A lot of my poor opinion on developer opinions dates back to a couple projects where Product and Marketing were begging us for substantial performance improvements and the teams pushed back saying this was as fast as you could expect. I got 2x in the first six months on both projects, and delivered 25% per quarter on one for almost 2 years before I started to scrape the bottom of my barrel.
It’s predominantly a way to shut down thinking, which is why it’s so often misquoted. Knuth’s version is more nuanced. Theirs is not at all.
As a compromise I got really good at doing optimizations that also improved readability. Thinks like hoisting and better variable naming combined together can be mistaken for just code cleanup.
I also discovered that keyhole optimizations are more palatable. Which is to say, move the slow bit yo a separate function with a single responsibility. Then do the optimization in a separate commit. Then the other devs don’t have to look at your changes and if they turn out to have a bug, we can always revert the change later. They almost never do, but having the option seems to grease the wheels.
25% faster is 1.33x work done in the same time. What you want is 0.75^7 (almost 2 years) gives you 13.3% of the original time, which is 1/7.5. Only it was more like 25-30% so I rounded up.
Interesting how people interpret the word faster differently.
To you, 2x faster means 2x the work done in the same time, but 25% faster means 1.33x the work done in the same time.
I see this a lot - in software people interpret 50% faster as a 50% reduction in time taken.
If a car is 50% faster, I hope people would universally interpret that as e.g. 20 to 30 mph. But if I asked people how long would a 1 hour journey take if it were 50% faster, I bet a lot would say 30 mins, because it kind of seems like that's what I mean.
My general rule of thumb is that if you see you can 2x it easily, you can actually get somewhere around 10x with effort, since the presence of that easy 2x means nobody really spent any time on performance.
This sounds like being completely performance oblivious, like an extreme opposite of premature optimisation.
My rule of thumb is that as long as it doesn’t really complicate or convolute the code, write with performance in mind.
My expertise is in C# and sometimes this is as simple as using a hash set or dictionary instead of a plain list.
Other times it’s having a decent understanding of how EF (an ORM) actually works (the static typing benefits of them in C# makes ORMs very valuable when used appropriately) and the cost of round trips to any network host (including a DB).
It can go to extremes. Like 100x by going from a dynamic to static language then 100x more adjusting the data structures/algorithm…
Once I got very confused when exactly this kind of 10k x speed up I demonstrated for a problem introduced to me as a business bottleneck then got ignored (I wasn’t on this team, though, but it’s still hard to understand the mentality and incentives there).
I've known about a student, long ago in the '90s, who got to speed up a batch codebase in some bigcorp, as a summer job. The original did basically everything wrong, up to bubblesort. So student diligently cleaned up the mess and got a speedup from multiple hours to a few minutes.
Then he could not get an interview anywhere. After some backchannel communication it turned out he had to remove that experience from his CV, as all employers assumed he was just lying.
All types of architecture have advantages and disadvantages. Clean code might not be very cache-friendly, but it does a pretty good job of making sure you don't have to hunt in too many places to update things and It also makes it pretty simple to split things up so that you can write unit test for most everything.
This is not the right choice for everything, just as using Python is not the right choice for everything. The question to ask is: what are we trying to do in the first place? Then take it from there.
You did that by making sure the speed-up was business relevant. I'm mostly putting this out to force people to think about whether that also applies to their specific thing.
"Clean" code for modelling links between objects would be to store pointers within them. But that means dangling pointers when deleting objects, so you need to either update manually everywhere (easy to forget somewhere) or use shared/weak pointers (extra bookkeeping and allocations).
"Unclean" code for this is putting objects in a table, giving objects a generation (that increments whenever you create/delete one) and storing pairs of (index, generation) numbers everywhere you need a link. Link is live only if the generations matches and since only odd generations are live objects, default construction means no link. Lifted straight from 2000s video game tricks and with modern C++ it's basically all encapsulated away.
I've made that refactoring in a codebase, not because of speed but because of maintenance and memory fragmentation concerns. Sometimes, the clean solution really isn't the right one and it's really sad that compsci only ever teaches classical data structures, like we're still coding in the 1970s on computers with no caches that don't suffer from pointer chasing.
Something like this happened to me on a very high budget java project. I said I didn't want to make high memory pressure code that performed badly, the senior guy said the optimization was premature. It didn't matter in the end because they didn't adopt our solution.
If it has a pittance of cache, then mechanical sympathy might not mean cache-friendliness. Linked lists were mechanically sympathetic on uncached systems.
I thought up a RAM that paired every data word, with an address word, pointing to its "next" word. ListRAM.
Reduces usable RAM in half, but speed is speed.
The idea being the RAM buses could be designed to provide fast pipelines of chained data to the cache, despite arbitrarily scattering in RAM.
There is no way this could compete with a single batch read of actually adjacent data. But it could speed up "Random" accessed data, which often is not - in fact - actually random from the program's or the data structure's point of view, just not adjacent.
And it would do one thing better: Reading a null terminated string wouldn't over read. The null next-address would stop every small string read from becoming a page read.
Even more flexible, send little read-chain algorithms to RAM. A little address math chaining and branching on in-RAM values, on little distributed address-math cores (RAM embedded APU cores!), could avoid a lot slow turnarounds.
These kinds of ideas seem even better to me now, by orders of magnitude, than they did originally. RAM really needs help.
If you don't need insert/remove in middle, only at the end, there are more optimal data structures for list like RAOTS[1].
Btrees are O(log n) random access, O(log n) cons and use O(log n) excess space.
Unrolled linked lists are still O(n) random access because we ignore the constant factor of block size. They also use O(n) excess space for pointers, but have O(1) cons.
RAOTS are O(1) random access, O(1) amortized cons [O(√n) worse case], and use O(√n) excess space.
RAOTS is basically a tree of depth 1. The root is an "index block" of pointers to data blocks which grow in size proportional to the square root of the data length (similar to Hashed Array Trees, but doesn't require restructuring on each power of 2).
The square root is an approximation - the structure grows in "superblocks" where you add `~√n` blocks of size `~√n` The pattern is: [1x1, 1x2, 2x2, 2x4, 4x4, 4x8, 8x8, 8x16, 16x16, 16x32, ...] Even indexed superblocks increase the data block size, odd indexed superblocks increase the number of blocks per superblock. It doesn't use an actual `sqrt()` call uses but some bit tricks via `clz`.
A simpler trick, which has no single name but have been known as "segmented lists" among others, is to just have an index block of length log₂n where each index i points to a block of length 2ⁱ. These have O(1) random access, O(1) amortized cons [O(n) worst case] and waste O(n) space [with constant factor of 1/2]. The random access is very efficient as it's only two dereferences and a few bit tricks:
index_block[bit_width(idx)][idx - bit_floor(idx)]
This is also probably the most cache friendly of the lot.
Looking at the specs: 32 KiB of L1, 256 KiB of L2, in-order and partial dual-issue.
That's the least forgiving kind of chip when it comes to performance: fast enough to outpace main memory by two orders of magnitude, yet the slightest speed bump (branch misprediction, cache miss) stalls it without any ability to reorder around it.
I recently spent three weeks paring down a piece of software until it could run entirely in the L1 cache of the processor it was running on. The performance was - to put it mildly - impressive. And this was 1970's technology running on a modern CPU. This stuff can do miracles if you use it right.
Someone asked me why Zig programmers seem to genuinely enjoy passing the allocator around as an argument. I didn’t have a good answer at the time, but I think I do now, thanks to your comment - feeling the “weight” of allocations is an example of mechanical sympathy, and having to pass around and use an allocator explicitly makes your code-effort more closely resemble the chip-effort it is asking for.
Heap allocations aren't really the culprit of the waste that the root comment is talking about. Indirection in general is, and passing around an allocator doesn't guard you against that. Virtual dispatch occurs even for objects on the stack.
The problem is that it's basically a book and deep-dive. You don't get much out of it by skimming. I doubt that CPU architectures have changed enough to matter. I admit I'm not up-to-date, but CPUs are not the web ecosystem. What has changed is that nobody has the attention span to read something like this any more.
And the title was wrong when it came out - not every programmer needs to know those details. Many programmers work on some CRUD app with frontend and REST API and should learn about CREATE INDEX or start to measure performance in the first place. And some performance work has moved to GPUs instead of CPUs (not always for a good reason IMO).
I didn't get the sense that this was the end-all, just a relatively simple way to show how much impact cache-friendly data structures can have for somewhat little effort over the naive solution and give readers a sense for how to think through the problem.
Modern CPUs have hundreds of megabytes of cache. That's more than the entire hard disk space required by Windows in the 1990s.
You could install an entire Windows system, emulated in cache of a modern CPU, including the hard disk, and modern computers are still slow, partly because the CPU does nothing while waiting for RAM to fetch data, and partly because decades of people prioritizing abstractions over efficient coding have made software totally inefficient.
Software has gotten vastly more complex over the intervening decades, and unfortunately it has historically been beyond human capacity and/or institutional time/money availability to keep everything running super efficiently.
The good news is that with LLMs, we can make software efficient again, much more cheaply!
I go to Hacker News to hear people say that LLMs will automagically make software clean and efficient again.
Then I go back to Cursor’s agent chat window and spend another day endlessly struggling to keep LLMs from inserting needless complexity and N+1 queries into the code at a faster rate than mere humans could ever hope to achieve.
I remember have a very long argument with a team I worked on, who were mostly convinced our service couldn't handle requests any faster because the CPUs were already at 100%. In the end we tripled the requests-per-second by cleaning up the service's memory access patterns...
This is the kind of thing that needs a “things every programmer should know about…” blog post.
I would estimate that a single digit percentage of my colleagues are aware that, according to `top`, sitting around picking at your fingernails while you wait on the memory controller to run across town and back counts as working your ass off.
Hi, i just have 2 questions. Are your machines priorizing Space over Processing ? In terms to save a few bytes, cos "Rendering" (for example) is done by the clients machine as an example? Or have i missunderstood something? I am asking, cos the first answer in the posting seem to miss: "commenting the code good enough than anyone may 'understood, what heck this typed line may be for...'" just asking...thanks
79 comments
[ 0.12 ms ] story [ 51.7 ms ] threadOn the other hand, in my team I wrote my custom C++ STL containers that allocate inline, SAX-style processing libraries and an object pool with links that use index+generations like 2000s video games.
Mechanical sympathy feels like a lost art to people I demonstrate it to... and I had to demonstrate to the first team with benchmark numbers that I would not be the bottleneck between us by orders of magnitudes.
I kinda get the commodification of games engines and Unreal. I am sure a lot of us saw the gaming revolution unfold, watching gaming companies one-up each other in graphics and interactivity. But once you get to the point of UE5, Unity, or Godot there is little need or value to make a game engine from scratch anymore.
Don't get me wrong, if you want to write an engine, go for it. But if you just want to make a game then an existing engine is good enough.
So your argument is that game engines will just decay.
The vast majority of programmers today don't even understand the basics of how-computer-work - not just microarchitecural considerations but even how to interface with the kernel efficiently / understand the performance considerations of their target platform.
Ask your average programmer how to say, simply perform I/O efficiently as possible on NT given some workload characteristics. One needs to first understand this before they can begin making concessions in favour of other demands (abstraction, flexibility, ect) and very few programmers can even get beyond the starting line.
This was on a team and at a time where Clean Code was very popular. I somewhat wonder if the problem is that, in the 2010s, many people became convinced that certain aesthetic standards were both objectively, provably correct, and more important than anything else.
Calling everyone who downvotes you "incels" is very much against site rules. You've certainly been here long enough to know that. Knock it off.
A "real performance bottleneck that I observed during testing" is a little bit unclear whether it occurred outside of test conditions, or whether speeding up that particular bit improves anything anyone cares about.
I think cache friendly code also tends to be reason about friendly and debugger friendly. None of that is remotely premature optimization.
It’s predominantly a way to shut down thinking, which is why it’s so often misquoted. Knuth’s version is more nuanced. Theirs is not at all.
As a compromise I got really good at doing optimizations that also improved readability. Thinks like hoisting and better variable naming combined together can be mistaken for just code cleanup.
I also discovered that keyhole optimizations are more palatable. Which is to say, move the slow bit yo a separate function with a single responsibility. Then do the optimization in a separate commit. Then the other devs don’t have to look at your changes and if they turn out to have a bug, we can always revert the change later. They almost never do, but having the option seems to grease the wheels.
To you, 2x faster means 2x the work done in the same time, but 25% faster means 1.33x the work done in the same time.
I see this a lot - in software people interpret 50% faster as a 50% reduction in time taken.
If a car is 50% faster, I hope people would universally interpret that as e.g. 20 to 30 mph. But if I asked people how long would a 1 hour journey take if it were 50% faster, I bet a lot would say 30 mins, because it kind of seems like that's what I mean.
My rule of thumb is that as long as it doesn’t really complicate or convolute the code, write with performance in mind.
My expertise is in C# and sometimes this is as simple as using a hash set or dictionary instead of a plain list.
Other times it’s having a decent understanding of how EF (an ORM) actually works (the static typing benefits of them in C# makes ORMs very valuable when used appropriately) and the cost of round trips to any network host (including a DB).
Once I got very confused when exactly this kind of 10k x speed up I demonstrated for a problem introduced to me as a business bottleneck then got ignored (I wasn’t on this team, though, but it’s still hard to understand the mentality and incentives there).
Then he could not get an interview anywhere. After some backchannel communication it turned out he had to remove that experience from his CV, as all employers assumed he was just lying.
I see what you did there
This is not the right choice for everything, just as using Python is not the right choice for everything. The question to ask is: what are we trying to do in the first place? Then take it from there.
You did that by making sure the speed-up was business relevant. I'm mostly putting this out to force people to think about whether that also applies to their specific thing.
"Unclean" code for this is putting objects in a table, giving objects a generation (that increments whenever you create/delete one) and storing pairs of (index, generation) numbers everywhere you need a link. Link is live only if the generations matches and since only odd generations are live objects, default construction means no link. Lifted straight from 2000s video game tricks and with modern C++ it's basically all encapsulated away.
I've made that refactoring in a codebase, not because of speed but because of maintenance and memory fragmentation concerns. Sometimes, the clean solution really isn't the right one and it's really sad that compsci only ever teaches classical data structures, like we're still coding in the 1970s on computers with no caches that don't suffer from pointer chasing.
Reduces usable RAM in half, but speed is speed.
The idea being the RAM buses could be designed to provide fast pipelines of chained data to the cache, despite arbitrarily scattering in RAM.
There is no way this could compete with a single batch read of actually adjacent data. But it could speed up "Random" accessed data, which often is not - in fact - actually random from the program's or the data structure's point of view, just not adjacent.
And it would do one thing better: Reading a null terminated string wouldn't over read. The null next-address would stop every small string read from becoming a page read.
Even more flexible, send little read-chain algorithms to RAM. A little address math chaining and branching on in-RAM values, on little distributed address-math cores (RAM embedded APU cores!), could avoid a lot slow turnarounds.
These kinds of ideas seem even better to me now, by orders of magnitude, than they did originally. RAM really needs help.
Btrees are O(log n) random access, O(log n) cons and use O(log n) excess space.
Unrolled linked lists are still O(n) random access because we ignore the constant factor of block size. They also use O(n) excess space for pointers, but have O(1) cons.
RAOTS are O(1) random access, O(1) amortized cons [O(√n) worse case], and use O(√n) excess space.
RAOTS is basically a tree of depth 1. The root is an "index block" of pointers to data blocks which grow in size proportional to the square root of the data length (similar to Hashed Array Trees, but doesn't require restructuring on each power of 2).
The square root is an approximation - the structure grows in "superblocks" where you add `~√n` blocks of size `~√n` The pattern is: [1x1, 1x2, 2x2, 2x4, 4x4, 4x8, 8x8, 8x16, 16x16, 16x32, ...] Even indexed superblocks increase the data block size, odd indexed superblocks increase the number of blocks per superblock. It doesn't use an actual `sqrt()` call uses but some bit tricks via `clz`.
[1]:https://cs.uwaterloo.ca/research/tr/1999/09/CS-99-09.pdf
A simpler trick, which has no single name but have been known as "segmented lists" among others, is to just have an index block of length log₂n where each index i points to a block of length 2ⁱ. These have O(1) random access, O(1) amortized cons [O(n) worst case] and waste O(n) space [with constant factor of 1/2]. The random access is very efficient as it's only two dereferences and a few bit tricks:
This is also probably the most cache friendly of the lot.That's the least forgiving kind of chip when it comes to performance: fast enough to outpace main memory by two orders of magnitude, yet the slightest speed bump (branch misprediction, cache miss) stalls it without any ability to reorder around it.
Programs should express the solution, not the problem.
cos...everyone seems still 'unreal busy'...or mentioning doom (3) ^^
Has anyone rewritten this for 2026?
The problem is that it's basically a book and deep-dive. You don't get much out of it by skimming. I doubt that CPU architectures have changed enough to matter. I admit I'm not up-to-date, but CPUs are not the web ecosystem. What has changed is that nobody has the attention span to read something like this any more.
And the title was wrong when it came out - not every programmer needs to know those details. Many programmers work on some CRUD app with frontend and REST API and should learn about CREATE INDEX or start to measure performance in the first place. And some performance work has moved to GPUs instead of CPUs (not always for a good reason IMO).
Still not orders of magnitude better.
I like flamegraphs
I know folks use perf to help generate them
But in the future, now that Llm is here, get and agent to check assembly, PMUs and also see what you’re doing in assembly.
My 2c and happy coding to everyone :)
Microcosm of Pivot itself I guess. I just don't get why this project exists when DuckDB and Clickhouse both exist.
I don't think they were claiming it was novel. Seemed just like an article explaining a performance optimization
You could install an entire Windows system, emulated in cache of a modern CPU, including the hard disk, and modern computers are still slow, partly because the CPU does nothing while waiting for RAM to fetch data, and partly because decades of people prioritizing abstractions over efficient coding have made software totally inefficient.
The good news is that with LLMs, we can make software efficient again, much more cheaply!
Then I go back to Cursor’s agent chat window and spend another day endlessly struggling to keep LLMs from inserting needless complexity and N+1 queries into the code at a faster rate than mere humans could ever hope to achieve.
I would estimate that a single digit percentage of my colleagues are aware that, according to `top`, sitting around picking at your fingernails while you wait on the memory controller to run across town and back counts as working your ass off.