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... on a STM32H5 with less than a megabyte of RAM.
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’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.
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 (that maths to around 8x) 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. Things 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.
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).
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.
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 if you are tactful it’s fine.
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.
> I think cache friendly code also tends to be reason about friendly and debugger friendly.
Not sure what examples you have in mind, but the very first things I think of when I hear cache friendly code are cache-blocking and cache-oblivious algorithms, which are quite the opposite of that. See also B-trees vs. binary search trees, etc.
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.
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
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.
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.
I’ll admit I lost the plot a long time ago, but there’s a part of me that wonders if the main purpose behind the game engine and graphics card treadmill is to line the pockets of people who sell games engines and graphics cards. My subjective sense of the connection between technology and enjoyment as a player started to degrade about a decade back.
I think it was just studios competing with each other in the 2010s to see who could make the biggest most ambitious game worlds. Leading to ballooning dev costs and then a feedback loop of higher production costs leading to safer design decisions.
There was also a heavy emphasis on games being "cinematic" which necessitates photorealistic rendering.
AAA games have been pretty artless for a while now baring a couple exceptions.
It’s especially the “cinematic” bit that got me. It’s technically impressive, but isn’t as visually interesting as what lower budget games have been doing with less polygons and more inspiration.
I'd it's more a convenient outcome of maximizing profits than premeditated. Not optimizing is cheaper for studios while Nvidia doesn't mind extra popularity for their DLSS feature.
Crysis 3 is over a decade old, still looks great and ran on hardware that wouldn't suffice for web browsing today. I haven't seen anything in games since then that justified a significant increase in system requirements.
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.
Games are a bit different in that the game engine is a core part of the product. There aren't many projects with $100M budgets building directly on top of a search engine. A lot of small and medium sized companies will use off the shelf game engines, but you'll never see Rockstar switching over to Unity/Unreal.
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.
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.
If it has a pittance of cache, then mechanical sympathy might not mean cache-friendliness. Linked lists were mechanically sympathetic on uncached systems - it cost the same to access any byte of memory, so you might as well go directly to the one you wanted.
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 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.
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...
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 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.
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... on a STM32H5 with less than a megabyte of RAM.
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’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.
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 (that maths to around 8x) 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. Things 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.
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.
i got over 800 this way. O(n^2) everywhere and n+1 queries
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).
a hashset was used... then ".Where(x=>x == key).Count() == 0"
Nitpick but 25% improvement over 8 quarters is 1.25^8, almost a runtime in 1/6th of the time.
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.
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 if you are tactful it’s fine.
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.
> objectively
I see what you did there
Processors I mostly write firmware for have little cache at all.
I think cache friendly code also tends to be reason about friendly and debugger friendly. None of that is remotely premature optimization.
> I think cache friendly code also tends to be reason about friendly and debugger friendly.
Not sure what examples you have in mind, but the very first things I think of when I hear cache friendly code are cache-blocking and cache-oblivious algorithms, which are quite the opposite of that. See also B-trees vs. binary search trees, etc.
I usually start with simpler things like paying attention to locality of reference and adjusting data so that it takes up less space in memory.
Even CPUs like the ARM cores have caches, usually 16K, occasionally 32K, and even larger L2 caches (512K).
Before AI, nobody has been responsible for more psychic damage against software developers than Bob Martin.
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.
I liked the book, so did my colleagues, and we even had a meetup to discuss the book on how to improve the legacy codebase we had inherited.
think you mean "psychological damage". Can you give some examples of what you mean?
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
Psychic is a damage type in D&D (and probably other RPGs too), so that is most likely a reference.
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.
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.
If everyone stops writing game engines then who is going to program unreal engine/unity/godot?
I’ll admit I lost the plot a long time ago, but there’s a part of me that wonders if the main purpose behind the game engine and graphics card treadmill is to line the pockets of people who sell games engines and graphics cards. My subjective sense of the connection between technology and enjoyment as a player started to degrade about a decade back.
I think it was just studios competing with each other in the 2010s to see who could make the biggest most ambitious game worlds. Leading to ballooning dev costs and then a feedback loop of higher production costs leading to safer design decisions.
There was also a heavy emphasis on games being "cinematic" which necessitates photorealistic rendering.
AAA games have been pretty artless for a while now baring a couple exceptions.
It’s especially the “cinematic” bit that got me. It’s technically impressive, but isn’t as visually interesting as what lower budget games have been doing with less polygons and more inspiration.
I'd it's more a convenient outcome of maximizing profits than premeditated. Not optimizing is cheaper for studios while Nvidia doesn't mind extra popularity for their DLSS feature.
Crysis 3 is over a decade old, still looks great and ran on hardware that wouldn't suffice for web browsing today. I haven't seen anything in games since then that justified a significant increase in system requirements.
What about hardware that could run crysis3 wouldn’t suffice for web browsing?
Well, it ran on my 9600GT, which I used in a desktop from 2016 and recently used that desktop with 16GB RAM for the last few weeks.
I was unable to see any site that used webgl, which is almost all games site and many charting and graphing apps.
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.
Games are a bit different in that the game engine is a core part of the product. There aren't many projects with $100M budgets building directly on top of a search engine. A lot of small and medium sized companies will use off the shelf game engines, but you'll never see Rockstar switching over to Unity/Unreal.
Okay, but google search has only gotten worse for decades.
So your argument is that game engines will just decay.
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.
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.
Yes, I was suggesting allocation as another example of the same principle.
If it has a pittance of cache, then mechanical sympathy might not mean cache-friendliness. Linked lists were mechanically sympathetic on uncached systems - it cost the same to access any byte of memory, so you might as well go directly to the one you wanted.
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.
What is a cache friendly version of linked lists?
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.
Cdr-coding?
https://en.wikipedia.org/wiki/Unrolled_linked_list
Similarly, if you squint at B-trees, they are just unrolled linked lists with "skipping".
B+ tree if you want unrolling, B-tree if you just want the faster random access.
> mechanical sympathy feels like a lost art
Programs should express the solution, not the problem.
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...
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!
https://people.freebsd.org/~lstewart/articles/cpumemory.pdf
Has anyone rewritten this for 2026?
I feel like the solution presented is just a clustered hashmap or “locality sensitive” if you’d like it.
Still not orders of magnitude better.
Uh, isn't this just radix-partitioned aggregation? Hierarchical (fixed two level) aggregation is old. What's novel here?
Microcosm of Pivot itself I guess. I just don't get why this project exists when DuckDB and Clickhouse both exist.
From the article: "To do this, we'll use a method called radix partitioning."
I don't think they were claiming it was novel. Seemed just like an article explaining a performance optimization
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.
Shout out to the author, it seems coherent.
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 :)