Comment by boricj
15 hours ago
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.
4 replies →
Nitpick but 25% improvement over 8 quarters is 1.25^8, almost a runtime in 1/6th of the time.
2 replies →
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.
1 reply →
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.
1 reply →
think you mean "psychological damage". Can you give some examples of what you mean?
6 replies →
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
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?
> 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?
10 replies →
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
3 replies →
> mechanical sympathy feels like a lost art
Programs should express the solution, not the problem.