There is also new Adaptive Radix Tree implementation - https://www.db.in.tum.de/~leis/papers/ART.pdf which is supposed to be faster than B-Tree
High-performance header-only container library for C++23 on x86-64
11–20 of 30 posts
Re: High-performance header-only container library for C++23 on x86-64
#12[dead]
Re: High-performance header-only container library for C++23 on x86-64
#132-5x faster than both abseil's b+tree and std::map means that abseil's b+tree had to be the same performance as std::map for the tested workload. This is... very unusual. I have only ever seen it be much faster or moderately slower.
Re: High-performance header-only container library for C++23 on x86-64
#14> History/Motivations This project started as an exploration of using AI agents for software development. Based on experience tuning systems using Abseil's B+tree, I was curious if performance could be improved through SIMD instructions, a customized allocator, and tunable node sizes. Claude proved surprisingly adept at helping implement this quickly, and the resulting B+tree showed compelling performance improvement…
Both Codex/Claude Code are terrible with C++. Not sure why that is, but they just spit out nonsense that creates more work than it helps me. Have you tried to do any OpenGL or Vulkan work with it? Very frustrating. React and HTML, though, pretty awesome.
I use ChatGPT with C++ but in very limited manner. So far it was overall win. I watch the code very closely of course and usually end up doing few iterations (mostly optimizing for speed, reliability, concurrency).
Also to generate boilerplate / repetitive.
Overall I consider it a win.
Re: High-performance header-only container library for C++23 on x86-64
#15> History/Motivations This project started as an exploration of using AI agents for software development. Based on experience tuning systems using Abseil's B+tree, I was curious if performance could be improved through SIMD instructions, a customized allocator, and tunable node sizes. Claude proved surprisingly adept at helping implement this quickly, and the resulting B+tree showed compelling performance improvement…
Both Codex/Claude Code are terrible with C++. Not sure why that is, but they just spit out nonsense that creates more work than it helps me. Have you tried to do any OpenGL or Vulkan work with it? Very frustrating. React and HTML, though, pretty awesome.
I use Claude to generate C++ 23, it usually performs well. It takes a bit of nudging to avoid repeating itself, reusing existing functionality, not altering huge portions without running tests, etc. But generally it is helpful and knows what to do.
Re: High-performance header-only container library for C++23 on x86-64
#16> History/Motivations This project started as an exploration of using AI agents for software development. Based on experience tuning systems using Abseil's B+tree, I was curious if performance could be improved through SIMD instructions, a customized allocator, and tunable node sizes. Claude proved surprisingly adept at helping implement this quickly, and the resulting B+tree showed compelling performance improvement…
I'd love to see a breakdown of what exactly worked here, or better yet, PR to upstream Abseil that implements those ideas.
AI is always good at going from 0 to 80%, it's the last 20% it struggles with. It'd be interesting to see a claude-written code making its way to a well-established library.
Re: High-performance header-only container library for C++23 on x86-64
#172-5x faster than both abseil's b+tree and std::map means that abseil's b+tree had to be the same performance as std::map for the tested workload. This is... very unusual. I have only ever seen it be much faster or moderately slower.
Not necessarily. Insert could be 5x faster in one, and 2x faster in another, and there would still be orders of magnitude difference between both. 2x-5x is a long range.
Re: High-performance header-only container library for C++23 on x86-64
#18Ok, maybe someone here can clear this up for me. My understanding of B+tree's is that they are good for implementing indexes on disk because the fanout reduces disk seeks... what I don't understand is in memory b+trees... which most of the implementations I find are. What are the advantages of an in memory b+tree?
Re: High-performance header-only container library for C++23 on x86-64
#19Earlier quoted context omitted.
Both Codex/Claude Code are terrible with C++. Not sure why that is, but they just spit out nonsense that creates more work than it helps me. Have you tried to do any OpenGL or Vulkan work with it? Very frustrating. React and HTML, though, pretty awesome.
Which models? It's possible Opus 4.5 and GPT-5.2 are significantly less terrible with C++ than previous models. Those only came out within the past 2 months. They also have significantly more recent knowledge cut-off dates.
I'll be specific:
I've been recently working with Opus 4.5 and GPT-5.2. Both have been unable to migrate a project from using ARB shaders to 3.3 and GLSL. And I don't mean migrating the shaders themselves, just changing all the boring glue code that tells the application to use GLSL and manage those instead of feeding the ARB shaders directly.
They have also failed spectacularly at implementing this paper: https://www.cse.chalmers.se/~uffe/soft_gfxhw2003.pdf
No matter how I sliced it, I could not get a simple cube to have the shadows as described in the paper.
I've also recently tried to get Opus 4.5 to move the Job system from Doom 3 BFG to the original codebase. Clean clone of dhewm3, pointed Opus to the BFG Job system codebase, and explained how it works. I have also fed it the Fabien Sanglard code review of the job system: https://fabiensanglard.net/doom3_bfg/threading.php
As well as the official notes that explain the engine differences: https://fabiensanglard.net/doom3_documentation/DOOM-3-BFG-Te...
I did that because, well, I had ported this job system before and knew it was something pretty "pluggable" and could be implemented by an LLM. Both have failed. I'm yet to find a model that does this.
Re: High-performance header-only container library for C++23 on x86-64
#20Ok, maybe someone here can clear this up for me. My understanding of B+tree's is that they are good for implementing indexes on disk because the fanout reduces disk seeks... what I don't understand is in memory b+trees... which most of the implementations I find are. What are the advantages of an in memory b+tree?
https://github.com/abseil/abseil-cpp/blob/master/absl/contai... mentions that b-tree maps hold multiple values per node, which makes them more cache-friendly than the red-black trees used in std::map.
You use either container when you want a sorted associative map type, which I have not found many uses cases for in my work. I might have a handful of them versus many instances of vectors and unsorted associative maps, i.e. absl::flat_hash_map.