stb VS CC

Have you considered not using an engine at all, in favor of libraries? There are many amazing libraries I've used for game development - all in C/C++ - that you can piece together:

* General: [stb](https://github.com/nothings/stb)

Great to see more accessible references on font internals. I have dabbled on this a bit last year and managed to have a parser and render the points of a glyph's contour (I stopped before Bezier and shape filling stuff). I still have not considered hinting, so it's nice that it's covered. What helped me was an article from the Handmade Network [1] and the source of stb_truetype [2] (also used in Dear ImGUI).

[1] https://handmade.network/forums/articles/t/7330-implementing....

[2] https://github.com/nothings/stb/blob/master/stb_truetype.h

So I read through the materials on mesh shaders and work graphs and looked at sample code. These won't really work (see below). As I implied previously, it's best to research/discuss these sort of matters with professional graphics programmers who have experience actually using the technologies under consideration.

So for the sake of future web searchers who discover this thread: there are only two proven ways to efficiently draw thousands of unique textures of different sizes with a single draw call that are actually used by experienced graphics programmers in production code as of 2023.

Proven method #1: Pack these thousands of textures into a texture atlas.

Proven method #2: Use bindless resources, which is still fairly bleeding edge, and will require fallback to atlases if targeting the PC instead of only high end console (Xbox Series S|X...).

Mesh shaders by themselves won't work: These have similar texture access limitations to the old geometry/tessellation stage they improve upon. A limited, fixed number of textures still must be bound before each draw call (say, 16 or 32 textures, not 1000s), unless bindless resources are used. So mesh shaders must be used with an atlas or with bindless resources.

Work graphs by themselves won't work: This feature is bleeding edge shader model 6.8 whereas bindless resources are SM 6.6. (Xbox Series X|S might top out at SM 6.7, I can't find an authoritative answer.) It looks like work graphs might only work well on nVidia GPUs and won't work well on Intel GPUs anytime soon (but, again, I'm not knowledgeable enough to say this authoritatively). Furthermore, this feature may have a hard dependency on using bindless to begin with. That is, I can't tell if one is allowed to execute a work graph that binds and unbinds individual texture resources. And if one could do such a thing, it would certainly be slower than using bindless. The cost of bindless is paid "up front" when the textures are uploaded.

Some programmers use Texture2DArray/GL_TEXTURE_2D_ARRAY as an alternative to atlases but two limitations are (1) the max array length (e.g. GL_MAX_ARRAY_TEXTURE_LAYERS) might only be 256 (e.g. for OpenGL 3.0), (2) all textures must be the same size.

Finally, for the sake of any web searcher who lands on this thread in the years to come, to pack an atlas well a good packing algorithm is needed. It's harder to pack triangles than rectangles but triangles use atlas memory more efficiently and a good triangle packing will outperform the fancy new bindless rendering. Some open source starting points for packing:

https://github.com/nothings/stb/blob/master/stb_rect_pack.h

https://github.com/ands/trianglepacker

The STB headers are mostly built like that: https://github.com/nothings/stb

You could also add an optional 'convenience API' over the lower-level flexible-but-inconvenient core API, as long as core library can be compiled on its own.

In essence it's just a way to decouple the actually important library code from runtime environment details which might be better implemented outside the C/C++ stdlib.

It's already as simple as the stdlib IO functions not being asynchrononous while many operating systems provide more modern alternatives. For a specific type of library (such an image decoder) it's often better to delegate such details to the library user instead of circumventing the stdlib and talking directly to OS APIs.

My stuff for instance:

https://github.com/floooh/sokol

...inspired by:

https://github.com/nothings/stb

But it's not so much about the build system, but requiring a separate C/C++ compiler toolchain (Rust needs this, Zig currently does not - unless the proposal is implemented).

STB Libraries: https://github.com/nothings/stb

stb_ds is also very popular.

With extendible macros, you could achieve the following:

In my example the table stores the hash codes themselves instead of the keys (because the hash function is invertible)

Oh, I see, right. If determining the home bucket is trivial, then the back-shifting method is great. The issue is just that it’s not as much of a general-purpose solution as it may initially seem.

“With a different algorithm (Robin Hood or bidirectional linear probing), the load factor can be kept well over 90% with good performance, as the benchmarks in the same repo demonstrate.”

I’ve seen the 90% claim made several times in literature on Robin Hood hash tables. In my experience, the claim is a bit exaggerated, although I suppose it depends on what our idea of “good performance” is. See these benchmarks, which again go up to a maximum load factor of 0.95 (Although boost and Absl forcibly grow/rehash at 0.85-0.9):

https://strong-starlight-4ea0ed.netlify.app/

Tsl, Martinus, and CC are all Robin Hood tables (https://github.com/Tessil/robin-map, https://github.com/martinus/robin-hood-hashing, and https://github.com/JacksonAllan/CC, respectively). Absl and Boost are the well-known SIMD-based hash tables. Khash (https://github.com/attractivechaos/klib/blob/master/khash.h) is, I think, an ordinary open-addressing table using quadratic probing. Fastmap is a new, yet-to-be-published design that is fundamentally similar to bytell (https://www.youtube.com/watch?v=M2fKMP47slQ) but also incorporates some aspects of the aforementioned SIMD maps (it caches a 4-bit fragment of the hash code to avoid most key comparisons).

As you can see, all the Robin Hood maps spike upwards dramatically as the load factor gets high, becoming as much as 5-6 times slower at 0.95 vs 0.5 in one of the benchmarks (uint64_t key, 256-bit struct value: Total time to erase 1000 existing elements with N elements in map). Only the SIMD maps (with Boost being the better performer) and Fastmap appear mostly immune to load factor in all benchmarks, although the SIMD maps do - I believe - use tombstones for deletion.

I’ve only read briefly about bi-directional linear probing – never experimented with it.

From your other comments, it seems like your knowledge of hash tables might be limited to closed-addressing/separate-chaining hash tables. The current frontrunners in high-performance, memory-efficient hash table design all use some form of open addressing, largely to avoid pointer chasing and limit cache misses. In this regard, you want to check our SSE-powered hash tables (such as Abseil, Boost, and Folly/F14), Robin Hood hash tables (such as Martinus and Tessil), or Skarupke (I've recently had a lot of success with a similar design that I will publish here soon and is destined to replace my own Robin Hood hash tables). Also check out existing research/benchmarks here and here. But we a little bit wary of any benchmarks you look at or perform because there are a lot of factors that influence the result (e.g. benchmarking hash tables at a maximum load factor of 0.5 will produce wildly different result to benchmarking them at a load factor of 0.95, just as benchmarking them with integer keys-value pairs will produce different results to benchmarking them with 256-byte key-value pairs). And you need to familiarize yourself with open addressing and different probing strategies (e.g. linear, quadratic) first.

CC (Full disclosure: I authored this one)

If you're using C23 or have typeof (so GCC or Clang), then yet another approach is to define a type that aliases the specified type if it is unique or otherwise becomes a "dummy" type. Here's what that looks like in CC:

I’d like to share version 1.0.3 of Convenient Containers (CC), my generic container library. The library was previously discussed here and here. As explained elsewhere,

Convenient Containers (CC) - I'm the author of this one.

"Using macros" is a broad description that covers multiple paradigms. There are libraries that use macros in combination with typed pointers and functions that take void* parameters to provide some degree of API genericity and type safety at the same time (e.g. stb_ds and, as you mentioned, my own CC). There are libraries that use macros (or #include directives) to manually instantiate templates (e.g. STC, M*LIB, and Pottery). And then there are libraries that are implemented entirely or almost entirely as macros (e.g. uthash).

Shameless plug: Take a look a my library Convenient Containers, which solves this exact problem within the (narrow) domain of data structures.