Klib VS stb

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.

It could be that the cost of the function calls, either directly or via a pointer, is drowned out by the cost of the one or more cache misses inevitably invoked with every hash table lookup. But I don't want to say too much before I've finished my benchmarking project and published the results. So let me just caution against laser-focusing on whether the comparator and hash function are/can be inlined. For example stb_ds uses a hardcoded hash function that presumably gets inlined, but in my benchmarking (again, I'll publish it here in coming weeks) shows it to be generally a poor performer (in comparison to not just CC, the current version of which doesn't necessarily inline those functions, but also STC, khash, and the C++ Robin Hood hash tables I tested).

Not an entirely uncommon idea. I've written one.

There's also a well-known one here, in klib: https://github.com/attractivechaos/klib/blob/master/kvec.h

Code reuse is achievable by (mis)using the preprocessor system. It is possible to build a somewhat usable API, even for intrusive data structures. (eg. the linux kernel and klib[1])

I do agree that generics are required for modern programming, but for some, the cost of complexity of modern languages (compared to C) and the importance of compatibility seem to outweigh the benefits.

[1]: http://attractivechaos.github.io/klib

Unordered hash map shootout CMAP = https://github.com/tylov/STC KMAP = https://github.com/attractivechaos/klib PMAP = https://github.com/greg7mdp/parallel-hashmap FMAP = https://github.com/skarupke/flat_hash_map RMAP = https://github.com/martinus/robin-hood-hashing HMAP = https://github.com/Tessil/hopscotch-map TMAP = https://github.com/Tessil/robin-map UMAP = std::unordered_map Usage: shootout [n-million=40 key-bits=25] Random keys are in range [0, 2^25). Seed = 1656617916: T1: Insert/update random keys: KMAP: time: 1.949, size: 15064129, buckets: 33554432, sum: 165525449561381 CMAP: time: 1.649, size: 15064129, buckets: 22145833, sum: 165525449561381 PMAP: time: 2.434, size: 15064129, buckets: 33554431, sum: 165525449561381 FMAP: time: 2.112, size: 15064129, buckets: 33554432, sum: 165525449561381 RMAP: time: 1.708, size: 15064129, buckets: 33554431, sum: 165525449561381 HMAP: time: 2.054, size: 15064129, buckets: 33554432, sum: 165525449561381 TMAP: time: 1.645, size: 15064129, buckets: 33554432, sum: 165525449561381 UMAP: time: 6.313, size: 15064129, buckets: 31160981, sum: 165525449561381 T2: Insert sequential keys, then remove them in same order: KMAP: time: 1.173, size: 0, buckets: 33554432, erased 20000000 CMAP: time: 1.651, size: 0, buckets: 33218751, erased 20000000 PMAP: time: 3.840, size: 0, buckets: 33554431, erased 20000000 FMAP: time: 1.722, size: 0, buckets: 33554432, erased 20000000 RMAP: time: 2.359, size: 0, buckets: 33554431, erased 20000000 HMAP: time: 0.849, size: 0, buckets: 33554432, erased 20000000 TMAP: time: 0.660, size: 0, buckets: 33554432, erased 20000000 UMAP: time: 2.138, size: 0, buckets: 31160981, erased 20000000 T3: Remove random keys: KMAP: time: 1.973, size: 0, buckets: 33554432, erased 23367671 CMAP: time: 2.020, size: 0, buckets: 33218751, erased 23367671 PMAP: time: 2.940, size: 0, buckets: 33554431, erased 23367671 FMAP: time: 1.147, size: 0, buckets: 33554432, erased 23367671 RMAP: time: 1.941, size: 0, buckets: 33554431, erased 23367671 HMAP: time: 1.135, size: 0, buckets: 33554432, erased 23367671 TMAP: time: 1.064, size: 0, buckets: 33554432, erased 23367671 UMAP: time: 5.632, size: 0, buckets: 31160981, erased 23367671 T4: Iterate random keys: KMAP: time: 0.748, size: 23367671, buckets: 33554432, repeats: 8, sum: 4465059465719680 CMAP: time: 0.627, size: 23367671, buckets: 33218751, repeats: 8, sum: 4465059465719680 PMAP: time: 0.680, size: 23367671, buckets: 33554431, repeats: 8, sum: 4465059465719680 FMAP: time: 0.735, size: 23367671, buckets: 33554432, repeats: 8, sum: 4465059465719680 RMAP: time: 0.464, size: 23367671, buckets: 33554431, repeats: 8, sum: 4465059465719680 HMAP: time: 0.719, size: 23367671, buckets: 33554432, repeats: 8, sum: 4465059465719680 TMAP: time: 0.662, size: 23367671, buckets: 33554432, repeats: 8, sum: 4465059465719680 UMAP: time: 6.168, size: 23367671, buckets: 31160981, repeats: 8, sum: 4465059465719680 T5: Lookup random keys: KMAP: time: 0.943, size: 23367671, buckets: 33554432, lookups: 34235332, found: 29040438 CMAP: time: 0.863, size: 23367671, buckets: 33218751, lookups: 34235332, found: 29040438 PMAP: time: 1.635, size: 23367671, buckets: 33554431, lookups: 34235332, found: 29040438 FMAP: time: 0.969, size: 23367671, buckets: 33554432, lookups: 34235332, found: 29040438 RMAP: time: 1.705, size: 23367671, buckets: 33554431, lookups: 34235332, found: 29040438 HMAP: time: 0.712, size: 23367671, buckets: 33554432, lookups: 34235332, found: 29040438 TMAP: time: 0.584, size: 23367671, buckets: 33554432, lookups: 34235332, found: 29040438 UMAP: time: 1.974, size: 23367671, buckets: 31160981, lookups: 34235332, found: 29040438

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.