treeedb VS hashbrown

https://github.com/topics/datalog?l=rust ... Cozo, Crepe

Crepe: https://github.com/ekzhang/crepe :

> Crepe is a library that allows you to write declarative logic programs in Rust, with a Datalog-like syntax. It provides a procedural macro that generates efficient, safe code and interoperates seamlessly with Rust programs.

Looks like there's not yet a Python grammar for the treeedb tree-sitter: https://github.com/langston-barrett/treeedb :

> Generate Soufflé Datalog types, relations, and facts that represent ASTs from a variety of programming languages.

Looks like roxi supports n3, which adds `=>` "implies" to the Turtle lightweight RDF representation: https://github.com/pbonte/roxi

FWIW rdflib/owl-rl: https://owl-rl.readthedocs.io/en/latest/owlrl.html :

> simple forward chaining rules are used to extend (recursively) the incoming graph with all triples that the rule sets permit (ie, the “deductive closure” of the graph is computed).

ForwardChainingStore and BackwardChainingStore implementations w/ rdflib in Python: https://github.com/RDFLib/FuXi/issues/15

Fast CUDA hashmaps

Gdlog is built on CuCollections.

GPU HashMap libs to benchmark: Warpcore, CuCollections,

https://github.com/NVIDIA/cuCollections

https://github.com/NVIDIA/cccl

https://github.com/sleeepyjack/warpcore

/? Rocm HashMap

DeMoriarty/DOKsparse:

That's because you're looking at a wrapper around the actual implementation (which lives in an external package). Notice "use hashbrown::hash_map as base;" at the top.

There's far more unsafe there: https://github.com/rust-lang/hashbrown/blob/f2e62124cd947b5e...

You might want to check out how popular ecosystem crates do some of these things. Particularly relevant to you are probably crates providing collections, such as smallvec, hashbrown, or indexmap.

https://github.com/topics/swisstable

rust-lang/hashbrown: https://github.com/rust-lang/hashbrown

CuPy has array but not yet hashmaps, or (GPU) SIMD FWICS?

NumPy does SIMD:

Thanks, great point indeed. I am looking into this https://github.com/rust-lang/hashbrown

The way I think about it -- rather naively, I suppose -- is that I care more about the references cells make to each other than the actual grid of cells displayed on a table. The latter feels more like a "view" of the data than an actual data structure?

This also seems to align with the relative priority of (sorted from highest to lowest): figuring out the order of evaluation, calculating those evaluations, and finally displaying the results of the evaluation

updated std's hashbrown dependency to 0.14 which contains some optimizations

The std hashmap is actually very fast and uses state of the art hashmap design, namely because it's implemented by hashbrown

I believe Rust uses hashbrown as the underlying implementation now. This just calculates the number of buckets based on the number of items requested:

https://github.com/rust-lang/hashbrown/blob/009969a860290849...

Is it really the case that rehashing can guarantee that the number of buckets allocated will be sufficient for any given set of keys? In principle you could fail to rehash in a way that reduces collisions after k attempted rehashings.

For example, since the hashbrown crate is marked with #![no_std], it can be used as a dependency for the standard library.

To implement something that cannot be expressed in safe Rust, or at least cannot be expressed succinctly in safe Rust, like fundamental datastructures. The hashbrown crate contains a lot of unsafe code, but it's such high quality that it's now the backing implementation for std::collections::HashMap.