ipc-bench VS hashbrown

Anyone who thinks they understand unix signals is fooling themselves. Anyway, the basis of the claim that you can exchange half a million small messages per second using signals is misunderstanding. The benchmark suite in question passes no data, it only ping-pongs the signal.

https://github.com/goldsborough/ipc-bench

Having now done some benchmarking, I would like to use shared_memory.

I only use AF_UNIX sockets when I need to pass open file handles between processes. I generally prefer message queues: https://linux.die.net/man/7/mq_overview

I haven’t measured myself, but other people did, and they found the latency of message queues is substantially lower: https://github.com/goldsborough/ipc-bench

After reading your post, it seems as if you are interested in something called Inter Process Communication (IPC). There are as many solutions to this as there are birds in the sky (not really, but almost). If you want _speed_, take a look at this benchmark comparison: https://github.com/goldsborough/ipc-bench.

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.