wyhash VS countwords

I recently switched to wyhash as it seems to have a good combination of speed and stability.

wyhash is a decent option for if you don't need a cryptographical quality hash

New Bare Hash Map: 2X-3X Speedup over SOTA\ (32 comments)

I feel like you’d want something a bit safer than “we don’t store the keys and just rely on the hash to be really good” [1], putting “please do not use this for serious tasks” in a comment embedded in the header file isn’t a clear enough warning.

It’s not clear to me that that probability of collision assumptions hold. It’s basically assuming that the hashing is perfect and distributes any inputs to the full 64-bit space with uniform probability. That’s the usual hash map / randomized algorithm hope, but does BigCrush or similar avalanche testing really prove that? (Presumably not, otherwise there wouldn’t be image attacks for things like md5).

[1] https://github.com/wangyi-fudan/wyhash/blob/d2a305811972f391...

"dang, I didn't know that was 50x faster than the idiomatic way" or "hey, I didn't know that this implementation in the stdlib prioritized this over that and made this so slow, that's interesting" -- .e.g, there's some kinda neat language details to be found in something like Ben Hoyt's community word count benchmarks repo and 'simple' vs 'optimal' code: https://github.com/benhoyt/countwords

It benchmarks programs that count the total number of unique words in some input. It's not exactly equivalent to your problem, but it's similarish. All of the programs used some kind of hash map for lookups, but I contributed a program that used a trie. Its performance in my experience varies depending on the CPU interestingly enough. On my old CPU (i7-6900K) it was a little slower, but on my new cpu (i9-12900KS) it was faster.

Why not read the source code? :-)

I wrote comments explaining things: https://github.com/benhoyt/countwords/blob/8553c8f600c40a462...

My own code example of a drastic speed up (~25%) simply replacing a couple of for loops with iters: https://github.com/benhoyt/countwords/pull/115

And yet Go was faster than Rust in a simple app that count words: https://benhoyt.com/writings/count-words/

> but the performance profiles & characteristics that we must know about in order to make a choice on which tool to use. And it shouldn't be that each user has to figure it out on their own, dig into PR's or whatever.

That's an interesting take – I like the idea of a catalog of standard tasks with implementations in several languages as well as their performance characteristics. I suppose Rosetta Code gets the ball rolling with this, but it's missing some performance metrics. It reminds me of [Ben Hoyt's piece](https://benhoyt.com/writings/count-words/) on counting unique words in the KJV Bible in different languages.

This article shows that map lookups can be optimized by using the (unintuitive) pattern: