Spreads VS m2cgen

Branchless or not in this case, it still touches memory in not so good pattern. I found that a significant speedup of a classic BS could be achieved by switching to linear SIMD search when the remaining range has a width of 3-4 SIMD lines (or maybe even a little more). The bounds of that range are likely already touched and in cache, then prefetching helps. It gives 30-50% gain on 1K items array of integers, 10-25% on 1M items, depending on data distribution. Here is an example in C#: https://github.com/Spreads/Spreads/blob/main/src/Spreads.Cor...

Check out: https://github.com/BayesWitnesses/m2cgen

Notice that I’m using random values to populate the Sentiment field. You might compute the values for your fields based on other fields or actually use an ML model to perform the transformation. E.g. you could make use of m2cgen to transform trained models to pure python code and load them in **RedisGears **to be executed in a *GearsBuilder *instance. Another option is to pull out the big guns and go straight to RedisAI.

I wish that it was more common for model outputs to be converted the way bayeswitness does with mc2gen https://github.com/BayesWitnesses/m2cgen

And that’s it! All the magic in just two lines of code. I would like to thank the authors of the m2cgen library and encourage you to try it out.

I forgot to update the README. We just replaced RNN with xgboost that has a better f1 and is very quick, as the decision trees are translated to plain rust using m2cgen.

Probably, as many frameworks come with a Rust support (or there are wrappers). Some models, like decision tree, can also be automatically translated to plain Rust (in my company we use m2cgen to translate xgboost models to plain rust code).

These repositories on GitHub are good start I think: https://github.com/BayesWitnesses/m2cgen and https://github.com/vickylance/dart_nn