Spreads VS ML.NET

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...

You can direct your question to https://github.com/dotnet/machinelearning/issues. Perhaps it is already documented.

Here I'm analyzing all projects in the src and test directories of the ML.NET repository. I chose to include these as separate paths because they represent two different groupings of projects in this repository.

Important Note: looping over repository commits takes a long time for large repositories. It took 52 minutes to analyze the ML.NET repository this code example refers to, which had 2,681 commits at the time of analysis on February 25th, 2023.

We checked the ML.NET 1.7.1 version. The source code of this project's version is available on GitHub.

machine learning? https://github.com/dotnet/machinelearning

You can follow some of our plans by taking a look at our roadmap which we'll be updating shortly to more accurately reflect the areas we're investing in.

Re: ONNX, if you run into similar issues in the future, feel free to reach out in our GitHub repo or the ONNX Runtime repo and we'd be happy to help!

Good clarification, I think using something like ML.NET could be cool but I have some experience with Blazor that might be fun to use as well, I think generally performance monitoring or optimizing systems seems interesting to me, and I'm really open to other ideas as well. Let me know if any of that helps narrow my question down!