fib VS tangram

I've typically found rust to be about 100x faster than Python for the projects I've ported. This is consistent with https://github.com/drujensen/fib.

Here is a similar benchmark but includes compile times. https://github.com/drujensen/fib

How? Do you mean how do I know it's slow? Because it takes longer to run.

Write a typical computation such as Fibonacci in Java and Erlang/Elixir and compare. Fortunately someone has already done this.

Elixir is 3x slower than C and 2x slower than Java for this single thread example.

https://github.com/drujensen/fib

Apparently this upsets people for me to point this out. However, I did not say that Elixir was slow in general or a bad choice. It's an excellent choice for problems which suit parallelization or which require reliable, consistent performance.

Since the parent poster had commented that adding this multi-module dispatch would not be performant, I merely pointed out that the single thread peformance was already slow (as in, why worry too much about the performance cost of the multi dispatch suggestion).

Is there some advantage over repos that also show the results ?

Head over to https://www.tangram.dev and give it a try!

In this post, we will go over how we optimized our Gradient Boosted Decision Tree library. This is based on a talk that we gave at RustConf 2021: Writing the Fastest Gradient Boosted Decision Tree Library in Rust. The code is available on GitHub.

You can check out the Tangram Ruby Gem. We built it using Ruby FFI and the source is available on our GitHub repo.

We are using Rust for our web application and website at Tangram. You can view the source here: https://github.com/tangramdotdev/tangram/tree/main/crates/www. The website is at https://www.tangram.dev. We decided to write our own web framework because we needed server rendering and we wanted to use the builder pattern in creating components. Here is an example component for our Logo to get a sense of what this looks like: https://github.com/tangramdotdev/tangram/blob/main/crates/www/ui/logo.rs

Check out https://github.com/tangramdotdev/tangram.

Tangram | Senior Rust Programmer | Remote | https://www.tangram.dev

Tangram is an all in one machine learning framework designed for programmers. With Tangram, developers can train models and make predictions on the command line or with libraries for languages including Elixir, Go, JS, Python, Ruby, and Rust, and learn about their models and monitor them in production from a web application. To learn more about what the product does, watch the demo on the homepage at https://www.tangram.dev or check it out on GitHub at https://www.github.com/tangramdotdev/tangram.

We are looking to grow our engineering team with senior Rust programmers. We are currently based in Boston, MA but are looking to build a remote team. At Tangram, you'll get to work on everything from our core machine learning algorithms to writing front-end code in Rust! We are looking for developers with experience in Rust and familiarity with or willingness to learn machine learning concepts. If this sounds exciting, email me (Isabella, cofounder) at [email protected].

We chose to use Rust instead of TypeScript for the front end of https://github.com/tangramdotdev/tangram.

This allows us to:

* Share code with our server written in Rust.

I'm working on machine learning in Rust at Tangram. We currently only provide an implementation of linear models and gradient boosted decision trees but will move into exposing training of deep models in the future. You can check out Tangram here: https://github.com/tangramdotdev/tangram. You may also be interested in checking out Linfa https://github.com/rust-ml/linfa. If you're interested in the future of machine learning in Rust, check out Luca Palmieri's blog post: https://www.lpalmieri.com/posts/2019-12-01-taking-ml-to-production-with-rust-a-25x-speedup/