matrixmultiply VS faer-rs

The matrixmultiply crate from the ndarray author (https://github.com/bluss/matrixmultiply) is one such implementation. It uses the same algorithm as the BLIS project (https://www.cs.utexas.edu/users/flame/pubs/TOMS-BLIS-Analytical.pdf) to partition the problem and exploit the cache hierarchy. It isn't as well tuned as eg. Intel MKL or BLIS, but the results are very respectable.

Do you plan to support integers as native types? I know there is an issue for the crate matrixmultiply for that, it seems it can be problematic because of overflow.

There's a famous crate [matrixmultiply](https://github.com/bluss/matrixmultiply) for matrix-matrix multiplication in Rust. But it's a bit slow for me.

In my benchmarks, Nim is faster than Rust:

- multithreading runtime (i.e Rayon vs Weave https://github.com/mratsim/weave)

- Cryptography: https://hackmd.io/@gnark/eccbench#Pairing

- Scientific computing / matrix multiplication: https://github.com/bluss/matrixmultiply/issues/34#issuecomme...

There is no inherent reason why a Nim program would be slower than Rust.

see the official website and the docs.rs documentation for code examples and usage instructions.

`faer` is a collection of crates that implement linear algebra routines in pure Rust. the aim is to eventually provide a fully featured library for linear algebra with focus on portability, correctness, and performance.

see the [official website](https://faer-rs.github.io) and the docs.rs documentation for code examples and usage instructions.

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this release implements the initial design of the high level api (see https://docs.rs/faer/0.11.0/faer/), which should allow users to get started with writing easy to read code without having to deal with managing memory or multithreading settings on their own.

i also added a thin compatibility layer with ndarray and nalgebra. this way users are able to mix and match libraries if they wish to do so, or to help with migration to different libraries

Faer is comparable to Eigen in most of their benchmarks, so they are getting closer.

performance compared to openblas is shown in the benchmarks on the repository, and on the website. (ndarray is using openblas)

currently working on a very efficient linear algebra library https://github.com/sarah-ek/faer-rs

We are also watching this project, which when a bit further along could allow us to support a purely Rust version with no recourse to external blas/lapack libraries in the SDP case.