Programming-Language-Benchmarks VS matrixmultiply

The benchmark available at https://programming-language-benchmarks.vercel.app/ demonstrates that Golang stands out as one of the most memory-efficient languages presently available. This achievement is attributable to several inherent features of Golang, such as its static typing, robust garbage collection system, and the inherent structuring of data within the language. These traits collectively contribute to Golang's exceptional efficiency in terms of minimal memory consumption compared to other languages.

I found Zig implementation of json parsing is interesting. The code is free from hidden control flow !.

https://github.com/hanabi1224/Programming-Language-Benchmark...

I think https://programming-language-benchmarks.vercel.app/ is a good starting point to compare languages and compilers, also implementations are optimized for the specific language so you don't end up with a poorly ported c++ implementation in rust and wonder why it performs so bad.

It varies a lot depending on what the code consists of, but if you want concrete numbers for certain benchmarks, this site might be of interest: https://programming-language-benchmarks.vercel.app/

There is not "one real" benchmark. In the end, all you can do is test languages for a specific feature / purpose. You can see how many different suggestions people have here, and here (I think) you can see the difficulties of comparing languages. That site uses quite a lot of algorithms / problems with multiple inputs, single and multithreaded, with different optimization flags (where applicable) and so on paired with different languages, and it's a mess. Sometimes one language is on top, sometimes another. (I mean, python will very rarely beat pure C, but I wont rule out that someone already created an edge case just to refute exactly this point)

Hello, World!: Elixir vs. Go vs. Rust

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.