Programming-Language-Benchmarks VS Petalisp

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

While not "as fast as C" (C is not the absolute pinnacle of performance), Common Lisp is incredibly fast compared to the majority of programming languages around today. There is even a huge amount of ongoing work being done to make it faster still. We are seeing many interesting projects that make better use of the hardware in your computer (e.g. https://github.com/marcoheisig/Petalisp).

i think lisp-stat library is actually being developed. however one numerical cl library that doesnt get enough mention and is being constantly developed is petalisp for HPC

https://github.com/marcoheisig/Petalisp

However, if you have a lisp library that puts those semantics to use, then you could get it to employ magicl/ext-blas and cl-bmas to speed it up. (petalisp looks relevant, but I lack the background to compare it with APL.)

> his means cl pagckages can be "done".

this is true if there is nothing functional that can be added to a package. however its very much not true for ml frameworks right now. new things are being added all the time in the field. however even in the package i linked you have the necessary ingredients for any deep learning model: cuda and back propagation. the other person mentioned convolution which i think is pretty trivial to implement but still, if you expect everything for you to be ready made then you should probably stick to tf and pytorch. if you want to explore the cutting edge and push the boundaries then i think common lisp is a good tool. as an aside it might also be interesting to note that a common lisp package (Petalisp) is being used for high performance computing by a german university

https://github.com/marcoheisig/Petalisp

One trick is that Common Lisp can generate and compile code at runtime, whereas static languages typically do not have a compiler available at runtime. This lets you make your own lazy person's JIT/staged compiler, which is useful if some part of the problem is not known at compile-time. Such an approach has been used at least for array munging, type munging and regular expression munging.