chapel VS django-rest-framework

Thanks, @subharmonicon!

While Chapel can run on many different systems, the main goal is making HPC programming much easier. Therefore, we are currently focusing on hardware that you can find in HPC systems (NVIDIA, AMD and Intel). Metal doesn't fall into that category, unfortunately. So far, the name came up infrequently in our discussions IIRC (especially targetting SPIRV), but we haven't heard from any [potential] user who may be interested in it. I would encourage you or anybody else interested in it to create an issue asking for the feature: https://github.com/chapel-lang/chapel/issues/new. Seeing public interest in that direction can change our prioritization.

One thing that I wanted to add that's not in the blogpost is the "cpu-as-device" mode. With that mode, you can use any machine, even one without a GPU, to write applications using Chapel's GPU features. That mode is for those who want to do initial development/debugging on their personal laptops before putting their application on an HPC system. In other words, while you can't use Metal directly, you can still write GPU-enabled applications in your Mac using Chapel, if the end goal is to run it on an HPC system. More details on cpu-as-device: https://chapel-lang.org/docs/main/technotes/gpu.html#cpu-as-...

Agreed. Here is a serious contender[0] minus all the hype and the $100M in VC money. You would expect a minimum of interest given how Mojo is received by the community, but not really in practice.

[0]: https://chapel-lang.org/

Cray is pushing their own language as well, Chapel.

https://chapel-lang.org/

As for Julia on Cray,

"Julia — The Newest Petaflop Family Language We Have Started to Love"

https://www.avenga.com/magazine/julia-programming-language

> Julia is one of the few languages that are in the so-called PetaFlop family; the other languages are C, C++ and Fortrant. It achieved 1.54 petaflops with 1.3 million threads on the Cray XC40 supercomputer.

https://chapel-lang.org if possible, Nix was also recently mentioned in Chapel Workshop https://chapel-lang.org/CHIUW2023.html https://github.com/twesterhout/nix-chapel

See Val for a possible step into that direction.

https://www.val-lang.dev/

Or how the Chapel language for HPC is going at it,

https://chapel-lang.org/

I suggest posting to a PLT focused resource, such as http://lambda-the-ultimate.org/

That said, a bit confused about the languages you reference in this context (Python, C#, JS) - didn't see any mention here or at your github repo of languages (some relatively ancient) in this space designed.

Sandia: Programming Languages for HPC [high performance computing] - is there life after MPI?

https://www.sandia.gov/app/uploads/sites/179/2022/04/SOS10-T...

Chapel:

https://chapel-lang.org/

https://en.wikipedia.org/wiki/Category:Array_programming_lan...

We needed the implicit conversion to `uint` in order for the overload resolution rules to make reasonable choices when faced with binary overloads for all of the numeric types. The document I linked talks through the examples. The case we were facing is something that we shared with `C#` -- in `C#` terms, if I make overloads for `f` for all numeric types (see https://github.com/chapel-lang/chapel/blob/main/test/types/coerce/allNumericsBinary.cs if you want to know exactly what I am talking about), then `f( myInt, myUlong )` runs `f(float, float)` which makes no sense. Especially if you care about numerical accuracy or program performance.

Code | Blog Walkthrough

- Django Rest Framework has no async support? https://github.com/encode/django-rest-framework/issues/7260

A URL shortener is a service that transforms long and complex URLs into short, easily memorable ones. This tutorial guides you through creating a URL shortener using Django REST framework and Postgres, deploying it on Koyeb.

Now, I'm converting this app into a Vue-based SPA (still powered by Django). I'm using the Django REST Framework to build the API that the SPA will interact with. (I'll be using token-based auth, via django-rest-knox. ETA: I'll actually be using djangorestframework-simplejwt.)

For the backend, we chose Python, Django Rest Framework. On the frontend, React, Redux, Saga, Sass. Let’s start with the backend, which was managed by Yegor. He writes about the server part of the project himself.

With regard to JSON API aspects of Django, have you used https://www.django-rest-framework.org/ ? I find it to be very satisfactory.

django-rest-framework and the Fetch API, or

Django REST Framework (DRF) Django REST Framework is a famous, powerful, and flexible toolkit for building Web APIs in Django. It provides a set of reusable components and tools to simplify API development, including serialization, authentication, permissions, and view sets.

I wish Django would take async more seriously. This comment gives a pretty good overview of the current situation (some points are more valid than others): https://github.com/encode/django-rest-framework/discussions/...

The Python ecosystem is strange. Where other dev communities will embrace new ways of doing things faster than most people can keep up — the Python community needs to be pulled kicking and screaming into the light once ever decade or so. Python 2 to 3, ~10 years.

async/await has been in Python since 2015, it feels like it's going to be another 5 years before we see people taking async seriously in the big packages. Same problem we had during the 2/3 transition. No library support, no developer support.

Thanks for helping Steve to complete his REST API, he is now partying🥳. You can get more information on how to use the Django REST framework to best way read the documentation. There are many alternatives to the Django REST framework, you can give them a try if you want,

[Django Rest Framework](https://www.django-rest-framework.org/) has the very nice idea of a [ViewSet](https://www.django-rest-framework.org/api-guide/viewsets/#modelviewset) that allows really minimal code to perform all the basic CRUD actions on a model.