PIM-Programming-In-Spiral-UPMEM-Demo VS chapel

I originally started work on [Spiral](https://github.com/mrakgr/The-Spiral-Language) back in late 2016 because I wanted a functional language in which I could program novel AI hardware that hadn't existed at the time, and still doesn't, but it won't be long before it arrives. It took 3 years of full time work to get it to its current standard of quality, and I'd really feel comfortable programming new hardware devices in my favored functional style. I've designed Spiral so it is both extremely powerful, easy to use while being efficient enough to program devices like GPUs that can't even use heap allocation for their objects.

I am not really concerned about what I'll do when I get access to Tenstorrent chips in six months; my personal needs for the language are met. But I would like it if I could spread the language more broadly, make it useful for people other than myself and get people to sponsor my work on it.

Here is the value proposition of Spiral.

It is a high-level functional PL that has some features that other languages don't, but that isn't really the point. On mainstream devices like the x86 ones there are a lot of programming languages that are good, and it would be tedious to use Spiral to compile to such platforms compared to using such languages directly. It is a bit how ReasonML compiles to JS. Back when I tried it I found using Typescript easier to deal with. So that is not where I'd like to go into, though using Spiral would have benefits in certain areas.

Rather, while reading the [CNX blog](https://www.cnx-software.com/) I realized that while consumer facing AI chips are not here yet, there is a lot of hardware development in the embedded space. They are heterogenous architecture. They have GPU and TPUs in addition to CPUs. And these cross platform interactions within the same system is something that existing languages are really poor at tackling.

If you look at Python or C#, for example, you can't really program the GPU on them directly. They are CPU focused, and don't have the right semantics and would be too inefficient to program devices like GPUs directly. The way I've designed Spiral is that you can program the CPU and the GPU and whatever else from within the same language.

It is not suitable for just GPUs, check this [demo out](https://github.com/mrakgr/PIM-Programming-In-Spiral-UPMEM-Demo). I recently did a backend for UPMEM devices, which are the first commercialized Process-In-Memory chips. I've posted the link to this on HN yesterday and on the Reddit embedded sub, but I got zero interest. And this is really a pity because that map kernel I've demoed is actually a big deal. Back when I first started working on Spiral, it took me 1.5 years of full time work to get to the point where I could write a program like that in the language. And without backend nesting of the kind that Spiral offers, it is impossible to write those kinds of programs no matter how skilled one is as a programmer.

The kind of backend nesting I've demonstrated is not something you can do in F#, Python or any of the languages that I know of. I could easily create such backends for many kinds of hardware. And people would benefit from that because unlike the mainstream computing devices, the hardware coming down the pipeline will have poor language support, nothing on the level of what Spiral offers. For the kinds of heterogeneous architectures I am envisioning, the language designs that are good in the CPU-dominant era, will simply not be suited in the heterogeneous era.

I need chances to demonstrate how good Spiral is, but I am not sure how to get them. If I do not get them, the future of computing will be a lot worse off. I wasn't there when Cuda was incumbent so I missed the boat on that, but I'd like it if Spiral became dominant on future computing devices. Not because I was the one who made the language, but simply because no other design is as suited for them.

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.

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