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

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.

Reminds me of HVM[0]

[0]https://github.com/HigherOrderCO/HVM

Really interesting to see how new lang concepts and refinements keep popping up this last decade, between Vale, Gleam, Hylo, Austral...

Linear types really opened up lots of ways to improve memory management and compilation improvements.

I have a tangential question that is related to this cool new feature.

Warning: the question I ask comes from a part of my brain that is currently melted due to heavy thinking.

Context: I write a fair amount of Clojure, and in Lisps the code itself is a tree. Just like this F# parallel graph type-checker. In Lisps, one would use Macros to perform compile-time computation to accomplish something like this, I think.

More context: Idris2 allows for first class type-driven development, where the types are passed around and used to formally specify program behavior, even down to the value of a particular definition.

Given that this F# feature enables parallel analysis, wouldn't it make sense to do all of our development in a Lisp-like Trie structure where the types are simply part of the program itself, like in Idris2?

Also related, is this similar to how HVM works with their "Interaction nets"?

https://github.com/HigherOrderCO/HVM

https://www.idris-lang.org/

https://clojure.org/

I'm afraid I don't even understand what the difference between code, data, and types are anymore... it used to make sense, but these new languages have dissolved those boundaries in my mind, and I am not sure how to build it back up again.

Impressive presentation but I find two things missing in particular:

* GRIN [1] - arguably a breakthrough in FP compilation; there are several implementation based on this

* HVM [2] - parallel optimal reduction. The results are very impressive.

[1] https://link.springer.com/chapter/10.1007/3-540-63237-9_19

[2] https://github.com/HigherOrderCO/HVM

Purity has nothing to do with memoization. Haskell's semantics never "rewrite under a lambda" (unlike, e.g. HVM). Calling (\_ -> e) () twice will (modulo optimizations) always perform the computation in e twice.

The most recent exploration of this, that I'm aware of is HVM (another intermediate language / runtime), although this one is not actually based on the lambda calculus, but on the interaction calculus.

Then, actually unrelated but worth mentioning: HVM. Finally, something new on the functional front that isn't dependent types!

If you are into functional PL, how about https://github.com/HigherOrderCO/HVM? You could experiment if you could schedule that on a GPU?

In the interest of seeking ways of optimizing my code, I stumbled upon http://www.rntz.net/datafun/ as a means to do incremental computations of fixpoints while avoiding redundant work. And also the idea of automatic parallelism achieved by using Interaction Nets as a model of computation https://github.com/HigherOrderCO/HVM.