accelerate VS chapel

Someone has opened a PR for accelerate here https://github.com/AccelerateHS/accelerate/pull/525 (sadly seems not actively maintained at the moment, but that can always change if people care enough). I agree for an executable you should freeze your dependencies and compiler version, and using 8.10 is fine. Although there are tons of improvements in 9.2+

Backprop is a neat library. However, I guess its use case is if you actually don't want to go for anything standard like Torch or TF (perhaps for research?) For instance, if I were to use something like Accelerate for GPU acceleration, or some other computation-oriented library, then I would mix it with Backprop. Previously, I have benefited from Backprop in a ConvNet tutorial and I liked it.

Wait, really? Here's a conversation I had with him: https://github.com/AccelerateHS/accelerate/discussions/528

I know Accelerate is being developed at Utrecht University in the Netherlands. You can look at publications by Trevor McDonell to get a taste of what they are doing.

I’m personally a big fan of http://www.acceleratehs.org / https://github.com/AccelerateHS/accelerate-llvm

Looking for a few projects that make use of it, I found accelerate, hawk, polysemy and pretty-simple, so I'll be interested to poke around in their code and see how they have things set up.

There's accelerate for GPU computing and hmatrix for bindings to BLAS and LAPACK.

I'm toying with the idea of replacing vector values with vector generators, where e.g. v1 + v2 is not evaluated to a new vector, but to a vector program. This is similar to the approaches of Accelerate and TensorFlow. On the flip side, I don't think I could get rid of the overhead, and I expect much smaller computation loads than aforementioned libraries, so overheads could be very significant. The added benefit of using vector generators is that the generator could not only be evaluated, but also be turned into a Latex formula.

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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