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Comparing Parallel Functional Array Languages: Programming and Performance

https://arxiv.org/abs/2505.08906
54•vok•2d ago

Comments

yubblegum•5h ago
Chapel got a mention in the 'Related Work' section. I looked at it a few years ago and found it compelling (but I don't do HPC so it was just window watching). What's the HN feedback on Chapel?

https://chapel-lang.org/

marai2•3h ago
If you scroll down on the Chapel-lang website, there seems to be a lot of activity happening with this language. There is even going to be a ChapelCon 2025.

https://chapel-lang.org/blog/posts/chapelcon25-announcement/

throwaway17_17•52m ago
Chapel and Lustre (a parallel, distributed file system) from Cray were funded by DARPA’s High Productivity Computing Systems program. This work, along with Fortress, from Sun, were developed explicitly to enable and ‘simplify’ the programming of distributed “supercomputers”. The work and artifacts, along with the published documentation and research is of particularly high quality.

Even if you aren’t involved in HPC I’d say the concepts transfer or provide a great basis for parallel and distributed idioms and methodologies that can be adapted to existing languages or used in development of new languages.

TL;DR - Chapel is cool and if you are interested in the general subject matter (despite a different focus) Fortress, which is discontinued, should also be checked out.

munchler•5h ago
Are these languages pure in the functional sense? E.g. Do they allow/encourage mutation? My understanding is that APL permits mutable state and side effects, but maybe they are rarely used in practice? If you're modifying the contents of an array in-place, I don't think it's reasonable to consider that functional.
zfnmxt•3h ago
Futhark, SaC, and Accelerate have purely functional semantics. Futhark has something called "in-place updates" that operationally mutate the given array, but semantically they work as if a new array is created (and are statically guaranteed to work this way by the type system).
RodgerTheGreat•3h ago
APL arrays are values in the same sense as value types in any functional language. You don't explicitly modify arrays in-place; if they happen to have a refcount of 1 operations may happen in-place as an optimization, but not in a manner which observably alters program behavior.
grg0•2h ago
Accelerate is a Haskell library/eDSL.
teleforce•3h ago
Notice that all the all the languages mentioned depends on the external BLAS library for example OpenBLAS for performance.

D language have excellent support functional and array features with parallel support. On top that not known to others it has high performance native BLAS kind of library with ergonomic and intuitiveness similar to python [1].

[1] Numeric age for D: Mir GLAS is faster than OpenBLAS and Eigen (2016):

http://blog.mir.dlang.io/glas/benchmark/openblas/2016/09/23/...

zfnmxt•2h ago
> Notice that all the all the languages mentioned depends on the external BLAS library for example OpenBLAS for performance.

That's incorrect. Futhark doesn't even have linear algebra primitives---everything has to be done in terms of map/reduce/etc: https://github.com/diku-dk/linalg/blob/master/lib/github.com...

joe_the_user•21m ago
"Notice that all the all the languages mentioned depends on the external BLAS library". I didn't notice this 'cause I don't think it's true. For example, it highly implausible that APL[1] would depend on BLAS[2] considering APL predates BLAS by 5-10 years ("developed in the sixties" versus "between 1971 and 1973"). I don't think Futhark uses BLAS either but in modern stupidity, this currently two hour old parent has taken over Google results so it's hard to find references.

[1] https://en.wikipedia.org/wiki/APL_(programming_language)

[2] https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprogra...

DrNosferatu•3h ago
Matlab supposedly is “portable APL”.

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Comparing Parallel Functional Array Languages: Programming and Performance

https://arxiv.org/abs/2505.08906
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