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> Julia is the first language I've seen in years that implicitly abstracts parallelism cleanly. What do you mean by “implicitly”? A single dot is short but not
by Someone 1mo ago
> Julia is the first language I've seen in years that implicitly abstracts parallelism cleanly.
What do you mean by “implicitly”? A single dot is short but not implicit.
I also do not see https://docs.julialang.org/en/v1/manual/parallel-computing/ https://docs.julialang.org/en/v1/manual/parallel-computing/ s mention that such map calls (can) run on multiple threads.
- Joel_Mckay 1mo agoThe abstraction does not require a lot of understanding/bodges to get performant code by amateurs: https://cuda.juliagpu.org/stable/tutorials/introduction/ https://cuda.juliagpu.org/stable/tutorials/introduction/ But I agree the shared memory Distributed Computing part of Julia still needs a lot of work. Spawning binary image instances over ssh is too fragile. =3
- deleted 1mo ago[deleted]
- Someone 1mo ago> The abstraction does not require a lot of understanding/bodges to get performant code by amateurs I agree the change is simple, and didn’t question that; I questioned the “implicitly” in the claim > Julia is the first language I've seen in years that implicitly abstracts parallelism cleanly. (Aside: I think scala does this even nicer. There, adding `.par` can make code run multi-threaded. See https://docs.scala-lang.org/overviews/parallel-collections/overview.html https://docs.scala-lang.org/overviews/parallel-collections/o...)
- Joel_Mckay 1mo agoIf you read the tutorial, the broadcast iterators and GPU kernel are often implicit. As the CUDA array type hints means you don't need to explicitly define standard operations unless doing something custom. I hope one day Julia does a cleaner version of Scala or Erlang/Elixir OTP languages. Clustering on OTP was certainly an area even seasoned gray beards tried to avoid. =3 https://youtu.be/lg-710Kk5CM?t=20 https://youtu.be/lg-710Kk5CM?t=20