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These core counts are insane. I’m curious about any programming languages that are designed to be easily to parallelize and how they will work on these 96 core

These core counts are insane. I’m curious about any programming languages that are designed to be easily to parallelize and how they will work on these 96 core chips

mumumu 4y ago
All languages implemented with llvm-mlir. Others languages such Chapel, Halide probably will do fine
fxtentacle 4y ago
C++ has been used on machines with very high CPU core counts for a long time. By now, things like std::async for spawning tasks on a different core are even part of the standard library. Go is also really good at saturating many cores with its low-overhead goroutines.
dragontamer 4y ago
make -j works pretty darn well in my experience. Honestly, the #1 programming language for parallelization, in practice, is bash scripting. you can convert any single-threaded program into a background thread with the & on the end, and then start running something else. Yeah yeah yeah, people wanna be complicated with pthreads or async or other features. But... bash's "background" processing (&) gets a surprising amount of things done in my experience. Just spin up a program 300+ times with a & to make them all process in the background, and you're set.
- yazzku 4y ago
  I've found make -j doesn't actually scale very well when you have a ridiculous number of cores and less than, e.g., 2 GB RAM/core.
  - Avlin67 4y ago
    ram bandwidth matters in some cases, check for arrow/wide GMI3 in Genoa slides
solomatov 4y ago
Rust is very good for parallelizing code, especially if used with libraries like https://crates.io/crates/rayon https://crates.io/crates/rayon
zac_hudson 4y ago
Fortran is very well suited for highly parallalized workloads, given that all HPC tasks are basically parallelized matrix manipulations. For some reason people have the notion that Fortran is still the punch-card days, but Fortran 08 is in fact widely used and quite friendly nowadays. If you're building a very-parallel application that could use something like MPI, you should very seriously consider Fortran.
sosodev 4y ago
Go can utilize the full CPU with ease :)
IYasha 4y ago
I'd use all of them for 3D! And MSVC supports parallel compilation since, like, 2009..?
pajko 4y ago
Doing that in C is not that hard with OpenMP, you don't even have to fiddle with threads and/or manually parallelize your code. Just an example: https://tildesites.bowdoin.edu/~ltoma/teaching/cs3225-GIS/fall17/Lectures/openmp.html https://tildesites.bowdoin.edu/~ltoma/teaching/cs3225-GIS/fa...

These core counts are insane. I’m curious about any programming languages that are designed to be easily to parallelize and how they will work on these 96 core chips

mumumu 4y ago

All languages implemented with llvm-mlir. Others languages such Chapel, Halide probably will do fine

fxtentacle 4y ago

C++ has been used on machines with very high CPU core counts for a long time. By now, things like std::async for spawning tasks on a different core are even part of the standard library. Go is also really good at saturating many cores with its low-overhead goroutines.

dragontamer 4y ago

make -j works pretty darn well in my experience. Honestly, the #1 programming language for parallelization, in practice, is bash scripting. you can convert any single-threaded program into a background thread with the & on the end, and then start running something else. Yeah yeah yeah, people wanna be complicated with pthreads or async or other features. But... bash's "background" processing (&) gets a surprising amount of things done in my experience. Just spin up a program 300+ times with a & to make them all process in the background, and you're set.

yazzku 4y ago
I've found make -j doesn't actually scale very well when you have a ridiculous number of cores and less than, e.g., 2 GB RAM/core.
- Avlin67 4y ago
  ram bandwidth matters in some cases, check for arrow/wide GMI3 in Genoa slides

solomatov 4y ago

Rust is very good for parallelizing code, especially if used with libraries like https://crates.io/crates/rayon https://crates.io/crates/rayon

zac_hudson 4y ago

Fortran is very well suited for highly parallalized workloads, given that all HPC tasks are basically parallelized matrix manipulations. For some reason people have the notion that Fortran is still the punch-card days, but Fortran 08 is in fact widely used and quite friendly nowadays. If you're building a very-parallel application that could use something like MPI, you should very seriously consider Fortran.

sosodev 4y ago

Go can utilize the full CPU with ease :)

IYasha 4y ago

I'd use all of them for 3D! And MSVC supports parallel compilation since, like, 2009..?

pajko 4y ago

Doing that in C is not that hard with OpenMP, you don't even have to fiddle with threads and/or manually parallelize your code. Just an example: https://tildesites.bowdoin.edu/~ltoma/teaching/cs3225-GIS/fall17/Lectures/openmp.html https://tildesites.bowdoin.edu/~ltoma/teaching/cs3225-GIS/fa...