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Etterererererer OP t1_jabbuok wrote

Reply to comment by royalemate357 in [P] [R] Neural Network in Fortran! by Etterererererer

Many neural networks that are used in like physics chemistry and other fields are often written in Fortran and then trained on supercomputers. It’s mainly because Fortran is really easy to parallelize and even has built-in support for linear algebra. additionally most supercomputers run on Linux and to my knowledge fortran runs really well on Linux OS. I am also planning on learning C/C++ down the road for similar applications but currently I just think Fortran is more fun lol and again I’m doing it just for the fun of it.

Edit: this is to my knowledge again I am EXTREMELY new to Fortran but atleast from my google research it seems to be used in a range of large scale projects.

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weeeeeewoooooo t1_jabotnq wrote

There is a lot of outdated code in physics and chemistry. It is in Fortran because no one wants to rewrite it.

It is actually more difficult to parallelize fortran code on supercomputers compared to modern distributed computing libraries like HPX, which is a C++ library. Fortran will also perform much worse as you won't beat the DAG schedulers in HPX.

Fortran is also slower, because you can't write generic high performance code. This is also true of C. You need something akin to templating which allows you to optimize expressions at compile-time like you can do in C++. You need generic code for all but the most trivial operations, else it is very difficult and time consuming to build more complex operations like those that build into neural networks.

Additionally, CUDA is native C++ (and again can be optimized generically which you can't do once you have C or Fortran APIs), so if you want to seriously take advantage of vectorization, you should look at using GPUs. It is likely that if a place has a supercomputer, they probably also have a GPU cluster.

All these reasons are why C++ is the king of performance programming.

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CKtalon t1_jabg9b5 wrote

Fortran is pretty much a dead language though. People still use Fortran in their DFT computation only because no one has ported them over to a modern language. Since you are picking up a new language, just pick up Python to get the required support or you will be having a lot of trouble finding help.

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Etterererererer OP t1_jabgx28 wrote

I would say I already have a good understanding on Python and have done many Deep learning and machine learning projects in the past using tensorflow and even making my own crappy framework. I’m more just looking towards learning more about the bare bone fundamentals of how to parallelize neural networks by making them from scratch so I can best understand it all.

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karius85 t1_jabp9sc wrote

I would look at C++ frameworks. Never heard of any sane implementation in pure fortran.

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tysam_and_co t1_jabuusq wrote

Hey, do what works for you, kid. (I say that respectfully). If you're passionate about writing a neural network in Fortran...

...then do it. If you follow one of the practical solutions here and it makes you miserable, then don't do that. There are easier ways than Fortran, but if you accept that it's not necessarily the most efficient or easy, like, at all, way to go about things, and this is a passion project for fun, then that's something you can learn with. Whatever fills your sails and gets you _learning things_.

I think you want to ask yourself why Fortran specifically (it's a low level language, you like it in particular, older supercomputing history, etc?)

Once you have written down all of the _why_ for what you want to do, make sure that you've chosen the right building blocks (the _what_) for it.

I hope this helps you on your journey. Much love and care from this fair enby over here! <3 <3 <3 :DDDD

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Etterererererer OP t1_jabvoad wrote

That’s a very good idea. I probably will do that, after getting a lot of these responses I’ve been thinking about just doing C++

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Zatujit t1_jac6493 wrote

Maybe it is just that physicists love Fortran. Probably for good reasons and that do not have something with deep learning. Sometimes you stick to what you really know well. I have a math background and I was really surprised when a physicist friend said he coded in Fortran...

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