Creating LFortran, an interactive Fortran compiler built on top of LLVM

https://lfortran.org/blog/2019/04/why-we-created-lfortran/

by bauta-steen • 7 years ago

86 51 7 years ago

ktpsns• 7 years ago

Using Fortran in Jupyter CNA be extremely handy to learn certain algorithms by interactive exploration. Despite all the nice and modern alternatives, Fortran is so popular in HPC world that still generations of students will have to learn it.

archgoon• 7 years ago

There seems to be a missing question in the FAQ: Why is Fortran worth saving? What are the advantages of writing code in Fortran? Why not just allow the ports to C++ to happen?

There might well be good answers to these; but it seems that if you want to make Fortran cool again, you have to provide a vision for what makes it special.

acqq• 7 years ago

Recently:

https://news.ycombinator.com/item?id=19705561#19706731

WalterGR• 7 years ago

That link is to a comment on the post

The new features of Fortran 2018 [pdf]

https://news.ycombinator.com/item?id=19705561

accurrent• 7 years ago

Modern Fortran is really nice when it comes to matrix arithmetic. I really wish it were more widespread and there were more libraries for data loading into the language. Having written a lot of CV/Robotics related code in Python and C++, I find that often there are errors in coding which you may not catch till runtime. For instance matrix dimension mismatch will not be caught in OpenCV or in Python till you actually execute the code. On the other hand, these things can be hard coded into Fortran 2003 and the compiler will perform the checks before you shoot yourself in the foot.

cbcoutinho• 7 years ago

An important advantage that doesn't get mentioned a lot is that modern Fortran (imo f90+) is much simpler to write and reason about that C++, precisely because it limits what you can actually do in the language. That opens up the possibility for engineers and scientists to contribute to writing HPC code. If everything was in C++ then the ability to contribute would be available to a smaller number of capable programmers, and that's where Fortran still has a reasonable niche

hdfbdtbcdg• 7 years ago

I'm going to take this in good faith and assume that you are thinking of old school FORTRAN77. Modern Fortran (90 and onwards) is a very different language in many ways whilst maintaining high backwards compatibility. You should check out modern versions of the language and code examples.

Fortran is a language more suited for numerical computing than C++. Modern Fortran is simpler, more maintable and more easily optimised than C++. Yes one could pick a subset of C/C++ and write fast numerical code but it would require more work and more discipline than using modern Fortran.

zorked• 7 years ago

Where does the Modern Fortran community congregate? Any websites or mailing lists?

sampo• 7 years ago

What are the advantages of writing code in Fortran? Why not just allow the ports to C++ to happen?

A physicist or engineer, already being familiar with Matlab or Python, can pretty much pick up Fortran in about 2 days to 1 week, get to be productive, and the compiler will generally generate fast code. Try to teach C++ to a domain specialist, who doesn't have too much extra time to study programming language syntax and quirks, because their main motivation is working in their domain?

If the scientific code was in C++, some scientists would probably never become fluent enough, and the workflow would probably deviate towards a division where scientists prototype their ideas in Matlab or Python, and separate technical programmer personnel implement it in the production code. The turnaround time from ideas to seeing results would be days instead of hours.

enriquto4• 7 years ago

Why not just allow the ports to C++ to happen?

I see two reasons why C++ is not a suitable language for domain specialists in numerical computing.

1. Programming in C++ requires a certain discipline to understand what is going on, for example to not accidentally make useless copies of huge arrays. In fortran you do not need to learn such a discipline because everything is explicit.

2. Fortran natively supports multidimensional numeric arrays. In C++, not really; you need to use "libraries" whose evolution is independent to that of the language and may become unsupported. A few years ago, everybody said to use "blitz", somewhat later it was "eigen", now it probably is something new. The C++ numerical code that you may write today will probably be obsolete in a few decades. Yet the fortran code will be alright. It is "eternal".

alexhutcheson2• 7 years ago

Eigen is still the best-in-class C++ library for linear algebra as far as I know. It's been around for a while, is still actively developed, and I don't think there's a risk of it becoming obsolete anytime soon.

That being said, the learning curve for someone not familiar with C++ to write code using Eigen is much higher than writing the same code in Fortran. If you're not already a C++ programmer, you just want to do some math, and Numpy/Matlab are too slow or constrained, then Fortran is a solid choice.

melan133• 7 years ago

I see two reasons why C++ is not a suitable language for domain specialists in numerical computing.

Have you heard of MPI or even OpenMP ?. They can be used to accelerate fast computation in C++ or even work on domain-specified parallel execution.

bachmeier• 7 years ago

A few years ago, Scott Meyers gave a keynote at the D Language Conference. It was titled "The Last Thing D Needs". The answer given at the end was, "Someone like me". It's a bad sign if your language is so complex that it needs someone like him to write books on how to not massively screw things up.

Fortran does not need Scott Meyers. And that's a good thing, because those who program in Fortran are typically not full-time software developers, they are individuals with other things to do that would not ever invest the time to write "proper" C++.

certik8• 7 years ago

One of LFortran authors here. You have a very good point. We are going to publish a new blog post answering this exact question.

In the meantime, others in this thread pretty much hit all the main points already why Fortran as a language is still a very good choice for numerical computing. It's the tooling that must improve.

atemerev2• 7 years ago

Because modern Fortran is an extremely fast, vectorized and highly optimized numerical computing language. Like MATLAB, but much faster.

C++ is a messy, unsafe, and not particularly elegant systems programming language, which can be used for numerical computing, but it is not a good idea.

betatim• 7 years ago

You can try it online: https://mybinder.org/v2/gl/lfortran%2Fweb%2Flfortran-binder/... !

C1sc0cat2• 7 years ago

Not sure what they mean by lack of support for GPU - Intel Fortan has this and I am sure others do.

certik8• 7 years ago

I am one of the LFortran authors.

Can you please point me to some documentation or examples where Intel Fortran is used to offload to, say, NVIDIA GPUs? I don't think it supports it yet.

PGI and IBM support CUDA Fortran, other compilers don't. PGI/Flang has an experimental support for offloading "do concurrent". Some compilers also support the openmp/openacc pragmas and GPU offloading. One can also call CUDA C from any Fortran compiler via iso_c_binding. None of this is ideal.

I think it's still a research problem how to best integrate GPU support in the language itself. CUDA Fortran is nice --- one thing we want to do down the road is to implement CUDA Fortran and to allow LFortran to transform code it understands into a Fortran dialect that all Fortran compilers can compile, so that one can use any Fortran compiler and still use CUDA Fortran.

The other path is to parallelize "do concurrent", but part of this is also means to extend Fortran to allow to specify the array layout, as Kokkos (https://github.com/kokkos/kokkos) allows.

That is one of the biggest problem with Fortran: people want to be assured that their code will run on modern hardware. When using, e.g., C++ and Kokkos, then there is some assurance that the code will run.

C1sc0cat2• 7 years ago

Ah I sit corrected I though Intel would it looks like they have their own ideas in that area.

xiphias2• 7 years ago

AFAIK Julia uses patched LLVM's PTX output, which I think should be done by all languages to work towards a common optimization platform. Also CuArrays uses multiple higher level NVIDIA libraries, like CudaBLAS and CuDNN.

The goals look similar to me, so it's worth to take a look at them.

ben_e• 7 years ago

This post talks a lot about creating a community. Are they doing that anywhere in particular?

certik8• 7 years ago

I am one of LFortran authors. We just open sourced it recently. Do you have any recommendations if we should create a mailinglist, or rather use something like Discourse? Or both.

Right now if you have any questions, you can open an issue at our GitLab repository:

https://gitlab.com/lfortran/lfortran

goerz3• 7 years ago

This looks very interesting! I hope that maybe this project can include a “refactoring parser”, that is, for each token in the parse tree, store where in the source code file that token originated from. Also, white space and comments have would have to be included, such that the round trip (source file -> AST -> source file) is possible without loss. I’ve long wanted to write some refactoring tools for modern Fortran, but not having such a parser is a big blocker—there’s only so much you can do with regexes.

jagger272• 7 years ago

Wouldn't writing an Antlr grammar handle this?

goerz3• 7 years ago

Does Antlr support an annotated parse tree that would allow for round-tripping? I didn't see anything in the documentation, at a quick glance. Even then, formulating a grammar (especially one that also catches whitespace and comments) seems like a pretty daunting task.

certik8• 7 years ago

One of LFortran authors here. Yes, we already have an open issue for exactly this: https://gitlab.com/lfortran/lfortran/issues/42. As you can read there, it's actually not that easy as I first thought. But I think it's very much worth pursuing.

For example, Python's AST is not round-trippable, and that forces people to write alternative parsers such as Parso (https://github.com/davidhalter/parso).

ANTLR should allow to parse all the white space. But the issue is how to represent it in the AST, see the issue for more details. One could have an AST and a parser just for this round-trippable application, but that defeats the purpose. The goal should be to have this part of the compiler so that one can trust that it parses things correctly.

Right now I am concentrating my efforts to finish gfortran compatibility (see the roadmap at https://lfortran.org) and to get first users. Once we get further along, we will tackle this problem. I am hopeful there might be a way to do this.

new4thaccount3• 7 years ago

Question:

What is a good Fortran compiler/implementation these days?

I know they have Intel and IBM and SimplyFortran as commercial software. GFortran works via GCC. Is it as good? Has anyone on here used this LFortran.

atemerev2• 7 years ago

Everybody I know in public science (physics) uses gfortran, works just fine.

new4thaccount3• 7 years ago

Good to know, thanks! I've used software compiled with the Intel compiler, but have only written some toy programs in GFortran and it seems fast enough. I like the revival idea behind LFortran though. Reading scientific C++ is much more difficult than Fortran.

septc• 7 years ago

Gfortran is very nice, but I recommend a recent version (at least ver >= 7) rather than old versions (e.g. ver 4.4, which may still be the default on CentOS 6 etc). For performance, I guess Intel fortran is probably better (but also depends on calculations).

new4thaccount3• 7 years ago

Thanks!

certik8• 7 years ago

The best open source implementation is GFortran. Just make sure you use a recent version, as others mentioned.

Regarding commercial implementations, I only have experience with Intel and NAG, both of which are very good.

Creating LFortran, an interactive Fortran compiler built on top of LLVM

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