Me of fifteen years ago would love this for lab classes on, e.g., classical molecular dynamics.
The computational load of these simulations is, in modern terms, not large, so Emscripten-compiled simulations would be plenty fast enough, and deployment of web pages in a computer-lab environment (or for people working from their dorms!) is way easier than the alternatives.
So there are real, non-trivial applications. You wouldn't want to be running ABINIT or whatever in your web browser, but GROMACS on some small system? Not unreasonable.
For classroom study - wouldn't straight javascript suffice for basic classical molecular dynamics? That approach would also make it much easier for students to play around with different update algorithms, temperature control, etc.
Yep, I will definitively try to compile my Rust MD code to emscripten and give it a look! There are quite a few JS molecules visualizers, so the outcome can be pretty nice for teaching.
In a past job we had some ada code in our codebase that was just awful looking. It was machine translated from fortran to ada. From what I could tell, it was just a few files that were translated and then fixed up and checked in before they gave up on that conversion project.
we nicknamed such code "ada-tran"
Thus the I feel the new translated code should be called "Forscript"
To my understanding, Emscripten thankfully does not attempt to keep the JS sane for humamn consumption at all. It's basically a higher level assembly language that targets a browser. What Emscripten does is more compiling to JS, not translating to it.
> we nicknamed such code "ada-tran"
If Fortran -> Ada is ada-tran, then wouldn't Fortran -> Javascript be Javascran? :)
This is the same LLVM Fortran project as discussed two years ago in the thread you linked. Two years ago was just an announcement, now they are publishing some open source code (which might or might not actually work, for some definition of "work", I haven't tried it).
So the backstory is that Nvidia bought PGI, a company which produced a C/C++/Fortran compiler suite which was moderately commonly used in HPC. Then they took the Fortran frontend from the PGI suite and bolted it to LLVM and open sourced it as flang.
Yes indeed. Perhaps you are not the target demographic, but there are a lot of algorithms which can be interactive and have a visualization for example
There are many useful computational model written in fortran that would be absolutely useful if they can be run in a browser, simply because telling the user to compile them on their own is impossible unless they're familiar the toolkit themselves.
For example, it would be incredibly useful if you can run various US EPA's dispersion models right in your browser.
I've heard a horror story of someone doing signal processing in node.js. It'd be nice to use a well tested Fortran FFT routine - there aren't many .js libs for doing numerics AFAIK (disclaimer: not a js programmer)
Except for some obscure edge case, it wouldn't really be a great idea to run them on a JS vm... FORTRAN code still in use is computation-heavy stuff for use cases where speed makes a big difference, the performance hit of a VM wouldn't be acceptable.
There is a lot of battle-tested Fortran out there. If you're for one reason or another in a situation where you have to deploy to the web platform and you need some numerical algorithms, I can imagine it could make sense to pick some routines from netlib instead of reinventing the wheel.
Hmm, seems you're right. Though that seems to be some fallback thing which is used if the real thing isn't found during the build. At least on Ubuntu 16.04 the lapack_lite module links against the real (Fortran) lapack library:
If you can run the processing on the client side, then you can scale up much, much easier and provide whatever service much cheaper, or even free. Eg, you can provision fewer servers, no need to build a processing queue or load balancing setup, if the input is provided by the client then you use less bandwidth by avoiding a round trip on that, etc.
Cluster-scale calculations? Probably not. If we could /easily/ port FORTRAN code to JavsScript then you could do this [1] kind of thing with a lot less manual re-implementing.
Hmm, I guess it depends on the application whether that's a good idea or not.
Or rather, before flang + emscripten/wasm/whatever, there was no reasonable way to run Fortran in a browser; now there is. Application developers can then choose whatever is most important for their application.
Disclaimer: my web dev experience predates the "modern web" with SPA's, fast javascript runtimes, html5 etc.
You have accelerated graphics on any modern browser, on any OS, on any CPU architecture. I'll rag on JS as much as the next person but you have to admit 1) that's a pretty big feat 2) it's not an apples to apples comparison.
This is not true, because I have a couple of devices with pefectly fine working OpenGL ES 3.x GPUs where the browsers either don't support any form of WebGL or the FPS are a single digit number.
The thing is that pretty much all Fortran is "some obscure edge case". Otherwise it'd be written in something that's not Fortran.
Another example, I know the guy who maintains NIST's fluid properties solver (back in the day he worked at a subcontractor for NASA characterizing superfluids in fuel tanks). It's ancient Fortran compiled to a windows .dll and wrapped in VB in an excel file. It's super battle tested (being used for nearly 30 years), and it'd be awesome to be exposed in the browser.
...and then your Javascript code will run on node.js, and the instance will be Dockerized for ease of deployment to a VPS. How many layers of abstractions are there?
and also nginx reverse proxy m8, with apache compiling instances to be deployed automatically to scale using latest gcc7 with 03 optimization for robustness against DDOS
One other nice benefit to this project is determining name mangling schemes when linking to other Fortran projects using a pure LLVM toolchain.
Say we want to use BLAS and LAPACK in a C/C++ project that uses clang as the compiler. All we have to do is write the appropriate prototype for the LAPACK function in C and then link to BLAS/LAPACK. That said, there are four different name mangling schemes that the BLAS/LAPACK could use, which are combinations of upper case or with trailing underscores, e.g. gemm, GEMM, gemm_, and GEMM_. Both Autotools and CMake will determine the mangling scheme for us, but they need a Fortran compiler to determine that. Right now, that's almost always GFortran. With this project, it could ostensibly be Flang, which would eliminate the need for GCC to determine the mangling if a pure LLVM toolchain is desired.
The C ABI is pretty stable, so if you're using LAPACKE you shouldn't need to worry about which Fortran compiler was used to compile LAPACK.
Other than that, AFAIK the most common name mangling these days is lowercasing + trailing underscore. That being said, for "modern Fortran" with modules, OOP etc. the issues are vastly more complex and AFAICS there is no commonly agreed upon ABI.
Actually, modern Fortran is a pretty decent numeric programming language, comparable (if not superior) to MATLAB, and much faster than it. The only problem is that finding documentation on modern programming idioms and styles is complicated among MEGATONS of legacy code lying around literally from Cold War times (still perfectly working, of course, so if you urgently need to write a Monte Carlo solver for neutron transport equations — you got it covered).
I'm really interested in Fortran because I love super down-to-the-metal optimisation stuff, but I have no meaningful use-case for it. Where would you recommend someone start if they wanted to get into modern Fortran?
Fortran isn't really used much for down-to-the-metal optimisation. It's not at all built for the low-level tricks common in C, but rather expects you to program at a higher level (notably with first-class arrays), after which the compiler will (hopefully) do its magic. Although in practice, most Fortran code is not particularly optimised (or optimised for long-dead machines), and could be much much faster.
So, if you want to do down-to-the-metal optimisation for Fortran, you want to write a Fortran compiler, not a Fortran program.
Most Fortran programmers are scientists or engineers. So you could become a computational scientist. Materials science (density-functional theory, computational chemistry), climate/weather modeling, computational mechanics, fluid dynamics, etc. are all fields where Fortran usage is strong.
If you're not interested in science, I guess you could contribute to GCC or LLVM and try to make Fortran benchmarks run faster.
To add to some of the answers, Fortran's strength is that a scientist can write an algorithm for some numerical routine fairly easily and the output code will be highly optimized.
This is possible because you don't have to deal with all the weird edge cases you have in C/C++ like aliasing. The language is fairly simple so it can be optimized to death and it's easy to add things like automatic vectorization, etc.
Compared to C++ / C even old fortran is suprisingly pleasant to use, when you want to implement simple numerical algorithms, it feels a bit like a barebones matlab
Any time you want to do a big numerical computation fast. I suggest that, instead of looking at old fortran code, you make sure you are learning to use array fortran, which is supported by gfortran. You can do operations on arrays directly (makes the code concise), and get multi-processor parallelism with very little effort.
Due to it's simplicity, due to it's support for atomics, and due to some traditional Fortran features (modules, F9x object-based, build-in array support), Coarray Fortran does already allow to break it's own limits for development of more sophisticated parallel logic codes: https://github.com/MichaelSiehl/Atomic_Subroutines--How_the_...
Is it sensible to implement math heavy part of C application in Fortran and call it via FFI? Or is -Ofast enough in most cases without the costs (multiple, because performance may be one of them) of FFI?
Not for speed reasons alone. It's certainly possible to tune C code to have roughly equivalent performance to Fortran.
The real appeal for using Fortran is that for beginners it's a much easier language to pick up than C (much less footguns, for one), and that working with (dense) multidimensional arrays is very nice, roughly on par with high-level languages like numpy, R, or MATLAB.
To expand on this, I think the footguns you're talking about are specifically performance related.
Just an example: How do you specify a fast readonly array in C? If I recall correctly it's
restrict const * const int my_array[n]
In Fortran?
integer(4), intent(in) :: my_array(n)
Not even starting with array ops, multidimensionals, array slicing etc., that's already a big win in readability. Having a pass-by-reference language by default is refreshing for HPC purposes.
That being said there are disadvantages compared to C. Biggest one IMO is the lack of inline declarations, which especially makes privatizing things in OpenMP and OpenACC code a bit more tricky, i.e. it needs to be explicit.
Gentle reminder to future language designers: whatever else you decide to take from C, please, please don't take declarator syntax. This thread showcases why.
Actually one of the reasons that Fortran is fast is not that the code you write in it is inherently faster than C. It is more that the idiomatic way of programming it is fast. This is not necessarily the case in C, where it is quite natural to use pointer structures, while in Fortran you would in most cases use large arrays that consists of continuously allocated memory instead.
It is of cause possible to do that in C as well, but it is not as natural. You can use pointer structures in modern Fortrans as well (as in F90 and later). Again, the difference is that idiomatic Fortran often lead to faster code.
One real difference is that Fortran do not allow aliased memory in arrays unless that is specifically declared using the "equivalence" statement, while in C and C++ that is the default assumption. This allows for some optimizations that can't be done safely in C. In C99 and later, you can use the "restrict" keyword to state the opposite, namely that an array is not aliased, but usually you won't do that outside of tight loops if at all.
I used to work in a government research lab. We mostly used GFortran instead of Intel for political stability reasons. Your budget can change drastically from year-to-year (even if there's no change at the congressional level) because every dollar has to go through the entire bureaucracy before it makes it to your cubicle. The budget wars are constant at every level in the org pyramid.
GFortran is free, and always will be. Whether we can pony up for Intel is completely unknown 5 or 50 years from now. And a lot of people in the Fortran world are ultimately funded by Uncle Sam.
so your counting your power bill / cost per gflop when you do your budgeting and exactly how many licences would you need? you'd be building on a server not on everyone's desktop pc right?
Not OP, but In these types of scenarios power/utilities probably come out of someone elses budget with at least one layer of competing bureaucracy walling off anyone from asking that question, let alone investigating it and factoring it into the decision making process..
I know quite a few Fortran programmers, most of whom will avoid Gfortran because it lacks the features and performance of the commercial alternatives. When you consider that a lot of these people write code for large scale computer clusters and/or supercomputers, the cost of something like Intel Fortran pales in comparison to the cost of the hardware. I suspect this is a big part of why Gfortran is not of the same caliber as GCC
The ability to compile to LLVM IR, for one. Yeah, you could use dragonegg (and I have), but dragonegg is unmaintained and the last working versions are pretty old (LLVM 3.3 and gcc 4.8).
