Toward Modern Fortran Tooling and a Thriving Developer Community
arxiv.org
arxiv.org
(I have never used Rust for anything but HN seems to love it)
I'm not saying you are wrong, but I wonder what metric you are using.
In most of the HPC codes I've worked with there are no complicated memory management, most of the things are arrays usually declared and allocated at launch time and that's it, no more memory management after that. So I'm not sure what rust would bring on the table, but maybe I'm just missing it.
First, Rust's multidimensional arrays are either limited and/or difficult to use. Fast, flexible, and ergonomic multidimensional arrays and arithmetic are essential for HPC. They are possible with Rust, but my two favorite Rust books not mentioning them suggests to me that they're not the focus of the language. This may or may not change in the future.
Second, Rust may be too complex to learn for scientists who aren't paid to write software but to do research. Fortran is opposite--multidimensional whole-array arithmetic looks like you would write it as math on a whiteboard. While scientists can sure learn to program Rust effectively, I think most scientists don't think like Rust, but they do think like Fortran. For somebody not familiar with Fortran but familiar with Python, I'd say Fortran very much feels like NumPy.
Third, such ecosystem would be built in Rust from scratch. In Fortran, most of the value is already there, but needs to be made more accessible with better and more modern tooling. For example, Fortran's fpm (https://github.com/fortran-lang/fpm) is largely modeled after Rust's Cargo because we recognize the importance of good user experience when it comes to building and packaging software. With the recent Fortran-lang efforts, we study many programming language ecosystems and communities (e.g. Python, Julia, Rust, etc.) to find what could work best for modern Fortran tooling.
Rust can actually interoperate quite seamlessly with external libraries, and make its own libraries available to other languages. You're quite right that the support for even simple numerics in Rust isn't quite stable just yet, so it's not ready for productive use. But it can get there, to a far greater extent than C/C++.
Rust doesn't solve many problems for HPC, and HPC still often involves weird hardware targets compiling from some dialect of C or C++. A bit like embedded but without the market scale to justify tool investment. Fortran and Julia are more likely targets for a second language for the obvious reasons.
Another issue is that some HPC silicon has unusual low-level concurrency and memory semantics with no analogues in ordinary CPU architectures. At least in some cases, I suspect it would be require non-trivial modification of Rust's safety checking to allow it to work correctly on that silicon.
As long as it's properly reflected in the LLVM IR, I don't think this would be an issue.
In some systems, sophisticated memory ownership and concurrency control are implemented in the hardware. These abstractions are very low-level and are intended to transparently support fine-grained and extremely high concurrency with almost no overhead.
The memory ownership model is oblivious to the programming language's concept of such a thing. As long as the compiler is emitting the appropriate memory control instructions, there can never be a conflict no matter how many concurrent mutable references there are and this conflict resolution is nearly free. This scales to thousands of cores and millions of concurrent threads.
The issue with porting Rust to this silicon, without thinking about it too deeply, seems to be two-fold:
First, code organized to satisfy the borrow-checker etc is an anti-optimization on these CPUs because they are specifically designed to encourage you to ignore memory ownership and mutability at the code level. This enables absurdly concurrent code execution on mutable shared memory that would be difficult-to-impossible to write safely on an ordinary CPU in any language.
Second, you would probably want the Rust compiler to ignore many issues of ownership and concurrency, instead emitting appropriate memory control instructions and delegating it to the hardware, since the hardware does it much better. This seems non-trivial, particularly since the hardware model does not map perfectly to Rust's model in the details (which vary by CPU design). This also creates the case of code that safely compiles on this silicon won't compile on e.g. x86 because the safety is provided by the hardware rather than at compile-time.
Ironically, a large part of the reason these CPUs have never been commercially successful is that people don't know how to write idiomatic massively concurrent mutable code. The assumption that thread concurrency is expensive and dangerous is so pervasive that everyone writes code that tacitly assumes this even when it isn't true for the given hardware, with significant loss of performance. Idiomatic algorithms and data structures in C++ on these platforms look very different than what you would use on e.g. x86 or ARM.
The general idea is that every memory address has several semantic bits that annotate the contents on load and store. Each core has multiple independent hardware threads (128 in the case of MTA) which adapt their scheduling to those annotations on a clock cycle by clock cycle basis. You can design massively multithreaded code for these platforms with almost perfect scalability that would have catastrophically high contention and overhead anywhere else, which was the point.
There are quirks to designing software for these systems, but they don’t involve safety.
"There are quirks to designing software for these systems, but they don’t involve safety."
Parallel programming is intrinsically racey. Even if hardware could enforce safety like you're saying (which it can't, even in a single thread) it would imply a sequentially consistent memory model, i.e. the antithesis of any parallel machine.
My personnal belief is that Fortran codebases will be replaced as new programmers don't want to learn the language due to its reputation. At the moment they are rewritten in C++ (fast but so many footguns) and Python (easy but terrible performance): I am betting on Julia replacing Python in the medium term (faster, not harder to learn and designed for this particular use-case).
Rust is hard to learn so I think most scientists will not invest in it but I do hope to be wrong and that it will replace C++ in the HPC world...
Fortran is not popular with embedded, nor with distributed computing (in the internet sense, not HPC one), nor with OS, nor with UI/UX, nor with compilers, nor with computer graphics, nor with many other disciplines.
There are lots of areas which do complex, interesting tasks, and where Fortran is some dead language nobody uses.
For a new project that I would have chosen Fortran for five years ago, today I would choose Julia. But it’s still not dead. Anyone who thinks so does not read papers in computational physics, for example.
This is like the ship of Theseus, except where the ship has changed from a fishing vessel to a dreadnought.
-- Tony Hoare, winner of the 1980 Turing Award, in 1982
It’s an obscure language but maintained and used by very smart people. You just need to be a bit of a hacker to be good at it but it’s at the cutting edge of high performance programming language research.
"I was born in 77," I thought... this was trippy.
In the 60+ years since Fortran was invented, enormous strides in computer languages have made source code more readable, and 99.9% of new programmers learn on only those languages.
If you ignore all that and continue to write Fortran, you've guaranteed that almost no one except other scientists will ever want to look at your code. And probably not even them.
https://github.com/nasa/NASTRAN-95
Take NASTRAN-95, for example. It has good documentation in the form of manuals, books, and papers. If you have the mechanical background, you should be able to understand the docs explaining the implementation approach, and then you should be able to understand the code. It doesn't matter that there are no comments anywhere. Aside from some cosmetic differences, it looks pretty much like what you'd write today with MATLAB. It's perfectly readable and accessible by the target audience.
The question is, do people who use [1] and [2] use only FORTRAN for everything, even common programming tasks that any competent person could tackle? And when they have something new to write, do they continue with FORTRAN because it's what they're used to?
Fortran is like a DSL. You just write the numerical solver part with it. Everything else is just regular code.
Just like anything else, the decision comes down to whether the ecosystem has the right tools for the job. It's a deliberate decision. It's not like people are relegated to it because they don't know anything else.
No. I’ve never heard this before. It’s true that scientists don’t always write the clearest code, but Fortran, and even the style of Fortran typically written by engineers and physicists, is comparatively straightforward to read. It is named for FORmula TRANsator, after all. You will recognize the equations in the code. Fortran is inherently easier to read than C; it’s really not harder to read than Python. You can make it hard to read by doing complicated array indexing tricks, of course, and there are sometimes good reasons to resort to that.
And no one except other domain experts will ever have any interest in looking at your source. Why would they? No one except someone designing a website will ever want to try to figure out your CSS either.
What is it about Fortran’s control flow that makes you think it’s particularly hard to read? Say, harder than Python? Note that people don’t generally use computed GOTOs any more.