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Why physicists still use Fortran (2015)

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Re: Why physicists still use Fortran (2015)

#271

Post by dagw »

lostboys67 wrote: post #257
guitarbill wrote: post #205

Earlier quoted context omitted.

Trusting legacy code with few users is a dangerous proposition. My roommate was given some "state-of-the-art" code and told to run simulations with it. The only graphical output was postscript (for some reason), so every frame was 150 MiB and took minutes to dump - so usually, this was only done at the end to show the result. I managed to hack in a step which just dumped the memory of resulting frame to a file, and t…

They where not back checking against real word experiments?

They probably did at some point in the misty past. The problem is that running relevant experiments is often very expensive (otherwise they wouldn't need to do it in software) and they can only test a tiny handful of cases. More often than not your calibrating your model against another model that was calibrated against another model that was calibrated against a set of simple experiments done in the 60's.

Re: Why physicists still use Fortran (2015)

#272

Post by MaxBarraclough »

dagw wrote: post #270
MaxBarraclough wrote: post #261

Earlier quoted context omitted.

I've heard horror stories of supposedly quite serious software modelling programs behaving quite differently when their input problem was rotated a few degrees.

I've heard horror stories of supposedly quite serious software modelling programs behaving quite differently when their input problem was rotated a few degrees. Sounds perfectly normal :) The standard advice here at work for one of our modelling packages is: if it crashes try rotating your input model by one degree and try again. Most of the time that will fix it. If it doesn't fix it try one degree in the other dire…

Do people publish work based on this thing?

Is correctness somehow assured provided it doesn't crash? Or is the whole thing just a joke?

Re: Why physicists still use Fortran (2015)

#273

Post by dagw »

MaxBarraclough wrote: post #272
dagw wrote: post #270

Earlier quoted context omitted.

I've heard horror stories of supposedly quite serious software modelling programs behaving quite differently when their input problem was rotated a few degrees. Sounds perfectly normal :) The standard advice here at work for one of our modelling packages is: if it crashes try rotating your input model by one degree and try again. Most of the time that will fix it. If it doesn't fix it try one degree in the other dire…

Do people publish work based on this thing? Is correctness somehow assured provided it doesn't crash? Or is the whole thing just a joke?

Do people publish work based on this thing?

I'm sure lots of people do. We're a commercial company selling consultancy service around this software (technically around some inhouse tools built on top of this software), among many other things.

Is correctness somehow assured provided it doesn't crash?

On the whole well tested PDE solvers either crash, give answers that are off by several orders of magnitude, or give a correct answer. (If the answer they give is relevant to what you're trying to model is left as an exercise to the reader)

We're reasonably sure that, if the calculations converge, the solution provided is a correct numeric approximation (within the error bounds given) of the PDEs we're claiming to solve. We also believe that the PDEs we're using provide a reasonable balance between modeling what we claim we're modelling and our computation running in a reasonable amount of time.

Re: Why physicists still use Fortran (2015)

#274

Post by The_suffocated »

jxcole wrote: post #89

So the article basically says: 1) Some stuff is already written in Fortran so they don't want to rewrite that. I dig it. 2) It's fast (except C sometimes) but easier to write than c. Like 100x faster than python. I'm not sure about number two. With the gpu processing revolution wouldn't a python/TensorFlow stack be faster than Fortran? Am I missing something? I remember talking to someone who had worked heavily on at…

While I can imagine that neural networks are useful in predicting rainfalls or precipitations, I don’t see how they are suitable for some tasks, such as the forecasting of typhoon paths. A neural network is basically a function interpolation device. To use it for prediction, the implicit assumption is that the function it approximates is somehow well behaved, but some weather systems are chaotic. Function interpolation doesn’t seem very useful in this regard.

From what I see from googling, cyclone tracks forecasting by neural network is definitely an active area of research, but apparently they aren’t practical yet. The lady you talked to were probably working on some chaotic systems like this.

Re: Why physicists still use Fortran (2015)

#275

Post by sundarurfriend »

> researchers at MIT have decided to tackle this challenge with full force by developing a brand new language for HPC called Julia

This and the linked news piece [1] from MIT News sound pretty weird to me. The OP article probably takes the bit about "researchers at MIT" developing Julia from the MIT News page, so that's the real source of the issue - the MIT News pages seems to have been written with weird biases, making it sound like a primarily MIT project that other people have just tacked a few things on to. And then there's:

> A few years ago, when an HPC startup Edelman was involved in [...] was acquired by Microsoft, he launched a new project with three others.

That to me sounds like an implication that Edelman was the one to initiate the project and take in the others. They seem to be writing from the usual academic bias of "the senior faculty gets the credit even if the actual work is done by the PhD/graduate students". Edelman was Bezanson's thesis advisor and a crucial part of Julia's history, but this article seems to be downplaying the role of the other core contributors and the open source community.

I had assumed university news, at least in such technical topics, would be more reliable and less inherently biased, learned something new today.

[1] http://news.mit.edu/2014/high-performance-computing-programm...

Re: Why physicists still use Fortran (2015)

#277

Post by cm2187 »

guitarbill wrote: post #251
EvgeniyZh wrote: post #248

Earlier quoted context omitted.

Another problem is that most people who wrote this code aren't programmers - they don't write clean code, no tests, etc. They don't really know those are important. Sometimes the code that is used and updated for years looks like a dirty prototype. I don't know what can be done about it except hiring programmers to write code, which wouldn't be either easy or cheap

Well, I guess one moral of the story is that doing image manipulation is easier in Python than Fortran, so it's again using the right tool for the job. And I think the push for Physicists to use Python if possible is good, as the learning curve is less steep. And once you have that tool at your disposal, you might use it more often (instead of Excel). I can see many projects being improved by providing a Python pre-p…

I would have thought a more natural candidate for scientists would be languages like Java or C# rather than python: you get both a language that is relatively easy to write, with no memory to manage, no hornet nest of pointers on pointers, nice debuggers, lots of 3rd party libraries, while a the same time getting the performance of static typing / running compiled code once the program has been JIT (which for long running code is a negligible cost). It's never going to be as fast as a C++ or Fortran, but you get a compromise between performance and ease of use, while I understand python is going the other extreme, i.e. all ease of use at the expense of performance. Doesn't matter for a simple simulation, but for some large data analysis projects, I assume it could make a big difference.

But that doesn't seem the case. The choice seems to be between python and c++/fortran. Does anyone know why?

Re: Why physicists still use Fortran (2015)

#278

Post by wisty »

LeifCarrotson wrote: post #164
osteele wrote: post #112

Earlier quoted context omitted.

"Within a month of his arrival, Randy solved some trivial computer problems for one of the other grad students. A week later, the chairman of the astronomy department called him over and said, “So, you’re the UNIX guru.” "At the time, Randy was still stupid enough to be flattered by this attention, when he should have recognized them as bone-chilling words. Three years later, he left the Astronomy Department without…

I am unfamiliar with the passage. But it describes two options: 1. "A staggeringly comprehensive knowledge of UNIX", three years of domain-specific education, and a network of people who trust you to--no, who depend on you to be able to get things done with that knowledge 2. A piece of paper just like everyone else in the department. And while the former might be more difficult to make use of, I think it could be muc…

> three years of domain-specific education, and a network of people who trust you to--no, who depend on you to be able to get things done with that knowledge

This might be the case, but he's now like the janitor who unplugs the toilet. His work may be appreciated, and it might be necessary, but it's not respected or well remunerated. I guess he gets a little bit of respect for unplugging more theoretical pipes, but a little respect isn't a PhD.

Re: Why physicists still use Fortran (2015)

#279

Post by asfdsfggtfd »

cm2187 wrote: post #277
guitarbill wrote: post #251

Earlier quoted context omitted.

Well, I guess one moral of the story is that doing image manipulation is easier in Python than Fortran, so it's again using the right tool for the job. And I think the push for Physicists to use Python if possible is good, as the learning curve is less steep. And once you have that tool at your disposal, you might use it more often (instead of Excel). I can see many projects being improved by providing a Python pre-p…

I would have thought a more natural candidate for scientists would be languages like Java or C# rather than python: you get both a language that is relatively easy to write, with no memory to manage, no hornet nest of pointers on pointers, nice debuggers, lots of 3rd party libraries, while a the same time getting the performance of static typing / running compiled code once the program has been JIT (which for long ru…

1. Nice bindings already exist for numerical libraries in python. It is more accurate in some ways to say that scientists are using a domain specific language based on numpy than they are using python.

2. The choice is either "fastest possible" or "I just don't care how long it takes" - for either development or run time. There is usually no middle ground.

3. Tooling - the scientist is most likely using a text editor not an IDE (especially when they start working). Fortran and python are both low enough on boilerplate to not need IDE support.

4. Abstraction level. Scientists in general don't bother care about abstraction level at all. Procedural programing's abstractions are usually more than enough for them (and may actually be the correct level for some numerical work - think cache misses vs. hits).

5. Some areas of science do use Java (check out imagej for example).

Re: Why physicists still use Fortran (2015)

#280

Post by dagw »

dralley wrote: post #233
ptero wrote: post #150

Earlier quoted context omitted.

Yes, C++ does not buy you that much. Sure, it is a more modern language, but when used by scientists (not software engineers) the advantages are hardly earth shattering. Much of complexity is hidden (as it should be) into libraries. IMO, Python stands a better chance of breaking the Fortran's lock on physics related computing. Give it a few more years and enough numpy-based libraries might make Python a real competit…

>Give it a few more years and enough numpy-based libraries might make Python a real competitor. A lot of Numpy is written in FORTRAN.

Hardly any of numpy is written in Fortran. It's basically all C and Python. Numpy does however links to and provide wrappers around some existing Fortran libraries.
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