Slashdot Mirror
Should Undergraduates Be Taught Fortran?
Mike Croucher writes "Despite the fact that it is over 40 years old, Fortran is still taught at many Universities to students of Physics, Chemistry, Engineering and more as their first ever formal introduction to programming. According to this article that shouldn't be happening anymore, since there are much better alternatives, such as Python, that would serve a physical science undergraduate much better. There may come a time in some researchers' lives where they need Fortran, but this time isn't in 'programming for chemists 101.' What do people in the Slashdot community think?"
i spoke to someone studying engineering in 1990 who was being taught fortran. they were using a mathematical library that would solve partial differential equations, by presenting the user with the actual mathematical formulae to them.
these kinds of libraries are staggeringly complex to write, and they have been empirically proven over decades of use to actually work.
to start again from scratch with such libraries would require man-centuries or possibly man-millenia of development effort to reproduce and debug, regardless of the programming language.
so it doesn't matter what people in the slashdot community think: for engineers to use anything but these tried-and-tested engineering libraries, that happen to be written in fortran, would just be genuinely stupid of them.
If all you need is to crunch numbers, Fortran is a good choice even today.
It might not be the best language to introduce someone to computer science, but it is very powerful for anything that has to do with matrix operations.
A few years ago in a physics graduate course we had a simulation project which left the choice of language to the student.
We compared performance between implementations in C C++ and Fortran.
Fortran was consistently faster by a big margin.
It's also very easy to learn.
That said, I do most of my coding in C.
As of Postgres v6.2, time travel is no longer supported.
google Fortran tutorial download
How new does the book need to be for the language standard when it hasn't changed much in 2 decades? It's a simple, easy to use tool for serious engineering.
Fortran has tons of libraries specialized to whatever scientific field you are working in, and is unavoidable in high energy physics especially. Of course, most of these can be wrapped in C and then used in whatever high level language you like.
I did my graduate studies in a university electromagnetics lab. Two of the professors main research area was FEA. By default, we still ended up learning some even if it wasn't our research area. Most the students were pro Matlab, where as the professors were pro Fortran. As a result, if you were doing FEA for your research, you were learning fortran. If you are doing small simulations, then go ahead and use Matlab, since it will be easier to code and debug. Once you start creating 3D meshes, the number of unknowns becomes huge. At that point a compiled language is a better choice. At least fortran has complex numbers native to the language, so its implementation is a bit more elegant than say C/C++.
Fortran is still one of the best, fastest, most optimized tools for number crunching.
Agreed.
It's also very easy to write simple programs in it.
This is a strength of Python too.
No way I'd use Python for serious large data set numerical calculations.
It's not either/or, with F2Py you can put your inner loops in Fortran, and deal with the higher level abstractions with Python. So you get fast number crunching and all the 'batteries included' too.
0) A lot of legacy code people still have to work with is written in FORTRAN. Sad but true.
1) Many very optimized libraries available. Check if your language du jour has an implementation of a routine for solving a linear system using BLAS. That provides a huge improvement.
2) Many libraries are in fact only available for FORTRAN. For calculating the eigenvalues of a sparse matrix, there is only ARPACK (for Fortran), Arpack++ (a kludgy C++ interface to the very same FORTRAN library), and Matlab's "eigs" (a Visual Basic-style interface to the very same FORTRAN library).
3) Very expressive. For instance, you can reverse the entries of a vector of complex numbers in a single compiler instruction. This is a toy example, but for more complicate stuff this expressiveness pays: the compiler has an easier job in understanding what code can be safely optimized and what cannot. More complicate stuff involving e.g. C++ method calls suffers in terms of pointer aliasing problems and similar stuff. Of course you may write the very same thing in C or machine code, but for 99% of the computations you would use the "standard" interface to vectors/arrays of your languages and forget about this sort of micro-optimizations. A good commercial FORTRAN compiler (forget about gfortran, sorry GNU but sadly it's true) does this automatically.
4) FORTRAN 95 is not a punch-card language anymore, it has most of the fancy modern stuff if you wish to use it. While "bad programmers can write FORTRAN in every language", good programmers can write well-factored and perfectly readable FORTRAN code.
Nevertheless, I do matrix computations, and still I try to avoid it as much as I can. Most people in our field use MATLAB (which is essentially a Visual Basic-style interface to most of the awesome number-crunching FORTRAN libraries) even though for tight "for" loops its performance sucks. If performance is mission-critical, you may write FORTRAN subroutines and call them from MATLAB, and that's very convenient. Python still lacks many of Matlab's features, its only advantage is being Free Software.
BTW, a very ill-advised design choice of Python: http://www.python.org/dev/peps/pep-0211/ Ask any numerical analyst to know why it is a terrible idea to solve a linear system with inv(A)*b. But make sure you have at least half an hour free.
My first program:
Hell Segmentation fault
I'm an engineer with a large aerospace firm. All our major programs are in Fortran and have to be used, modified, and maintained. I remember a few years ago we hired a new grad from MIT; she had studied Basic, Pascal, and C; so of course we had to teach her Fortran so she could do her work. The engineering world is heavily dependant upon Fortran, and to not know it puts you at a huge disadvantage.
To make a long story short; solving Ax=b by calculating x=inv(A)*b is a terrible idea because calculating inv(A) is an inherently difficult thing. While it would be extremely useful to have inv(A), it's not strictly neccessary to obtain in in order to solve Ax=b.
At the most basic level, the technique which most would be aware of to solve Ax=b is basic Gauss Elimination, with an augmented matrix and back substitution. In fact, this is often the very first thing people learn how to do in a linear algebra course. It isn't much better than finding the inverse, but it saves a lot of computation in the long run.
Of course there are many other techniques. Happily however, most packages can now automatically make the best choice on which technique to use, depending on the properties of A. In Matlab and Octave, it all boils down to using the left division operator like so
x=A\b
instead of the inverse calculating
x=inv(A)*b
Using the first command, Matlab and Octave will choose a technique that best suits the matrix A. This page has a list of all the techniques that Matlab can use to solve the linear system. To my knowledge, Octave has a number of techniques as well, but I'm not sure if it's as comprehensive as Matlab. Also, Octave's left division operator has been known to have bugs.
And to return to the main topic, Octave and Matlab both use LAPACK extensively, which is written completely in Fortran(and based on BLAS). There's really no other language for linear algebra.
May the Maths Be with you!
I'll add a "+2" to this. My background is in Astrophysics, and the coding there is largely done in Fortran. The friends and people I know spread between 4-5 different universities all program in Fortran. I'm moving into Geophysics/Atmospheric/Oceanic sciences, and all that work is done in Fortran. From fluid dynamics to stress fault calculations, Fortran is the de facto language.
To be clear, we're not talking about programming here. We're talking about math. Pure, hardcore, overwhelming math. The crunching of terabytes of data. Matrices with millions upon millions of cells, being combined with more of the same.
If we were talking about pure programming, Fortran is a terrible language. What we're talking about here is automating massively complex mathematical calculations on enormous amounts of data.
Velociraptor = Distiraptor / Timeraptor
I think you are mistaken regarding what most undergraduate science students actually do (they are not maintaining/upgrading old fortran libraries). Most of the high performance capability that undergrads need involves matrix computations, FFTs, convolution, etc., all of which are included in the python numpy/Numeric module (which is a wrapper around fortran libraries, so they're just as efficient). And since they'll likely spend as much time analyzing data as producing it, python + numpy + matplotlib is a perfectly suitable solution.
I'm not suggesting that fortran isn't of value to some scientists in some situations but many science students will never have to touch fortran code unless they're forced to take a class that teaches it. As you said: "They're being taught to program as a mere tool for the important stuff being taught." Which is why it makes sense that their intro language is one that is easy to learn, supports multiple programming paradigms, has efficient numerical libraries, has easy-to-use visualization tools, an interactive interpreter, and can be used as a general purpose programming language. And while I personally prefer python for a high level language, there are others that could serve the same purpose.