
Bringing Tensor Cores to Standard Fortran - pjmlp
https://developer.nvidia.com/blog/bringing-tensor-cores-to-standard-fortran/
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tikej
It’s nice that fortran still runs strong in scientific computing. In my
opinion it’s probably still the fastest route for scientist to go from 'hello
world' to HPC on clusters.

If it wasn’t for Julia I would be using it as my primary language, alongside
something to manipulate strings, plot and work with data more nicely.

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awild
I had to dig through some fortran code for my thesis and still don't quite
undedsta what exactly it is that makes it so appealing or faster than the same
programs written in C/C++. The syntax seemed excruciatingly old and verbose.
Is there actually a reason for its dominance except its Og status?

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m_mueller
In order of importance:

1) built-in performant multidimensional arrays, incl. array expressions
(similar to Matlab). Built-in nature means basically all numerical libraries
can easily talk to each other since they all use compatible data structures.

2) Fortran's syntax is performant by default, so scientists don't have to know
as much to produce fast code. Best examples are default pass-by-reference and
allocatables instead of pointers, which makes them non-aliased.

3) along same lines: intent system

C: restrict const * const double x ...

Fortran: real(8), intent(in), allocatable, dimension(n,m) :: x, y, z

The second code is simply more expressive about what you think about when
doing numerical programming.

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awild
The fortran syntax is pretty odd but in contrast to c makes it's very clear.
Thanks for the explanation!

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shepardrtc
Please correct me if I'm wrong, but this seems like this lets you port
existing Fortran programs over to using the GPU with minimal effort. And if
that's the case, wouldn't this be pretty big?

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danmg
Yes. But this is not new. These features have more or less existed in this
compiler since it was PGI Fortran.

