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I am one of the co-founders of the fortran-lang effort. I did it while I was at LANL, where I worked as a scientist for almost 9 years. I think the report is overly pessimistic. Here is my full reply on the report: https://fortran-lang.discourse.group/t/an-evaluation-of-risk....


And yet, we now a compiler (Codon) that can compile Python into Fortran code, also allowing direct use of LLVM IR and ability to import C (and Fortran?) libraries.

Perhaps, interoperability is the way forward.


Yes, I lead the development of these two compilers:

https://lpython.org/ https://lfortran.org/

LPython compiles Python at the Fortran speed, since both compilers share the same internals.


So, I have a question (I didn't real the LANL paper, save for the highlights above, but I did read your initial post that you linked to).

And this isn't meant as some kind of bait, just honest intellectual curiosity (its the internet so you can't see how it presented, just read the raw words).

Simply, why is it important for Fortran to survive in this space? Why not let the field move on? Why fight this fight?


It's a great question. I think we answered it in one of our initial blog posts when we started LFortran:

https://lfortran.org/blog/2019/05/why-to-use-fortran-for-new...

Let me know if you have any follow up questions.


BLAS/LAPACK (fortran blobs) are the miracle that all AI relies on (to my understanding). I hope we keep it around and maintained.


I wonder why the cascaded triple for loop for GEMM is not just hardcoded in assembly for each architecture. And while we are at it, why is MKL > 5GB again?


Most decent BLAS implementations use some variation of the kernels in K. Goto et al. "Anatomy of High-Performance Matrix Multiplication" written in assembly.


Does this address your MKL question?[1] If so, sounds like we're two of today's lucky 10,000![0] Although for this esoterica, I'd probably reduce that to 10.

[0] https://xkcd.com/1053/

[1] https://community.intel.com/t5/Intel-oneAPI-Math-Kernel-Libr...




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