3 ms·

Your last sentence applies equally to Fortran. How would you compare Julia and Fortran?

realo 2y ago
Julia uses LLVM for its jit architecture, if I recall correctly. That makes it a good candidate for running well on ARM platforms (think embedded data processing at the edge). Not sure how well fortran does on ARM.
- pjmlp 2y ago
  Fortran does quite well on almost any major CPU since 1950's, including GPUs. Actually one of the reasons CUDA won the hearts of researchers over OpenCL, is that Khronos never cared for Fortran, and even C++ was late to the party. I attended one Khronos webminar where the panel was puzzled with a question from the audience regarding Fortran support roadmap. NVidia is sponsoring the work on the LLVM Fortran frontend, so same applies. https://flang.llvm.org/docs/ https://flang.llvm.org/docs/
  - pklausler 2y ago
    “sponsoring” in this case means writing nearly all of it ourselves (although we’ve had lots of help from Arm and some others on specific areas like OpenMP).
    - pjmlp 2y ago
      I see, I do follow LLVM conference talks, but not that deep.
- leephillips 2y ago
  And because it runs on arm (there are official arm binaries at julialang.org) you can run Julia on your phone: https://bsky.app/profile/badphysicist.bsky.social/post/3lhfmp3nnls2p https://bsky.app/profile/badphysicist.bsky.social/post/3lhfm...
SatvikBeri 2y ago
Julia is generally higher level than Fortran, with syntax inspired by Python/R/Matlab. We've been able to reliably hire Math PhDs and quickly get them productive in Julia, which would take much longer with Fortran.
eigenspace 2y ago
Julia is dynamically typed, has a very rich type system, powerful metaprogramming and polymorphism tools. Julia also has an active thriving ecosystem, and an excellent package manager.
ted_dunning 2y ago
Julia adds some pretty amazing stuff with multiple dispatch and run-time compilation. What this means is that you can glue code together in ways impossible for other languages. One example is a system that I built using three libraries. One was a C library from Postgres for geolocation, another was Uber's H3 library (also C) and a third was a Julia native library for geodesy. From Julia, I was able to extend the API of the H3 library and the Postgres library so that all three libraries would inter-operate transparently. This extension could be done without any mods to the packages I was important. Slightly similar, if you have a magic whizbang way of looking at your data as a strange form of matrix, you can simply implement a few optimized primitive matrix operations and the standard linear algebra libraries will now use your data structure. Normal languages can't really do that. More on that second case and the implications in the following video: https://www.youtube.com/watch?v=kc9HwsxE1OY https://www.youtube.com/watch?v=kc9HwsxE1OY

Julia uses LLVM for its jit architecture, if I recall correctly. That makes it a good candidate for running well on ARM platforms (think embedded data processing at the edge). Not sure how well fortran does on ARM.

pjmlp 2y ago
Fortran does quite well on almost any major CPU since 1950's, including GPUs. Actually one of the reasons CUDA won the hearts of researchers over OpenCL, is that Khronos never cared for Fortran, and even C++ was late to the party. I attended one Khronos webminar where the panel was puzzled with a question from the audience regarding Fortran support roadmap. NVidia is sponsoring the work on the LLVM Fortran frontend, so same applies. https://flang.llvm.org/docs/ https://flang.llvm.org/docs/
- pklausler 2y ago
  “sponsoring” in this case means writing nearly all of it ourselves (although we’ve had lots of help from Arm and some others on specific areas like OpenMP).
  - pjmlp 2y ago
    I see, I do follow LLVM conference talks, but not that deep.
leephillips 2y ago
And because it runs on arm (there are official arm binaries at julialang.org) you can run Julia on your phone: https://bsky.app/profile/badphysicist.bsky.social/post/3lhfmp3nnls2p https://bsky.app/profile/badphysicist.bsky.social/post/3lhfm...

SatvikBeri 2y ago

Julia is generally higher level than Fortran, with syntax inspired by Python/R/Matlab. We've been able to reliably hire Math PhDs and quickly get them productive in Julia, which would take much longer with Fortran.

eigenspace 2y ago

Julia is dynamically typed, has a very rich type system, powerful metaprogramming and polymorphism tools. Julia also has an active thriving ecosystem, and an excellent package manager.

ted_dunning 2y ago

Julia adds some pretty amazing stuff with multiple dispatch and run-time compilation. What this means is that you can glue code together in ways impossible for other languages. One example is a system that I built using three libraries. One was a C library from Postgres for geolocation, another was Uber's H3 library (also C) and a third was a Julia native library for geodesy. From Julia, I was able to extend the API of the H3 library and the Postgres library so that all three libraries would inter-operate transparently. This extension could be done without any mods to the packages I was important. Slightly similar, if you have a magic whizbang way of looking at your data as a strange form of matrix, you can simply implement a few optimized primitive matrix operations and the standard linear algebra libraries will now use your data structure. Normal languages can't really do that. More on that second case and the implications in the following video: https://www.youtube.com/watch?v=kc9HwsxE1OY https://www.youtube.com/watch?v=kc9HwsxE1OY