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For non-CS scientific computing users, "a high performance language with easy syntax" is a big feature.
by Fomite 9y ago
For non-CS scientific computing users, "a high performance language with easy syntax" is a big feature.
- flavio81 9y agoExactly, that's why I think Julia is a good idea. For scientific users used to R or Python, the performance should knock their socks off. (But users doing their work in C, C++ or Fortran will probably see little or no improvement).
- bokertov 9y agoDo we really need Julia given that Python has Numpy which does a lot of the heavy lifting in numerical computation and is pretty fast?
- Fomite 9y agoNumpy is great as long as you're using Numpy. There's lots of aspects of scientific computing that may not be using Numpy.
- tavert 9y agoNumpy is good for vectorized calculations on dense multidimensional arrays of homogenous 64 or 32 bit floating point numbers, or integers. If that describes your problem and you like Python, by all means use numpy. If that doesn't describe your entire problem, you'll need more tools.
- hpcjoe 9y agoIt should be understood that numpy is written in C, and is an example of the "2 language problem" that Julia solves rather well. The question really is the converse ... given how deft Julia is with general programming as well as numerically intensive programming, do you really need Python + numpy(C).
- tnecniv 9y agoOn top of what others have mentioned, there is something to be said about getting numerical linear algebra tools as native citizens of a language. There are many things I hate about MATLAB, having matrices built into the syntax is nice.
- tnecniv 9y agoIt's why the horror that is MATLAB exists. I'm looking forward to replacing MATLAB with the much more ergonomic Julia soon though.
- andyferris 9y agoYes, do it! It was an easy transition years ago, now Julia seems light-years ahead.
- walshemj 9y agoWould that not be true for all technical / scientific use cases if not most use cases in general?