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> Maybe Julia is the way forward? Julia is well worth learning, if you do computationally-expensive work. It is kind of a pain to use interactively, though.

> Maybe Julia is the way forward? Julia is well worth learning, if you do computationally-expensive work. It is kind of a pain to use interactively, though. I use both R and Julia in my research. Think of Julia as the new Fortran, though, not the new R.

edgyquant 5y ago
Isn’t Julia a compiled language “pretending” to be interpreted? I thought it was interpreted or JITd up until it’s last release or so when they mentioned it has to actually compile. It’s fast, sure, but is it faster than other compiled languages?
- bluenose69 5y ago
  I do not have deep experience with this, but the word on the street is that Julia can be faster than other compiled languages. I think that's partly because it can find a good algorithm, based on your data structure; think of loop unrolling, etc. You can inspect the assembly code for anything you're working on, and that can be quite helpful at times; see e.g. https://youtu.be/wU6c8CDRXJE?t=3887 https://youtu.be/wU6c8CDRXJE?t=3887. I think the reason why quite a few high-performance people (I mean in the science community -- I don't know much about other communities) are excited about Julia is simply that well-respected experts are also excited. An example is the Julia implementation of the MIT GCM (general circulation model) for the ocean; for similar projects, see https://github.com/CliMA https://github.com/CliMA. Programmer effort is also a factor in scientific computation. If the system can do some of your work for you, so much the better; see e.g. https://www.youtube.com/watch?v=rZS2LGiurKY https://www.youtube.com/watch?v=rZS2LGiurKY for a lecture that touches upon how the framework of Julia eases the burden of machine-learning tasks. As I say, though, I do not have deep experience with Julia. I've rewritten one of my numerical models in Julia and the speed is about the same as before, but my code is much shorter and easier to understand. I would not burn up 6 months translating a complex code, but nor would I start a 6-month coding project in Fortran anymore.
- adgjlsfhk1 5y ago
  In general, Julia has similar performance characteristics to other fast compiled languages (Fortran/C++). There are some performance differences do to different semantics (eg bounds checks by default), but Julia is good about giving you the ability to opt out of these easily. There are also definitely places where Julia makes it a lot easier to get better code by making it a lot easier to use better algorithms, so in general, simple Julia code tends to be similar/better performance than Fortran/C++, and optimized Julia code tends to be about the same speed as Fortran/C++, but with 10x less code.
adgjlsfhk1 5y ago
I agree that Julia isn't yet an R replacement, but I think that in addition to being a new Fortran, it also does well as a new Matlab/numpy.

edgyquant 5y ago

Isn’t Julia a compiled language “pretending” to be interpreted? I thought it was interpreted or JITd up until it’s last release or so when they mentioned it has to actually compile. It’s fast, sure, but is it faster than other compiled languages?

bluenose69 5y ago
I do not have deep experience with this, but the word on the street is that Julia can be faster than other compiled languages. I think that's partly because it can find a good algorithm, based on your data structure; think of loop unrolling, etc. You can inspect the assembly code for anything you're working on, and that can be quite helpful at times; see e.g. https://youtu.be/wU6c8CDRXJE?t=3887 https://youtu.be/wU6c8CDRXJE?t=3887. I think the reason why quite a few high-performance people (I mean in the science community -- I don't know much about other communities) are excited about Julia is simply that well-respected experts are also excited. An example is the Julia implementation of the MIT GCM (general circulation model) for the ocean; for similar projects, see https://github.com/CliMA https://github.com/CliMA. Programmer effort is also a factor in scientific computation. If the system can do some of your work for you, so much the better; see e.g. https://www.youtube.com/watch?v=rZS2LGiurKY https://www.youtube.com/watch?v=rZS2LGiurKY for a lecture that touches upon how the framework of Julia eases the burden of machine-learning tasks. As I say, though, I do not have deep experience with Julia. I've rewritten one of my numerical models in Julia and the speed is about the same as before, but my code is much shorter and easier to understand. I would not burn up 6 months translating a complex code, but nor would I start a 6-month coding project in Fortran anymore.
adgjlsfhk1 5y ago
In general, Julia has similar performance characteristics to other fast compiled languages (Fortran/C++). There are some performance differences do to different semantics (eg bounds checks by default), but Julia is good about giving you the ability to opt out of these easily. There are also definitely places where Julia makes it a lot easier to get better code by making it a lot easier to use better algorithms, so in general, simple Julia code tends to be similar/better performance than Fortran/C++, and optimized Julia code tends to be about the same speed as Fortran/C++, but with 10x less code.

adgjlsfhk1 5y ago

I agree that Julia isn't yet an R replacement, but I think that in addition to being a new Fortran, it also does well as a new Matlab/numpy.