5 ms·

(1) if every programming language was equally good at everything we would not have a multitude of programming languages. And there are always advances in progra

(1) if every programming language was equally good at everything we would not have a multitude of programming languages. And there are always advances in programming language theory and concepts which have not been captured in earlier languages. (2) The semantics of the language put restrictions on what sort of optimizations you can do or how easy they are to perform. Julia has been designed to make it very easy to optimize. E.g. something like JavaScript runs quite fast, but only through a herculean effort by the JIT makers. It's design is terrible with respect to making it easy to create high performance compilers/JIT. (3) What is "new" syntax. All languages have variations in syntax. Wherever Julia differs in syntax from whatever you know it isn't just for the hell of it. There are usually good reasons. Mostly Julia tries to look similar to programming languages familiar to its target group. Where it differs I'd say it is usually to improve on deficiencies in said languages. I am an iOS developer but I write smaller scripts to do custom processing of source code, assets etc. I wrote my scripts in Julia first, but decided to convert to Python afterwards to make it easier to distribute to other people, given that Julia does not come pre-installed. I got to say Julia is WAY WAY nicer to program than python for shell script like stuff. I kept hitting on so many issues when converting to Python that I kept a long list of issues with Python. My takeaway from this experience is that a LOT has happened since Python, Ruby etc was conceived. Julia has been able to tap into all the advances that has happened since then. It has a much smoother REPL environment, much more sensible set of built in functions. More sane naming conventions. And the libraries are built to combine effectively in much better way. You simply need to write much less code when writing Julia because stuff combine so well. While in Python I find myself having to write a lot of glue code to make pieces fit together. So the question is, why stick to a language just because it is well established. I had many more years of experience with Python. But I get the job done much faster with Julia. So why waste extra time on Python just because it is established? That has no inherent value.

edblarney 10y ago
"So the question is, why stick to a language just because it is well established." You answered the question yourself: "but decided to convert to Python afterwards to make it easier to distribute to other people" Languages are platforms. They have vast distribution, incumbency, developers, apis etc. - and that has a lot of value. Imagine if you did dev on Julia - how do you hire devs? Julia has a very, very small mind-share - in order for it to reach 'platform status' it really has to cross a threshold, and for that - it has to provide significantly more value. I'm also curious to know 'how much faster' it is - couldn't those performance problems be solved with more horsepower? Developers have a terrible habit of over-optimizing for performance, which usually comes at the cost of complexity. Anyhow - I know little about Julia so I'll hold my tongue, maybe it's the bees-knees ... It'd be great to see some data points.
- tavert 10y ago
  > Imagine if you did dev on Julia - how do you hire devs? Julia doesn't take long to learn if you've ever written any Python, or Matlab, or R. It's very good at calling into existing code written in other languages (the above, and C) so it's possible to transition gradually. > I'm also curious to know 'how much faster' it is - couldn't those performance problems be solved with more horsepower? Depends on the exact type of computation. If everything you're doing is straight BLAS/LAPACK or other library calls, then Python is just glue code and not taking much of the time. If you're implementing your own custom algorithms (or writing libraries) that are difficult to express in a vectorized way (where numpy just does the work in C) or you want more customized data structures and types than dense arrays of single or double precision floating point numbers, Julia will do well. There are various projects out there that try to make Python faster (PyPy, Numba, Pyston, Pyjion, the list seems to grow every few months) but it's a very difficult problem to do in full generality since you either have to restrict yourself to a subset of the language where the semantics permit optimizations, or lose compatibility with all the C-extension libraries you're used to. Julia has designed the language semantics and the conventions of the standard library to be more amenable to optimization. It's early days yet in terms of multithreading support, but Julia has a better story there as well. For a representative performance comparison case study, see section 4 of https://arxiv.org/pdf/1312.1431v1.pdf https://arxiv.org/pdf/1312.1431v1.pdf. There have been similar experiences reported and published in other application areas since.

edblarney 10y ago

"So the question is, why stick to a language just because it is well established." You answered the question yourself: "but decided to convert to Python afterwards to make it easier to distribute to other people" Languages are platforms. They have vast distribution, incumbency, developers, apis etc. - and that has a lot of value. Imagine if you did dev on Julia - how do you hire devs? Julia has a very, very small mind-share - in order for it to reach 'platform status' it really has to cross a threshold, and for that - it has to provide significantly more value. I'm also curious to know 'how much faster' it is - couldn't those performance problems be solved with more horsepower? Developers have a terrible habit of over-optimizing for performance, which usually comes at the cost of complexity. Anyhow - I know little about Julia so I'll hold my tongue, maybe it's the bees-knees ... It'd be great to see some data points.

tavert 10y ago
> Imagine if you did dev on Julia - how do you hire devs? Julia doesn't take long to learn if you've ever written any Python, or Matlab, or R. It's very good at calling into existing code written in other languages (the above, and C) so it's possible to transition gradually. > I'm also curious to know 'how much faster' it is - couldn't those performance problems be solved with more horsepower? Depends on the exact type of computation. If everything you're doing is straight BLAS/LAPACK or other library calls, then Python is just glue code and not taking much of the time. If you're implementing your own custom algorithms (or writing libraries) that are difficult to express in a vectorized way (where numpy just does the work in C) or you want more customized data structures and types than dense arrays of single or double precision floating point numbers, Julia will do well. There are various projects out there that try to make Python faster (PyPy, Numba, Pyston, Pyjion, the list seems to grow every few months) but it's a very difficult problem to do in full generality since you either have to restrict yourself to a subset of the language where the semantics permit optimizations, or lose compatibility with all the C-extension libraries you're used to. Julia has designed the language semantics and the conventions of the standard library to be more amenable to optimization. It's early days yet in terms of multithreading support, but Julia has a better story there as well. For a representative performance comparison case study, see section 4 of https://arxiv.org/pdf/1312.1431v1.pdf https://arxiv.org/pdf/1312.1431v1.pdf. There have been similar experiences reported and published in other application areas since.