Julia is excellent. There are a lot things with Julia (think multiple dispatch) that once you learn them you can't stop noticing that other languages are doing it wrong
I don't understand why people keep talking about multiple dispatch like Julia invented it. You can do that in many other languages, even languages designed for numeric computation. What's cool about Julia is that it has brought 90s compiler technology to scientific computing: a field which still thinks MATLAB is a really good way to develop and communicate scientific ideas.
I think a lot of the reason is that most previous incarnations of multiple dispatch give you slow multiple dispatch, and a different way to define functions that don't have multiple dispatch and are faster to call. As such MD tends not to be commonly used in languages where it exists. Julia isn't the first to have multiple dispatch, but it is the first where everything is multiple dispatch. The result of this is that we put in a ton of work to make multiple dispatch fast, and all the APIs are designed around it, which gives a very different feel to the language.
As I noted around a year ago, there's a little puzzle here: in principle Julia's approach was available in the 90s, when decent JIT technology was becoming available. So why was MD given only toy-like treatment before Julia? (MATLAB did get a JIT in 2002, so it's not really a toy, but the performance was not good enough to prove the concept)
My impression is that the complained-about inflexible division between concrete and abstract types was pretty necessary to achieving good results in practice, but this division is alien to the expressiveness-loving lisp culture.
MATLAB is fairly impressive for development. It works ok for communication. What are the areas for improvement you have in mind, and what are the alternatives I should consider?
The 2 biggest problems with Matlab are price and lack of community. The price is an issue even if you can afford it because it makes it really hard to deploy widely since anyone who wants to run the code also needs to be paying Mathworks. This closely ties in to the community issue. Almost all Matlab libraries are proprietary (and written by MathWorks). If you find a bug, your only option is to file a report and wait 3 years for it to not get fixed. In an open source ecosystem, you can dig into the code and fix the problem yourself if you need to.
I once got on a call with MATLAB compiler engineer and had him fix a bug in an afternoon. I should have written a blog post about it but it is too late, I have very little recollection of the details. It was kinda awesome though. How often can you do just call up someone to fix the compiler. To be fair, I worked for a very large Fortune 50 company and had over 10k licenses of MATLAB.
Also, it is interesting that outside of SV and HN crowd, we thought MATLAB is awesome. We had all the toolboxes, there is a gazillion of them. Even obscure RF related stuff. You just can't find a library for something like a phased array analysis. May be you can, but it won't be as high quality and industry proven as this: https://www.mathworks.com/products/phased-array.html
Another topic that we often understood is that when a jet engine is hoisted up for testing and you need to get something fixed or have a question about in next 2 hours, you need commercial software support to help you out. Gravity of situation cannot be understated. It is stressful. There is OSS software support, but MATLAB is on another level. Absolutely outstanding support. Companies like Apple and SpaceX rely on MATLAB heavily outside of software engineer orgs.
If you are basically using finished code, your own code is mostly a top-level script, and you mainly need polished functionality and professional support, then Matlab toolboxes and support are very good.
But the language itself is primitive compared to OSS alternatives, so when you need to develop your own software on a larger scale, it falls short.
There is of course a sliding scale between 'user' and 'developer' in any language, but I think that the closer you are to the 'user' end of the scale, the better Matlab looks.
I don't find the language primitive at all. It just has a poor deployment story that consists of "autogenerate to hidious unreadable c c++ cuda or vhdl, or writ it again yourself". It is amazing the autogen even works, which it really does, but that code...
But back to the language. I can typically do a line by line syntax change to get pytorch (julia is a but different but not too much). The resulting matlab sometimes runs faster too. If you avoid globals and write everything vectorized it is all really clean. OO code would be hidious, but I try not to use OO or goto in any language unless there is no other option. I like that arguments to a function are pass by reference unless you write to them, in which case it does a smart copy. Julia syntax looks different sometimes because you don't have to vectorize for performance as in matlab or python, in fact sometimes you shouldn't.
All in all, if the deployment story was solved, I probably wouldn't be trying out Julia. In fact I still prototype in Matlab before implementation in pytorch or julia, its just eaier to get that first thing working.
> Also, it is interesting that outside of SV and HN crowd, we thought MATLAB is awesome. We had all the toolboxes, there is a gazillion of them. Even obscure RF related stuff. You just can't find a library for something like a phased array analysis. May be you can, but it won't be as high quality and industry proven as this: https://www.mathworks.com/products/phased-array.html
As a matter of fact I find most comments about Matlab here supportive. I actually do find Matlab to be a polished product, specially in the IDE and documentation part. In the end you use whatever saves you time, that's why most people that use Python have chosen Python: because of the libraries. I have personally found Matlab toolboxes quite simplistic for my own purposes. Most people, myself included, actually trust more that SW is used and deployed in the millions than any kind of support.
>How often can you do just call up someone to fix the compiler. To be fair, I worked for a very large Fortune 50 company and had over 10k licenses of MATLAB.
Very often if you're paying upwards of $10,000,000 in licenses a month.
The community issue also means that code sharing is very limited compared to other languages. If it isn't in one of the Mathworks toolboxes (that you can afford), you'll probably have to end up at the Mathworks "File Exchange" site, where you take a look at the existing code that "solves your problem" (in a non-generic, buggy way), cry your eyes out, and end up implementing it yourself.
Both fair points. And the alternative? I like Julia, but i still haven't found a workflow I like. I still typically prototype in Matlab and deploy in c++/cuda.
I'm trying to work entirely inside the repl. Its not bad but for large stuff the lack of go to definition hurts, and there isn't an easy way to copy commands that worked into my module. I'll try vscode next. That is what I use for c++/c. Is there anything else I should try?
If you read the small print on that license it almost certain says something like "for hobby use only. Not for academic or commercial use". A MATLAB license that allows commercial use starts at around 2000$/user.
Which Lisp? What applications do you think it is slow at? Hint: It can be faster than C due to compiler macros, and infact, its regular expression engine is much faster than Perl's, which is written in C, just to give a concrete example.
My experience is that even writing "idiomatic", CLOS heavy code tends to be faster out of the box than most dynlangs out there. Writing close to C++ code requires a lot of manual work and is probably not even worth it unless for optimizing hot-paths.