Precisely; today Julia already solves many of those problems.
It also removes many of Matlab's footguns like `[1,2,3] + [4;5;6]`, or also `diag(rand(m,n))` doing two different things depending on whether m or n are 1.
It also removes many of Matlab's footguns like `[1,2,3] + [4;5;6]`, or also `diag(rand(m,n))` doing two different things depending on whether m or n are 1.
The most cogent argument for the use of parentheses for array slicing (which derives from Fortran, another language that I love) is that it can be thought of as a lookup table, but in practice it's useful to immediately identify if you are calling a function or slicing an array.
But Julia also introduces new problems, such as JIT warmup (so it's not really suitable for scripting) and is still not considered trustworthy:
Our goal is to make a runtime that lets people stay at the math layer as much as possible, and run the math as fast as possible.
> I don't think Julia really solves any problems that aren't already solved by Python.
I don't really need proper furniture, the cardboard boxes and books setup I had previously "solved" the same problems, but I feel less worried about random parts of it suddenly buckling, and it is much more ergonomic in practice too.
At least it has those tools and libraries, what cannot be said about Julia.
My experience with this website is that it would be rather pointless to enumerate, because you will then point to some poorly documented, buggy and supporting fraction of features Julia "alternatives" to Python packages or APIs that are developed and maintained by well-resourced organizations.
The same thing for tooling - unstable, buggy Julia plugin for VSCode is not the same as having products like PyCharm and official Python plugins made by Microsoft for VS and VSCode.
Now, I will admit that Julia also has some niceties that would be hard to find in Python ecosystem (mainly SciML packages), but it is not enough.
> Have you used the language or merely speculating?
I just saw the logo in Google Images.
This is a huge understatement. At the hedge fund I work at, I learned Julia by porting a heavily optimized Python pipeline. Hundreds of hours had gone into the Python version – it was essentially entirely glue code over C.
In about two weeks of learning Julia, I ported the pipeline and got it 14x faster. This was worth multiple senior FTE salaries. With the same amount of effort, my coworkers – who are much better engineers than I am – had not managed to get any significant part of the pipeline onto Numba.
> And if something is truly performance critical, it should be written or rewritten in C++ anyway.
Part of our interview process is a take-home where we ask candidates to build the fastest version of a pipeline they possibly can. People usually use C++ or Julia. All of the fastest answers are in Julia.
That's surprising to me and piques my interest. What sort of pipeline is this that's faster in Julia than C++? Does Julia automatically use something like SIMD or other array magic that C++ doesn't?
Everything you can do in Julia you can do in C++, but lots of projects that would take a week in C++ can be done in an hour in Julia.
In my view, it's not that Julia itself is faster than Rust - on the contrary, Rust as a language is faster than Julia. However, Julia's prototyping, iteration speed, benchmarking, profiling and observability is better. By the time I would have written the first working Rust version, I would have written it in Julia, profiled it, maybe changed part of the algorithm, and optimised it. Also, Julia makes more heavy use of generics than Rust, which often leads to better code specialization.
There are some ways in which Julia produces better machine code that Rust, but they're usually not decisive, and there are more ways in which Rust produces better machine code than Julia. Also, the performance ceiling for Rust is better because Rust allows you to do more advanced, low level optimisations than Julia.
The pipeline was pretty heavily focused on mathematical calculations – something like, given a large set of trading signals, calculate a bunch of stats for those signals. All the best Julia and C++ answers used SIMD.
It would be fun if you could share a similar pipeline problem to your take-home (I know you can't share what's in your interview). I started off in scientific Python in 2003 and like noodling around with new programming languages, and it's great to have challenges like this to work through. I enjoyed the 1BRC problem in 2024.
But isn't the whole point of this article that Matlab is more readable than Python (i.e. solves the readability problem)? The Matlab and Julia code for the provided example are equivalent[1]: which means Julia has more readable math than Python.
[1]: Technically, the article's code will not work in Julia because Julia gives semantic meaning to commas in brackets, while Matlab does not. It is perfectly valid to use spaces as separators in Matlab, meaning that the following Julia code is also valid Matlab which is equivalent to the Matlab code block provided in the article.
X = [ 1 2 3 ];
Y = [ 1 2 3;
4 5 6;
7 8 9 ];
Z = Y * X';
W = [ Z Z ];You read the article that compares MATLAB to Python? It's saying MATLAB, although some issues exist, still relevant because it's math-like. GP points out Julia is also math-like without those issues.
It's wild what people get used to. Rustaceans adapt to excruciating compile times and borrowchecker nonsense, and apparently Pythonistas think it's a great argument in favor of Python that all performance sensitive Python libraries must be rewritten in another language.
In fairness, we Julians have to adapt to a script having a 10 second JIT latency before even starting...
It is, because usually someone already did it for them.
The diag is admittedly unfortunate and it has confused me myself, it should actually be 2 different functions (which are sort of reverse of each other, weirdly making it sort of an involution).
>> [1 2 3] + 1
ans = [2 3 4]
>> [1 2 3] + [1; 2; 3]
ans = [2 3 4
3 4 5
4 5 6]
This does not work only with addition and subtraction, but with dot-product and other functions as well. You can do this across arbitrary dimensions, as long as your input matrices non-unit dimensions do not overlap.
Z = np.array([[1,2,3]])
W = Z + Z.T
print(W)
[[2 3 4]
[3 4 5]
[4 5 6]]
[1] https://numpy.org/devdocs/user/basics.broadcasting.html#gene...
Z = [1,2,3]
W = Z .+ Z' # note the . before the + that makes this a broadcasted
Matlab's functions like to create row vectors (e.g., linspace) in a world where column vectors are more common, so this is a common occurrence.
So `[1,2,3] + [4;5;6]` is a concise syntax for an uncommon operation, but unfortunately it is very similar to a frequent mistake for a much more common operation.
Julia tells the two operations (vector sum and outer sum) apart very elegantly: one is `S - T` and the other is `S .- T`: the dot here is very idiomatic and consistent with the rest of the syntax.