Anyone knows what Mojo is doing that Julia cannot do? I appreciate that Julia is currently limited by its ecosystem (although it does interface nicely with Python), but I don't see how Mojo is any better then.
Especially because Julia has pretty user friendly and robust GPU capabilities such as JuliaGPU and Reactant[2] among other generic-Julia-code to GPU options.
I get the impression that most of the comments in this thread don't understand what a GPU kernel is. These high-level languages like Python and Julia are not running on the kernel, they are calling into other kernels usually written in C++. The goal is different with Mojo, it says at the top of the article:
Julia's is high level yes, but Julia's semantics allow it to be compiled down to machine code without a "runtime interpretter" . This is a core differentiating feature from Python. Julia can be used to write gpu kernels.
It doesn’t make sense to lump python and Julia together in this high-level/low-level split. Julia is like python if numba were built-in - your code gets jit compiled to native code so you can (for example) write for loops to process an array without the interpreter overhead you get with python.
People have used the same infrastructure to allow you to compile Julia code (with restrictions) into GPU kernels
I guess that the interoperability with Python is a bit better. But on the other hand, the PythonCall.jl (allowing calling python from julia) is quite good and stable. In Julia, you have quite good ML frameworks (Lux.jl and Flux.jl). I am not sure that you have mojo-native ML frameworks which are similarly usable.
Mojo to me looks significantly lower level, with a much higher degree of control.
Also, it appears to be more robust. Julia is notoriously fickle in both semantics and performance, making it unsuitable for foundational software the way Mojo strives for.
I've looked into making Python modules with Julia and it doesn't look like that is very well supported right now. Where as it's a core feature of Mojo.
