| I love Julia and choose to work in it almost exclusively, but I agree with the points in the article. I've run into a lot of issues just writing numerical linear algebra type algorithms. Even core, and not quite core but maintained by core dev, libraries like Distributed.jl and IterativeSolvers.jl can feel pretty rough. For example IterativeSolvers has had strange type issues and not allowed multiple right hand sides for linear solves, for years, afaik due to some aspects of the type system and some indecision in the linalg interface. DistributedArrays still is very poorly documented and looks like it hasn't been touched in 3 years. I've run into problems when I need more explicit memory management, for example none of the BLAS/LAPACK routines have interfaces for the work arrays, so you either get reallocation or have to rewrite the ccall wrapper yourself. It can also be hard to tell where the memory allocation is happening. My most recent problem had been with Distributed and DistributedArrays, where everything is fine if you just want a basic parallel mapreduce, but has been a huge pain past that. It's not even clear to me if Distributed/DistributedArrays has been more or less abandoned in favor of MPI.jl, which for me removes most of the benefit of writing in julia, since you then have to run it through MPI. There is an MPI sort of interface for DistributedArrays but that part is not well documented and looks like more of an afterthought. My use case isn't even that complex, I just want to persistantly store some matrices across the nodes, run some linear algebra routines on them every iteration and send an update across the nodes, then collect at the end. If anyone has any idea how to do this correctly in Distributed or DistributedArrays or can point me to some examples that would be amazing because it has been taking me forever to piece it together. Not going to stop using Julia but there are many basic things even just in a scientific computing workflow that still feel like they were rushed and they can really take the wind out of your sails. |
1) Julia doesn't have X. X is critical for modern programming languages, and without X, we should not even entertain the idea that Julia may be usable
2) Julia's feature X is too unstable. It's like they tried to implement too many things in Julia 1.0, and developer time stretched thin. They should have just not implemented all this stuff!
I mean yes, we all would like a programming language that materializes with 1,000,000 developer hours already poured into it, great editor support out of the box, and which is somehow born with 10 years of usage. It's similar to wanting an employee who enters the work force with 10 years of industry experience. Nice, but it's not very realistic.