I havent tried Tsan with rr but msan and asan work quite well with it (it’s quite slow when doing this) but seeing the sanitizer trigger then following back what caused it to trigger is very useful.
Chaos mode is an option when invoking rr that can expose some concurrency issues. Basically it switches which thread is executing a bunch to try and simulate multiple cores executing. It has found some race conditions for me but it’s of course limited
There is, it's called count_ones. Though I wouldn't be surprised if LLVM could maybe optimize some of these loops into a popcnt, but I'm sure it would be brittle
So it was a bit more pervasive than this, the issue was that flushing subnormals (values very close to 0) to 0 is a register that gets set, so if a library is built with the fastmath flags and it gets loaded, it sets the register, causing the whole process to flush it's subnormals. i.e https://github.com/llvm/llvm-project/issues/57589
btw, there has been a pretty nice effort of reimplementing the tidyverse in julia with https://github.com/TidierOrg/Tidier.jl and it seems to be quite nice to work with, if you were missing that from R at least
LLVMs API is the c++ one. The C one while more stable also doesn't support everything. Keeping up with LLVM is annoying but it's not the source of bugs or anything of the sort. PS(it's not actually stable. Because if the c++ code it calls is removed it just gets removed from the C one)
I say this as one of the devs that usually do the work of keeping up the latest LLVM.
I do believe this is an issue of not having explicit dependencies. Julia takes the approach of, we build and ship everything for every OS, which means Pkg (the package manager) knows about binary dependencies as well. Making things more reproducible in language
Julia does have really nice GPU support, being able to directly compile julia code into CUDA, ROCm, Metal or other accelerators. (Being GPU code it's limited to a subset of the main language)
I'm sure there are reasons for it, but Chris Lattner has been jumping around a bit. Remember swift4TF.
But hopefully this one is seen through with lots of open source too.
One interesting thing is that if julia can prove what types a function will be called with at compile time, it doesn't have to do dynamic dispatch, so it has no overhead. It's what the julia folks call type-stable code
That's one of the cool things about Julia. If it can prove the types of the arguments to a function, or at least reduce them to a small set. It can do the dispatch at compile time.
Well, Julia has better than semi decent support for generics, since every function is by definition a generic.
Veritasium has a recent video that goes into the algorithm a little bit. So does 3blue1brown. But basically anything that processes signals digitally will probably use the FFT somewhere. But even outside of that, you can use it for some kinds of matrixes in linear algebra and probably many other places :)
SemVer is very important for me.
If the language has binary dependencies the package manager should also handle them. Depending on the system for them leads to hard to debug issues.
An easy way to reproduce environments is also important via a toml file or something similar.