IncompleteLU.jl efficiency much worse than Matlab ilu

Hello Julia user,

I’m developing a model with large matrix and systems of linear equation solver. I plan to change the original matlab code in Julia as I heard Julia is much faster than MatLab.

However, I found ilu in Julia is very slow and can lead to out of memory with small tolerance, but ilu() on matlab can always finish in 1s. My sparse matrix size is 10^6*10^6. Did I use a wrong function or variable format (current is SparseMatrixCSC)?

I used @btime to measure the elapsed time, but I found without @btime the code runs much faster.

I’m a green hand to Julia. Thank you for any replies and suggestions in advance!

1 Like

Could you show a minimum working example demonstrating the issue?


Welcome to the Julia forum! :wave:

A small remark on @btime – that of course runs the code more than once, to report proper timing and not just once like e.g. @time (or MATLABs tic() ... toc()), cf Manual · BenchmarkTools.jl – or use @benchmark to see a larger summary of that.
So for sure without benchmarking the code is faster.