Why is Julia's performance more sensitive to memory allocations than Numpy's?

[Palli]

@maxkapur

the sort of fine-toothed “avoid every possible allocation” advice that is so often
repeated on the Julia forum. It seems to me that Numpy just has a different
performance model than Julia in which the stray allocation here and there simply
isn’t that big of a deal.

It’s a rule of thumb, is true, while allocations in loops can be fast as a workaround with:

GitHub - MasonProtter/Bumper.jl: Bring Your Own Stack

Besides Libc.malloc and free, in Julia’s stdlib, there’s also:

GitHub - JuliaSIMD/ManualMemory.jl: Manual memory management utilities.

Can I manage the memory by myself?

I have a code, the GC live and RSS memory increased quickly when running, while I don’t need such big memory to do basic computation. I need to allocate lots of small dict for intermidiate computing before, and the memory increase quickly; to avoid memory allocation, I pre-allocate two array to avoid repeatly allocate small dict, it put off the invreasing. But still get lots of allocation when I repeatly add some sparse array to get final one. I am tired of avoiding allocation, and I guess memo…