I would like to parallelise a broadcast of a complicated function on my machine.
Being new to parallel computing, I’m getting swamped and lost in all the posts and comments and open issues on it. My context is a little more involved than all the tutorials’ contexts. Otherwise documentation for the various packages and procedures aren’t comprehensive/novice-proof enough for me to know if I can just willy nilly run my code in parallel.
My context is as follows. I have a function
f(x, y) that is defined to involve root-finding, interpolations, a lot of stuff involved. I would like to compute
x = LinRange(0.0, 2e3, Nx) y = LinRange(0.0, 5e2, Ny) f_grid = @time [f(x_, y_) for y_ in y, x_ in x] heatmap(x, y, f_grid)
where the comprehension takes a long time given the root-finding, etc.
Nx = 11; Ny = 9; 52.993936 seconds (173.35 M allocations: 4.156 GiB, 2.45% gc time)
Nx = 31; Ny = 25; 466.169140 seconds (1.39 G allocations: 33.223 GiB, 2.28% gc time)
Mind you, my goal of
Nx = 1001; Ny = 801; should suffice for my plotting purposes.
As per my title, alternatively I could compute
f_grid = @time f.(x', y)
- Should I go back to my code to first really focus on squeezing out as many allocations as I can? (As prompted by this comment.)
- If so, feel free to explore the ProfileView.jl outputted flame graph here in my repo. (No idea if it gives any insights.)
- If not Q1, or once Q1 is done,
- Should I run this type of code (with root-finding, interpolation, automatic differentiation, summations, and loops in loops in loops) in CPU or my NVIDIA GPU?
- Will it be safe to just run Julia with N cores, then use Strided.jl? (An elegant design imo.)
- I have two CPU cores, and 8 logical cores. should I use N = 8 or N = 2? This post doesn’t specify exactly how he altered the number of workers. (What is the difference between workers and threads? A Google search leaves it somewhat unclear on me.)
- Do I have to use
@everywhereon all the functions my function
fdepends on? (Prompted by this comment.)
I think that’s enough preliminary questions to present my confusion.