Hi all,

I am encountering slower than hoped for performance in my usage of Julia (in comparison to Matlab). I am sure that my coding style is largely responsible for this, but would appreciate any pointers.

I am calculating the probability of observing a given set (conditional on some values and a cut-off). I have tried to abstract away from the problem as much as possible and give a MWE that’s as minimal as possible. I’ve annotated to provide some insights on what I doing. The Julia profiler and Matlab profiler differ a lot, up to the point that I find it very hard to navigate any output the profiler gives me (I’m not a programmer, just an applied user).

```
using Combinatorics
a = 5;
values = randn(5, 15, 500)
sets = collect(powerset(1:a))
deleteat!(sets,1)
inv_set = collect(1:a)
p_set = zeros(size(sets,1), 15, 500)
@time for Z in 1:size(sets,1)
current_set = vcat(hcat(sets[Z,:]...)...); # get the vector with the current set
inv_current_set = setdiff(inv_set,hcat(sets[Z,:]...)); # get the vector with inverse of current set
first_part = prod(1 .-exp.(-exp.(-1 .*(0 .-values[current_set,:,:]))), dims = 1); # calculate p current set
second_part = prod(exp.(-exp.(-1 .*(0 .-values[inv_current_set,:,:]))), dims = 1); # calculate p inv set
third_part = prod(exp.(-exp.(-1 .*(0 .-values[:,:,:]))), dims = 1); # calculate p empty set
p_set[Z,:,:] = (first_part.*second_part) ./ (1 .- third_part); # wrap it all up
end
```

versioninfo() generates:

Julia Version 1.2.0

Commit c6da87ff4b (2019-08-20 00:03 UTC)

Platform Info:

OS: macOS (x86_64-apple-darwin18.6.0)

CPU: Intel® Core™ i7-7920HQ CPU @ 3.10GHz

WORD_SIZE: 64

LIBM: libopenlibm

LLVM: libLLVM-6.0.1 (ORCJIT, skylake)

Environment:

JULIA_NUM_THREADS = 4

JULIA_EDITOR = atom -a

PS: using threads speeds it up (~2x), but that’s as far as I get