I have been banging my head against a wall trying to use static arrays in julia.
https://github.com/JuliaArrays/StaticArrays.jl
They are fast but updating them is a pain. This is no surprise, they are meant to be immutable!
But it is continuously recommended to me that I use static arrays even though I have to update them. In my case, the static arrays are small, just length 3, and i have a vector of them, but I only update 1 length three SVector at a time.
Option 1
There is a really neat package called Setfield
that allows you to do inplace updates of SVectors in Julia.
https://github.com/jw3126/Setfield.jl
The catch... it updates the local copy. So if you are in a nested function, it updates the local copy. So it comes with some book keeping since you have to inplace update the local copy and then return
that copy and update the actual array of interest. You can't pass in your desired array and update it in place, at least, not that I can figure out! Now, I do not mind bookeeping, but I feel like updating a local copy, then returning the value, updating another local copy, and then returning the values and finally updating the actual array must come with a speed penalty. I could be wrong.
Option 2
It bugs me that in order to do an update a static array I must
exampleSVector::SVector{3,Float64}
<-- just to make clear its type and size
exampleSVector = [value1, value2, value3]
This will update the desired array even if it is inside a function, which is nice and the goal, but if you do this inside a function it creates a temporary array. And this kills me because my function is in a loop that gets called 4+ million times, so this creates a ton of allocations and slows things down.
How do I update an SVector
for the Option 2 scenario without creating a temporary array?
For the Option 1 scenario, can I update the actual array of interest rather than the local copy?
If this requires a simple example code, please say so in the comments, and I will make one. My thinking is that it is answerable without one, but I will make one if it is needed.
EDIT:
MCVE code - Option 1 works, option 2 does not.
using Setfield
using StaticArrays
struct Keep
dreaming::Vector{SVector{3,Float64}}
end
function INNER!(vec::SVector{3,Float64},pre::SVector{3,Float64})
# pretend series of calculations
for i = 1:3 # illustrate use of Setfield (used in real code for this)
pre = @set pre[i] = rand() * i * 1000
end
# more pretend calculations
x = 25.0 # assume more calculations equals x
################## OPTION 1 ########################
vec = @set vec = x * [ pre[1], pre[2], pre[3] ] # UNCOMMENT FOR FOR OPTION 1
return vec # UNCOMMENT FOR FOR OPTION 1
################## OPTION 2 ########################
#vec = x * [ pre[1], pre[2], pre[3] ] # UNCOMMENT FOR FOR OPTION 2
#nothing # UNCOMMENT FOR FOR OPTION 2
end
function OUTER!(always::Keep)
preAllocate = SVector{3}(0.0,0.0,0.0)
for i=1:length(always.dreaming)
always.dreaming[i] = INNER!(always.dreaming[i], preAllocate) # UNCOMMENT FOR FOR OPTION 1
#INNER!(always.dreaming[i], preAllocate) # UNCOMMENT FOR FOR OPTION 2
end
end
code = Keep([zero(SVector{3}) for i=1:5])
OUTER!(code)
println(code.dreaming)
SVector
each time? For option 2, you writeexampleSVector = SVector(value1, value2, value3)
. No allocations. In your MCVE code, just writevec = vec .* x
. BTW:vec
is the name of a function in Base, you shouldn't shadow it. – DNFMVector
is not that much slower than,SVector
. In some cases they are almost similar. (Actually, instead ofvec = vec .* x
you can also writevec *= x
.) – DNF