In Python, I tried many ways to apply roll rank function to a list, the speed is too slow. When the list length is about 100000, the fastest way in Python I tried takes more than 100ms.

Then in Juila, I tried the similar way used in Python, It still takes 80ms to the calculation.

So, my question is, Is there some way I am missing in using Julia, or is it just a matter of Julia limitation?
Here is my test code (I test it in Julia 1.8-beta1):

partslice(x::Int,window::Int,step::Int)=begin
partitionsize=div(x-window,step)+1+1
res=Vector{NTuple{3,Int}}(undef,partitionsize)
for i=1:partitionsize
if i==1
startloc=1
endloc=window
starti=1
elseif i==partitionsize
startloc=x-window+1
endloc=x
starti=res[i-1][2]-startloc+2
else
startloc=(i-1)step+1
endloc=startloc+window-1
starti=res[i-1][2]-startloc+2
end
res[i]=(startloc,endloc,starti)
end
res
end
function rollRank(arr::AbstractArray,starti::Int=1)
res=Vector{Float32}(undef,size(arr))
sorted_res=sort(arr[1:starti-1])
for i=starti:size(res,1)
iloc=searchsortedfirst(sorted_res,arr[i])
insert!(sorted_res,iloc,arr[i])
res[i]=100*(iloc-1)/size(sorted_res,1)
end
res[starti:end]
end
rollRankVec(arr::Vector,window::Int,n::Int)=begin
res=Vector{Float32}(undef,size(arr))
slicevec=partslice(size(arr,1),window,n)
@inbounds for (startloc,endloc,starti) in slicevec
res_=rollRank(@view(arr[startloc:endloc]),starti)
slicerange=startloc+starti-1:endloc
res[slicerange]=res_
end
res
end
#TEST:
testdata=rand(100_000)
#first time
@time rollRankVec(testdata,3000,1000);
#0.089189 seconds (965.58 k allocations: 18.778 MiB)

I am targeting to calculate the rank value compared to itâ€™s preceedents, which is a subvector with fixed window.
For exsample:
I have a vector named myvec containing 100 elements. If I set the window 20 and step 10;

for i=1:20, I calculate percentofscore(myvec[i], myvec[1:i-1])

step 10 foreward, for i=20:30, I calculate percentofscore( myvec[i], myvec[10:i-1])
.
.
.
At last, concatenate these.

Did you time your code twice, to avoid timing compilation? Because I get this when running your code:

1.7.2> @time rollRankVec(testdata,3000,1000); # first run, includes compilation
0.215553 seconds (386.24 k allocations: 28.302 MiB, 4.52% gc time, 88.10% compilation time)
1.7.2> @time rollRankVec(testdata,3000,1000); # second run
0.028715 seconds (504 allocations: 8.202 MiB)

So, 28ms, which seems, well, I dunno, but less bad. Notice the difference in memory allocations, and that 88% of the time is compilation in the first run.

It looks to me like the most obvious problems are fixed. In order to speed this up, I think a bit more thorough work is needed. Re-using vectors instead of creating new ones all the time. Make sure that insert! doesnâ€™t need to re-allocate many times.

Yes, Thanks.
It is surprising that your computer is twice faster than mine. Probably, the most direct way is just to upgrade the cpu of my computer. May I ask you what the cpu and os you are using in your computer?

I really hope thereâ€™s a better way than upgrading the computer

Itâ€™s a work laptop, a couple of years old: Intel(R) Coreâ„˘ i7-9750H CPU @ 2.60GHz in a Dell Precision 5540. Thin and light, but quite pricey, I think.