Dear all,
In a project I am developing a @profile shows me that the slowest part of
the code is the sum of elements of an Array that follow some conditions.
Please consider the following code:
y = rand(1000);
x = collect(0.0:0.001:0.999);
Ei = 0.2;
Ef = 0.7;
N = length(x)
I want to calculate the sum of elements in "y" for which elements the
respective values in "x" are between "Ei" and "Ef". If I was using R, for
example, I would use something like:
mysum = sum(y[which((x < Ef)&&(x > Ei))]); #(not tested in R, but I suppose
that is the way to do it)
In Julia, I can think in at least two ways to calculate it:
function calcSum(x::Array{Float64,1}, y::Array{Float64,1}, Ei::Float64,
Ef::Float64, N::Int64)
mysum=0.0::Float64;
for(i in 1:N)
if( Ei < x[i] <= Ef)
mysum += y[i];
end
end
return(mysum);
end
function calcSum2(x::Array{Float64,1}, y::Array{Float64,1}, Ei::Float64,
Ef::Float64, N::Int64)
return sum(y[map(v -> Ei < v < Ef, x)]);
end
As you can see below, for the first function (calcSum) I got a much better
performance than for the second one (minimum 10x faster).
@time calcSum(x,y,Ei,Ef,N)
0.003986 seconds (2.56 k allocations: 125.168 KB)
246.19757461217014
@time calcSum(x,y,Ei,Ef,N)
0.000003 seconds (5 allocations: 176 bytes)
246.19757461217014
@time calcSum(x,y,Ei,Ef,N)
0.000002 seconds (5 allocations: 176 bytes)
246.19757461217014
@time calcSum2(x,y,Ei,Ef,N)
0.003762 seconds (1.61 k allocations: 53.743 KB)
245.48156534879303
@time calcSum2(x,y,Ei,Ef,N)
0.000050 seconds (1.01 k allocations: 20.969 KB)
245.48156534879303
@time calcSum2(x,y,Ei,Ef,N)
0.000183 seconds (1.01 k allocations: 20.969 KB)
245.48156534879303
Does any one have an idea about how to improve the performance here?
Many thanks for any help! Happy new year to all of you!
Charles
--
Um axé! :)
--
Charles Novaes de Santana, PhD
http://www.imedea.uib-csic.es/~charles
