[julia-users] strange performance with boolean operators and sparse matrices

[Seth]

Hi all,

I ran into some puzzling performance today with sparse matrices. I defined

_column(a::AbstractSparseArray, i::Integer) = sub(a.rowval, a.colptr[i]:a.
colptr[i+1]-1)

# material nonimplication
⊅(p::Bool, q::Bool) = p & !q 

function ⊅(a::SparseMatrixCSC, b::SparseMatrixCSC) 
    (m,n) = size(a) 
    resultmx = spzeros(Bool,m,n) 
    for c = 1:n 
        for r in _column(a,n) 
            # info("row $r, col $c") 
            resultmx[r,c] = ⊅(a[r,c], b[r,c]) 
        end 
    end 
    return resultmx 
end

a = sprandbool(1000000,1000000,0.0001) 
b = sprandbool(1000000,1000000,0.0001)


and ran it in comparison with the & operator:

julia> @time z = a ⊅ b;
 15.272250 seconds (1.10 M allocations: 73.870 MB)

julia> @time z = a & b;   # this is still going ~10 minutes later.


It seems strange that my home-grown function is orders of magnitude more 
efficient than a built-in boolean primitive. Am I missing something?