[julia-users] Performance confusions on matrix extractions in loops, and memory allocations

[Todd Leo]

Hi fellows, 

I'm currently working on sparse matrix and cosine similarity computation, 
but my routines is running very slow, at least not reach my expectation. So 
I wrote some test functions, to dig out the reason of ineffectiveness. To 
my surprise, the execution time of passing two vectors to the test function 
and passing the whole sparse matrix differs greatly, the latter is 80x 
faster. I am wondering why extracting two vectors of the matrix in each 
loop is dramatically faster that much, and how to avoid the multi-GB memory 
allocate. Thanks guys.

--
BEST REGARDS,
Todd Leo

# The sparse matrix
mat # 2000x15037 SparseMatrixCSC{Float64, Int64}

# The two vectors, prepared in advance
v = mat'[:,1]
w = mat'[:,2]

# Cosine similarity function
function cosine_vectorized(i::SparseMatrixCSC{Float64, Int64}, 
j::SparseMatrixCSC{Float64, Int64})
    return sum(i .* j)/sqrt(sum(i.*i)*sum(j.*j))
end

function test1(d)
    res = 0.
    for i in 1:10000
        res = cosine_vectorized(d[:,1], d[:,2])
    end
end

function test2(_v,_w)
    res = 0.
    for i in 1:10000
        res = cosine_vectorized(_v, _w)
    end
end

test1(dtm)
test2(v,w)
gc()
@time test1(dtm)
gc()
@time test2(v,w)

#elapsed time: 0.054925372 seconds (59360080 bytes allocated, 59.07% gc 
time)
#elapsed time: 4.204132608 seconds (3684160080 bytes allocated, 65.51% gc 
time)