# Why is this Julia code considerably slower than Matlab

**URL:** <https://discourse.julialang.org/t/why-is-this-julia-code-considerably-slower-than-matlab/2365>\
**Category:** New to Julia\
**Tags:** performance\
**Created:** [February 28, 2017, 3:03pm UTC](https://discourse.julialang.org/t/why-is-this-julia-code-considerably-slower-than-matlab/2365 "2017-02-28T15:03:36Z")\
**Posts on this page:** 1\
**Showing post:** 62

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**Author:** ![stevengj](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/stevengj/32/71_2.png) [@stevengj](https://discourse.julialang.org/u/stevengj)\
**Post date:** [March 3, 2017, 2:47pm UTC](https://discourse.julialang.org/t/why-is-this-julia-code-considerably-slower-than-matlab/2365/62 "2017-03-03T14:47:23Z")

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> [@zsoerenm](#):
>
> `sum_signal .+= steering_vectors[:,i] .* cis.((2 * pi * 1.023e6 / 4e6) .* range_transp .+ (40 * pi / 180));`

Loop fusion will actually be slower here. This is clearer if we simplify the code a bit. Suppose we are doing `X .= colvec .* cis.(rowvec)` (i.e. combining a column vector and a row vector to make a matrix). This is lowered to `broadcast!((x,y) -> x * cis(y), X, colvec, rowvec)`, which is essentially equivalent to:

```julia
for j=1:length(rowvec), i=1:length(colvec)
    X[i,j] = colvec[i] * cis(rowvec[j])
end

```

The problem here is that if `X` is m×n, then we end up calling the `cis` function mn times.

If, instead, we use

```julia
tmp = cis.(rowvec)
X .= colvec .* tmp

```

it only calls the `cis` function n times, at the expense of requiring more storage.

This is the sort of space/time tradeoff that it is hard to automate. As a rule of thumb, however, if you are doing a broadcast operation combining a row and a column vector, it will be faster if you do any expensive operations on the row and column vector _before_ doing the broadcast combination.

In this particular case, I would suggest doing something like:

```julia
function test_perf5()
    rangeᵀ = (1:2000000)'
    rowtemp = similar(Complex128, rangeᵀ)
    steering_vectors = complex.(ones(4,11), ones(4,11)) # placeholder for actual vectors?

    sum_signal = zeros(Complex{Float64}, 4, length(rangeᵀ))
    for i = 1:11
        rowtemp .= cis.(1.6069246423111792 .* rangeᵀ .+ 0.6981317007977318)
        sum_signal .+= steering_vectors[:,i] .* rowtemp
    end
    return sum_signal
  end

```

You could also maybe try `@views`, or simply allocate `steering_vectors` as an array of vectors, to avoid allocating a copy in `steering_vectors[:,i]`. You can also use `@.` before the `for` and then omit all of the dots.

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