# How can I help Julia better allocate memory on sparse matrix operations?

**URL:** <https://discourse.julialang.org/t/how-can-i-help-julia-better-allocate-memory-on-sparse-matrix-operations/2770>\
**Category:** General Usage\
**Tags:** question, performance\
**Created:** [March 20, 2017, 7:27am UTC](https://discourse.julialang.org/t/how-can-i-help-julia-better-allocate-memory-on-sparse-matrix-operations/2770 "2017-03-20T07:27:08Z")\
**Posts on this page:** 5\
**Page:** 1

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**Author:** ![mcovalt](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/mcovalt/32/1722_2.png) [@mcovalt](https://discourse.julialang.org/u/mcovalt)\
**Post date:** [March 20, 2017, 7:27am UTC](https://discourse.julialang.org/t/how-can-i-help-julia-better-allocate-memory-on-sparse-matrix-operations/2770/1 "2017-03-20T07:27:08Z")

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I have a function in a for loop along the lines of this:

```julia
function spmat_ops!(B, C, D, E, F, G, b, x)
    # Upper case vars are SparseMatrixCSC and lower case are Vectors
    A = B*C + D*E*F + G
    x = somefastsolver(A,b)
end

function main()
    for i = 1:100
        spmat_ops!(B, C, D, E, F, G, b, x)
        b += x
    end
end

```

The `SparceMatrixCSC` operation is causing a lot of large allocations thus slowing down the code quite a bit. Is there any way to alleviate this?

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**Author:** ![JaredCrean2](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/jaredcrean2/32/3574_2.png) [@JaredCrean2](https://discourse.julialang.org/u/JaredCrean2)\
**Post date:** [March 20, 2017, 5:38pm UTC](https://discourse.julialang.org/t/how-can-i-help-julia-better-allocate-memory-on-sparse-matrix-operations/2770/2 "2017-03-20T17:38:39Z")

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What’s going on here is a bunch of temporary matrices are being constructing in the process of calculating A. To make this explicit, the line `A = B*C + D*E*F + G` gets expanded into something like:

```julia
T1 = B*C
T2 = E*F
T3 = D*T2
T4 = T1 + T2
A = T4 + G

```

So you end up generating 4 temporaries matrices, allocating a bunch of memory. What to do about this, I’m not exactly sure. Julia uses UMFPACK/SuiteSparse for sparse matrix operations and there might be a way to provide the output matrix for each operation (possibly reusing a single temporary matrix several times), but then you have to figure out the sparsity pattern of the output matrix, which is non-trivial for matrix-matrix multiplication.

Bottom line, I would look at the SuiteSparse documentation, see if the capability exists there, then have a look at julia’s base/sparse/cholmod.jl and see if the capability is exposed in julia.

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**Author:** ![mcovalt](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/mcovalt/32/1722_2.png) [@mcovalt](https://discourse.julialang.org/u/mcovalt)\
**Post date:** [March 20, 2017, 6:49pm UTC](https://discourse.julialang.org/t/how-can-i-help-julia-better-allocate-memory-on-sparse-matrix-operations/2770/3 "2017-03-20T18:49:05Z")

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Thanks for the feedback. I’ll look at the SuiteSparse docs and look through cholmod.jl.

The temp array structures should be unchanging and I do know their sparsity pattern.

I’m curious, for example, if `T1` is created, then directly modified? Or perhaps arrays `I, J, V` are created then `T1 = sparse(I, J, V)` in which case it seems like preallocating `T1` would be waste of space.

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**Author:** ![cortner](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/cortner/32/204_2.png) [@cortner](https://discourse.julialang.org/u/cortner)\
**Post date:** [March 20, 2017, 8:09pm UTC](https://discourse.julialang.org/t/how-can-i-help-julia-better-allocate-memory-on-sparse-matrix-operations/2770/4 "2017-03-20T20:09:04Z")

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> [@mcovalt](#):
>
> The temp array structures should be unchanging and I do know their sparsity pattern.

First of all I suspect you want `x[:] = ...`.

If `somefastsolver` is an iterative solver, then you are probably better off storing `A` as a type - say (I simplify because I don’t want to type so much)

```julia
type MyOperator
    B
   C
   G
end

function (Base.*)(A::MyOperator, x::AbstractVector)
   # now perform in-place sparse matrix multiplications using `A_mul_B!`
end

```

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<div class="post-metadata">

**Author:** ![mcovalt](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/mcovalt/32/1722_2.png) [@mcovalt](https://discourse.julialang.org/u/mcovalt)\
**Post date:** [March 20, 2017, 8:54pm UTC](https://discourse.julialang.org/t/how-can-i-help-julia-better-allocate-memory-on-sparse-matrix-operations/2770/5 "2017-03-20T20:54:26Z")

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Perfect, the in-place sparse functions were what I was looking for. The operator overloading is an interesting solution I might consider.