# Spdiagm allocation madness
**URL:** https://discourse.julialang.org/t/spdiagm-allocation-madness/52703
**Category:** Performance
**Created:** [January 1, 2021, 6:39pm UTC](https://discourse.julialang.org/t/spdiagm-allocation-madness/52703 "2021-01-01T18:39:50Z")
**Posts on this page:** 3
**Page:** 1
### Author:  [@ctkelley](https://discourse.julialang.org/u/ctkelley)
#### Post date: [January 1, 2021, 6:39pm UTC](https://discourse.julialang.org/t/spdiagm-allocation-madness/52703/1 "2021-01-01T18:39:50Z")
I am seeing surprisingly large allocations from `spdiagm`. The compute
times are ok for what I need to do, so perhaps this is a case where large
allocations do not harm. Here’s the story.
I compute the discrete negative Laplacian with homogeneous Dirichlet
boundary conditions on the unit square with an n x n grid.
```julia
function Lap2d(n)
h=1/(n+1);
maindiag=4*ones(n^2,)/(h*h);
sxdiag=-ones(n^2-1,)/(h*h);
sydiag=-ones(n^2-n,)/(h*h);
for iz=n:n:n^2-1
sxdiag[iz]=0.0;
end
L2d=spdiagm(-n => sydiag, -1 => sxdiag, 0=> maindiag,
1 => sxdiag, n => sydiag);
return L2d
end
```
The sparse matrix should take rougly 5 vectors or 40 n^2 bytes.
Not exactly …
```julia
julia> n=1000;
julia> @btime Lap2d($n);
129.733 ms (81 allocations: 358.46 MiB)
```
So I need 40 MB to store the matrix and am allocating 9 times that.
Why?
The timings are fast enough for what I need, so performance is fine.
---
### Author:  [@mikkoku](https://discourse.julialang.org/u/mikkoku)
#### Post date: [January 2, 2021, 8:12pm UTC](https://discourse.julialang.org/t/spdiagm-allocation-madness/52703/2 "2021-01-02T20:12:12Z")
`spdiagm` uses `sparse!` to create a `SparseMatrixCSC`. The result size will be 100 MB instead of 40 MB. It would be possible to construct the CSC representation directly and avoid all extra allocations, but it might not be worth the trouble.
Multiplying a matrix with a scalar allocates a new matrix for the result. Use `fill` instead of `ones`.
---
### Author:  [@ctkelley](https://discourse.julialang.org/u/ctkelley)
#### Post date: [January 2, 2021, 10:07pm UTC](https://discourse.julialang.org/t/spdiagm-allocation-madness/52703/3 "2021-01-02T22:07:14Z")
That’s progress. Thanks. This version save me some storage and is a somewhat faster.
```julia
function Lap2d(n)
h=1/(n+1);
hm2=(n+1.0)^2;
maindiag=fill(4*hm2,(n^2,));
sxdiag=fill(-hm2,(n^2-1,));
sydiag=fill(-hm2,(n^2-n,));
for iz=n:n:n^2-1
sxdiag[iz]=0.0;
end
L2d=spdiagm(-n => sydiag, -1 => sxdiag, 0=> maindiag,
1 => sxdiag, n => sydiag);
return L2d
end
```
```julia
julia> @btime Lap2d(1000);
99.829 ms (69 allocations: 312.69 MiB)
```