Examples & Benchmarks

Matrix creation example

An ExtendableSparseMatrix can serve as a drop-in replacement for SparseMatrixCSC, albeit with faster assembly during the buildup phase when using index based access.

Let us define a simple assembly loop

function assemble(A)
    n = size(A, 1)
    for i = 1:(n - 1)
        A[i + 1, i] += -1
        A[i, i + 1] += -1
        A[i, i] += 1
        A[i + 1, i + 1] += 1
    end
end;

assemble (generic function with 1 method)

Measure the time (in seconds) for assembling a SparseMatrixCSC:

t_csc = @belapsed begin
    A = spzeros(10_000, 10_000)
    assemble(A)
end

0.018505924

An ExtendableSparseMatrix can be used as a drop-in replacement. However, before any other use, this needs an internal structure rebuild which is invoked by the flush! method.

t_ext = @belapsed begin
    A = ExtendableSparseMatrix(10_000, 10_000)
    assemble(A)
    flush!(A)
end

0.001303954

All specialized methods of linear algebra functions (e.g. \) for ExtendableSparseMatrix call flush! before proceeding.

The overall time gain from using ExtendableSparse is:

t_ext / t_csc

0.07046143710522101

The reason for this situation is that the SparseMatrixCSC struct just contains the data for storing the matrix in the compressed column format. Inserting a new entry in this storage scheme is connected with serious bookkeeping and shifts of large portions of array content.

Julia provides the sparse method which uses an intermediate storage of the data in two index arrays and a value array, the so called coordinate (or COO) format:

function assemble_coo(n)
    I = zeros(Int64, 0)
    J = zeros(Int64, 0)
    V = zeros(0)
    function update(i, j, v)
        push!(I, i)
        push!(J, j)
        push!(V, v)
    end
    for i = 1:(n - 1)
        update(i + 1, i, -1)
        update(i, i + 1, -1)
        update(i, i, 1)
        update(i + 1, i + 1, 1)
    end
    sparse(I, J, V)
end;

t_coo = @belapsed assemble_coo(10_000)

0.000791984

While more convenient to use, the assembly based on ExtendableSparseMatrix is only slightly slower:

t_ext / t_coo

1.6464398270672134

Below one finds a more elaborate discussion for a quasi-3D problem.

Matrix creation benchmark

The method fdrand creates a matrix similar to the discretization matrix of a Poisson equation on a d-dimensional cube. The approach is similar to that of a typical finite element code: calculate a local stiffness matrix and assemble it into the global one.

Benchmark for `ExtendableSparseMatrix`

The code uses the index access API for the creation of the matrix, inserting elements via A[i,j]+=v, using an intermediate linked list structure which upon return ist flushed into a SparseMatrixCSC structure.

@belapsed fdrand(30, 30, 30, matrixtype = ExtendableSparseMatrix)

0.009990547

Benchmark for SparseMatrixCSC

Here, for comparison we use a SparseMatrixCSC created with spzeros and insert entries via A[i,j]+=v.

@belapsed fdrand(30, 30, 30, matrixtype = SparseMatrixCSC)

0.378055744

Benchmark for intermediate coordinate format

A SparseMatrixCSC is created by accumulating data into arrays I,J,A and calling sparse(I,J,A)

@belapsed fdrand(30, 30, 30, matrixtype = :COO)

0.008418403

This is nearly on par with matrix creation via ExtendableSparseMatrix, but the later can be made faster:

Benchmark for `ExtendableSparseMatrix` with `updateindex`

Here, we use a ExtendableSparseMatrix created withspzerosand insert entries viaupdateindex(A,+,v,i,j)`, see the discussion below.

@belapsed fdrand(30, 30, 30,
                 matrixtype = ExtendableSparseMatrix,
                 update = (A, v, i, j) -> updateindex!(A, +, v, i, j))

0.007806045

Matrix update benchmark

For repeated calculations on the same sparsity structure (e.g. for time dependent problems or Newton iterations) it is convenient to skip all but the first creation steps and to just replace the values in the matrix after setting the elements of the nzval vector to zero. Typically in finite element and finite volume methods this step updates matrix entries (most of them several times) by adding values. In this case, the current indexing interface of Julia requires to access the matrix twice:

A = spzeros(3, 3)
Meta.@lower A[1, 2] += 3

:($(Expr(:thunk, CodeInfo(
    @ none within `top-level scope`
1 ─ %1 = +
│   %2 = Base.getindex(A, 1, 2)
│   %3 = (%1)(%2, 3)
│        Base.setindex!(A, %3, 1, 2)
└──      return %3
))))

For sparse matrices this requires to perform the index search in the structure twice. The packages provides the method updateindex! for both SparseMatrixCSC and for ExtendableSparse which allows to update a matrix element with just one index search.

Benchmark for `SparseMatrixCSC`

A = fdrand(30, 30, 30; matrixtype = SparseMatrixCSC)
@belapsed fdrand!(A, 30, 30, 30,
                  update = (A, v, i, j) -> A[i, j] += v)

0.005200063

A = fdrand(30, 30, 30; matrixtype = SparseMatrixCSC)
@belapsed fdrand!(A, 30, 30, 30,
                  update = (A, v, i, j) -> updateindex!(A, +, v, i, j))

0.001958189

Benchmark for `ExtendableSparseMatrix`

A = fdrand(30, 30, 30; matrixtype = ExtendableSparseMatrix)
@belapsed fdrand!(A, 30, 30, 30,
                  update = (A, v, i, j) -> A[i, j] += v)

0.004669929

A = fdrand(30, 30, 30; matrixtype = ExtendableSparseMatrix)
@belapsed fdrand!(A, 30, 30, 30,
                  update = (A, v, i, j) -> updateindex!(A, +, v, i, j))

0.0025142

Note that the update process for ExtendableSparse may be slightly slower than for SparseMatrixCSC due to the overhead which comes from checking the presence of new entries.