Coloring benchmark problem about sparsedifftools.jl HOT 7 CLOSED

Oh, I cut SciML/SciMLBenchmarks.jl#91 a little too aggressively.

Mx = Tridiagonal([1.0 for i in 1:N-1],[-2.0 for i in 1:N],[1.0 for i in 1:N-1])
My = copy(Mx)

from sparsedifftools.jl.

what are Mx and My here?

from sparsedifftools.jl.

For the record, recorded 600 seconds initially (otherwise I'll forget and have to recompute it)

julia> @time colorvec = matrix_colors(jacsparsity)
683.859562 seconds (1.10 M allocations: 121.140 MiB, 0.01% gc time)

from sparsedifftools.jl.

It's the whole matrix to graph function which is slow, the rest can wait:

julia> @time matrix2graph(jacsparsity, false)
475.198843 seconds (719.90 k allocations: 100.197 MiB, 0.01% gc time)

from sparsedifftools.jl.

two potential speedups already: dropzeros should be the inplace version, the non-inplace currently used returns a value that is ignored

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since the indices are known, @inbounds should be used throughout the function

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#94 is a first take on that, I think there may be a way to improve this even more

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