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Sort SparseCSC elements function and speed up sparse matrix multiplication #8316

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Sort SparseCSC elements function and speed up sparse matrix multiplication #8316

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b43d288
Adding a function to sort the row values and nzvalues in a CSC matrix…
Sep 11, 2014
bd38148
Corrected scope and indentation, I hope
Sep 12, 2014
7314a38
Functional sorting of the matrix rather than the dual transpose. This…
Sep 17, 2014
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2 changes: 1 addition & 1 deletion base/linalg.jl
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
module LinAlg

importall Base
import Base: USE_BLAS64, size, copy, copy_transpose!, power_by_squaring, print_matrix, transpose!
import Base: USE_BLAS64, size, copy, copy_transpose!, power_by_squaring, print_matrix, transpose!, sortCSC!

export
# Modules
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6 changes: 3 additions & 3 deletions base/linalg/sparse.jl
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Expand Up @@ -190,9 +190,9 @@ function *{Tv,Ti}(A::SparseMatrixCSC{Tv,Ti}, B::SparseMatrixCSC{Tv,Ti})
deleteat!(nzvalC, colptrC[end]:length(nzvalC))

# The Gustavson algorithm does not guarantee the product to have sorted row indices.
Cunsorted = SparseMatrixCSC(mA, nB, colptrC, rowvalC, nzvalC)
Ct = Cunsorted.'
Ctt = Base.SparseMatrix.transpose!(Ct, SparseMatrixCSC(mA, nB, colptrC, rowvalC, nzvalC))
C = SparseMatrixCSC(mA, nB, colptrC, rowvalC, nzvalC)
sortCSC!(C)
return C
end

## solvers
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2 changes: 1 addition & 1 deletion base/sparse.jl
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Expand Up @@ -8,7 +8,7 @@ import Base.NonTupleType, Base.float, Base.Order, Base.Sort.Forward
export SparseMatrixCSC,
blkdiag, dense, diag, diagm, droptol!, dropzeros!, etree, full,
getindex, ishermitian, issparse, issym, istril, istriu, nnz,
setindex!, sparse, sparsevec, spdiagm, speye, spones,
setindex!, sortCSC!, sparse, sparsevec, spdiagm, speye, spones,
sprand, sprandbool, sprandn, spzeros, symperm, trace, tril, tril!,
triu, triu!, nonzeros

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40 changes: 40 additions & 0 deletions base/sparse/sparsematrix.jl
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Expand Up @@ -177,6 +177,46 @@ function full{Tv}(S::SparseMatrixCSC{Tv})
return A
end

function sortCSC!{Tv,Ti}(A::SparseMatrixCSC{Tv,Ti})
m, n = size(A)

colptr = A.colptr; rowval = A.rowval; nzval = A.nzval

# Crude estimate for a preallocation
rows = min(int(((A.colptr[end] - 1)/A.n)*2), m)
row = Array(Ti, rows)
val = Array(Tv, rows)

for i = 1:n
@inbounds col_start = colptr[i]
@inbounds col_end = (colptr[i+1] - 1)

numrows = int((col_end - col_start) + 1)
if numrows > rows
rows = numrows
row = Array(Ti, numrows)
val = Array(Tv, numrows)
end

jj = 1
@simd for j = col_start:col_end
@inbounds row[jj] = rowval[j]
@inbounds val[jj] = nzval[j]
jj += 1
end

index = sortperm(row[1:numrows])

jj = 1;
@simd for j = col_start:col_end
@inbounds rowval[j] = row[index[jj]]
@inbounds nzval[j] = val[index[jj]]
jj += 1
end
end
end


float(S::SparseMatrixCSC) = SparseMatrixCSC(S.m, S.n, copy(S.colptr), copy(S.rowval), float(copy(S.nzval)))

complex(S::SparseMatrixCSC) = SparseMatrixCSC(S.m, S.n, copy(S.colptr), copy(S.rowval), complex(copy(S.nzval)))
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