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Remove StartUpDG.jl type piracy #1665

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Oct 9, 2023
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Remove StartUpDG.jl type piracy #1665

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7d664cb
remove StartUpDG type piracy
jlchan Oct 8, 2023
c2e1db6
Merge branch 'main' into jc/StartUpDG_0.17.7
jlchan Oct 8, 2023
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330 changes: 0 additions & 330 deletions src/solvers/dgmulti/sbp.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -36,312 +36,6 @@ function DGMulti(element_type::AbstractElemShape,
surface_integral = surface_integral, volume_integral = volume_integral)
end

function construct_1d_operators(D::AbstractDerivativeOperator, tol)
nodes_1d = collect(grid(D))
M = SummationByPartsOperators.mass_matrix(D)
if M isa UniformScaling
weights_1d = M * ones(Bool, length(nodes_1d))
else
weights_1d = diag(M)
end

# StartUpDG assumes nodes from -1 to +1. Thus, we need to re-scale everything.
# We can adjust the grid spacing as follows.
xmin = SummationByPartsOperators.xmin(D)
xmax = SummationByPartsOperators.xmax(D)
factor = 2 / (xmax - xmin)
@. nodes_1d = factor * (nodes_1d - xmin) - 1
@. weights_1d = factor * weights_1d

D_1d = droptol!(inv(factor) * sparse(D), tol)
I_1d = Diagonal(ones(Bool, length(nodes_1d)))

return nodes_1d, weights_1d, D_1d, I_1d
end

function StartUpDG.RefElemData(element_type::Line,
D::AbstractDerivativeOperator;
tol = 100 * eps())
approximation_type = D
N = SummationByPartsOperators.accuracy_order(D) # kind of polynomial degree

# 1D operators
nodes_1d, weights_1d, D_1d = construct_1d_operators(D, tol)

# volume
rq = r = nodes_1d
wq = weights_1d
Dr = D_1d
M = Diagonal(wq)
Pq = LinearAlgebra.I
Vq = LinearAlgebra.I

VDM = nothing # unused generalized Vandermonde matrix

rst = (r,)
rstq = (rq,)
Drst = (Dr,)

# face
face_vertices = StartUpDG.face_vertices(element_type)
face_mask = [1, length(nodes_1d)]

rf = [-1.0; 1.0]
nrJ = [-1.0; 1.0]
wf = [1.0; 1.0]
if D isa AbstractPeriodicDerivativeOperator
# we do not need any face stuff for periodic operators
Vf = spzeros(length(wf), length(wq))
else
Vf = sparse([1, 2], [1, length(nodes_1d)], [1.0, 1.0])
end
LIFT = Diagonal(wq) \ (Vf' * Diagonal(wf))

rstf = (rf,)
nrstJ = (nrJ,)

# low order interpolation nodes
r1 = StartUpDG.nodes(element_type, 1)
V1 = StartUpDG.vandermonde(element_type, 1, r) /
StartUpDG.vandermonde(element_type, 1, r1)

return RefElemData(element_type, approximation_type, N,
face_vertices, V1,
rst, VDM, face_mask,
rst, LinearAlgebra.I, # plotting
rstq, wq, Vq, # quadrature
rstf, wf, Vf, nrstJ, # faces
M, Pq, Drst, LIFT)
end

function StartUpDG.RefElemData(element_type::Quad,
D::AbstractDerivativeOperator;
tol = 100 * eps())
approximation_type = D
N = SummationByPartsOperators.accuracy_order(D) # kind of polynomial degree

# 1D operators
nodes_1d, weights_1d, D_1d, I_1d = construct_1d_operators(D, tol)

# volume
s, r = vec.(StartUpDG.NodesAndModes.meshgrid(nodes_1d)) # this is to match
# ordering of nrstJ
rq = r
sq = s
wr, ws = vec.(StartUpDG.NodesAndModes.meshgrid(weights_1d))
wq = wr .* ws
Dr = kron(I_1d, D_1d)
Ds = kron(D_1d, I_1d)
M = Diagonal(wq)
Pq = LinearAlgebra.I
Vq = LinearAlgebra.I

VDM = nothing # unused generalized Vandermonde matrix

rst = (r, s)
rstq = (rq, sq)
Drst = (Dr, Ds)

# face
face_vertices = StartUpDG.face_vertices(element_type)
face_mask = vcat(StartUpDG.find_face_nodes(element_type, r, s)...)

rf, sf, wf, nrJ, nsJ = StartUpDG.init_face_data(element_type,
quad_rule_face = (nodes_1d, weights_1d))
if D isa AbstractPeriodicDerivativeOperator
# we do not need any face stuff for periodic operators
Vf = spzeros(length(wf), length(wq))
else
Vf = sparse(eachindex(face_mask), face_mask, ones(Bool, length(face_mask)))
end
LIFT = Diagonal(wq) \ (Vf' * Diagonal(wf))

rstf = (rf, sf)
nrstJ = (nrJ, nsJ)

# low order interpolation nodes
r1, s1 = StartUpDG.nodes(element_type, 1)
V1 = StartUpDG.vandermonde(element_type, 1, r, s) /
StartUpDG.vandermonde(element_type, 1, r1, s1)

return RefElemData(element_type, approximation_type, N,
face_vertices, V1,
rst, VDM, face_mask,
rst, LinearAlgebra.I, # plotting
rstq, wq, Vq, # quadrature
rstf, wf, Vf, nrstJ, # faces
M, Pq, Drst, LIFT)
end

function StartUpDG.RefElemData(element_type::Hex,
D::AbstractDerivativeOperator;
tol = 100 * eps())
approximation_type = D
N = SummationByPartsOperators.accuracy_order(D) # kind of polynomial degree

# 1D operators
nodes_1d, weights_1d, D_1d, I_1d = construct_1d_operators(D, tol)

# volume
# to match ordering of nrstJ
s, r, t = vec.(StartUpDG.NodesAndModes.meshgrid(nodes_1d, nodes_1d, nodes_1d))
rq = r
sq = s
tq = t
wr, ws, wt = vec.(StartUpDG.NodesAndModes.meshgrid(weights_1d, weights_1d, weights_1d))
wq = wr .* ws .* wt
Dr = kron(I_1d, I_1d, D_1d)
Ds = kron(I_1d, D_1d, I_1d)
Dt = kron(D_1d, I_1d, I_1d)
M = Diagonal(wq)
Pq = LinearAlgebra.I
Vq = LinearAlgebra.I

VDM = nothing # unused generalized Vandermonde matrix

rst = (r, s, t)
rstq = (rq, sq, tq)
Drst = (Dr, Ds, Dt)

# face
face_vertices = StartUpDG.face_vertices(element_type)
face_mask = vcat(StartUpDG.find_face_nodes(element_type, r, s, t)...)

rf, sf, tf, wf, nrJ, nsJ, ntJ = let
rf, sf = vec.(StartUpDG.NodesAndModes.meshgrid(nodes_1d, nodes_1d))
wr, ws = vec.(StartUpDG.NodesAndModes.meshgrid(weights_1d, weights_1d))
wf = wr .* ws
StartUpDG.init_face_data(element_type, quad_rule_face = (rf, sf, wf))
end
Vf = sparse(eachindex(face_mask), face_mask, ones(Bool, length(face_mask)))
LIFT = Diagonal(wq) \ (Vf' * Diagonal(wf))

rstf = (rf, sf, tf)
nrstJ = (nrJ, nsJ, ntJ)

# low order interpolation nodes
r1, s1, t1 = StartUpDG.nodes(element_type, 1)
V1 = StartUpDG.vandermonde(element_type, 1, r, s, t) /
StartUpDG.vandermonde(element_type, 1, r1, s1, t1)

return RefElemData(element_type, approximation_type, N,
face_vertices, V1,
rst, VDM, face_mask,
rst, LinearAlgebra.I, # plotting
rstq, wq, Vq, # quadrature
rstf, wf, Vf, nrstJ, # faces
M, Pq, Drst, LIFT)
end

# specialized Hex constructor in 3D to reduce memory usage.
function StartUpDG.RefElemData(element_type::Hex,
D::AbstractPeriodicDerivativeOperator;
tol = 100 * eps())
approximation_type = D
N = SummationByPartsOperators.accuracy_order(D) # kind of polynomial degree

# 1D operators
nodes_1d, weights_1d, D_1d, I_1d = construct_1d_operators(D, tol)

# volume
# to match ordering of nrstJ
s, r, t = vec.(StartUpDG.NodesAndModes.meshgrid(nodes_1d, nodes_1d, nodes_1d))
rq = r
sq = s
tq = t
wr, ws, wt = vec.(StartUpDG.NodesAndModes.meshgrid(weights_1d, weights_1d, weights_1d))
wq = wr .* ws .* wt
Dr = kron(I_1d, I_1d, D_1d)
Ds = kron(I_1d, D_1d, I_1d)
Dt = kron(D_1d, I_1d, I_1d)
M = Diagonal(wq)
Pq = LinearAlgebra.I
Vq = LinearAlgebra.I

VDM = nothing # unused generalized Vandermonde matrix

rst = (r, s, t)
rstq = (rq, sq, tq)
Drst = (Dr, Ds, Dt)

# face
# We do not need any face data for periodic operators. Thus, we just
# pass `nothing` to save memory.
face_vertices = ntuple(_ -> nothing, 3)
face_mask = nothing
wf = nothing
rstf = ntuple(_ -> nothing, 3)
nrstJ = ntuple(_ -> nothing, 3)
Vf = nothing
LIFT = nothing

# low order interpolation nodes
V1 = nothing # do not need to store V1, since we specialize StartUpDG.MeshData to avoid using it.

return RefElemData(element_type, approximation_type, N,
face_vertices, V1,
rst, VDM, face_mask,
rst, LinearAlgebra.I, # plotting
rstq, wq, Vq, # quadrature
rstf, wf, Vf, nrstJ, # faces
M, Pq, Drst, LIFT)
end

function Base.show(io::IO, mime::MIME"text/plain",
rd::RefElemData{NDIMS, ElementType, ApproximationType}) where {NDIMS,
ElementType <:
StartUpDG.AbstractElemShape,
ApproximationType <:
AbstractDerivativeOperator
}
@nospecialize rd
print(io, "RefElemData for an approximation using an ")
show(IOContext(io, :compact => true), rd.approximation_type)
print(io, " on $(rd.element_type) element")
end

function Base.show(io::IO,
rd::RefElemData{NDIMS, ElementType, ApproximationType}) where {NDIMS,
ElementType <:
StartUpDG.AbstractElemShape,
ApproximationType <:
AbstractDerivativeOperator
}
@nospecialize rd
print(io, "RefElemData{", summary(rd.approximation_type), ", ", rd.element_type, "}")
end

function StartUpDG.inverse_trace_constant(rd::RefElemData{NDIMS, ElementType,
ApproximationType}) where {NDIMS,
ElementType <:
Union{
Line,
Quad,
Hex
},
ApproximationType <:
AbstractDerivativeOperator
}
D = rd.approximation_type

# the inverse trace constant is the maximum eigenvalue corresponding to
# M_f * v = λ * M * v
# where M_f is the face mass matrix and M is the volume mass matrix.
# Since M is diagonal and since M_f is just the boundary "mask" matrix
# (which extracts the first and last entries of a vector), the maximum
# eigenvalue is the inverse of the first or last mass matrix diagonal.
left_weight = SummationByPartsOperators.left_boundary_weight(D)
right_weight = SummationByPartsOperators.right_boundary_weight(D)
max_eigenvalue = max(inv(left_weight), inv(right_weight))

# For tensor product elements, the trace constant for higher dimensional
# elements is the one-dimensional trace constant multiplied by `NDIMS`. See
# "GPU-accelerated discontinuous Galerkin methods on hybrid meshes."
# Chan, Jesse, et al (2016), https://doi.org/10.1016/j.jcp.2016.04.003
# for more details (specifically, Appendix A.1, Theorem A.4).
return NDIMS * max_eigenvalue
end

# type alias for specializing on a periodic SBP operator
const DGMultiPeriodicFDSBP{NDIMS, ApproxType, ElemType} = DGMulti{NDIMS, ElemType,
ApproxType,
Expand Down Expand Up @@ -450,30 +144,6 @@ end
@muladd begin
#! format: noindent

# This is used in `estimate_dt`. `estimate_h` uses that `Jf / J = O(h^{NDIMS-1}) / O(h^{NDIMS}) = O(1/h)`.
# However, since we do not initialize `Jf` for periodic FDSBP operators, we specialize `estimate_h`
# based on the reference grid provided by SummationByPartsOperators.jl and information about the domain size
# provided by `md::MeshData``.
function StartUpDG.estimate_h(e, rd::RefElemData{NDIMS, ElementType, ApproximationType},
md::MeshData) where {NDIMS,
ElementType <:
StartUpDG.AbstractElemShape,
ApproximationType <:
SummationByPartsOperators.AbstractPeriodicDerivativeOperator
}
D = rd.approximation_type
x = grid(D)

# we assume all SummationByPartsOperators.jl reference grids are rescaled to [-1, 1]
xmin = SummationByPartsOperators.xmin(D)
xmax = SummationByPartsOperators.xmax(D)
factor = 2 / (xmax - xmin)

# If the domain has size L^NDIMS, then `minimum(md.J)^(1 / NDIMS) = L`.
# WARNING: this is not a good estimate on anisotropic grids.
return minimum(diff(x)) * factor * minimum(md.J)^(1 / NDIMS)
end

# specialized for DGMultiPeriodicFDSBP since there are no face nodes
# and thus no inverse trace constant for periodic domains.
function estimate_dt(mesh::DGMultiMesh, dg::DGMultiPeriodicFDSBP)
Expand Down
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