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Fix type conversion and type instability in MortarL2 #2242

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merged 2 commits into from
Jan 20, 2025
Merged

Fix type conversion and type instability in MortarL2 #2242

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57938ef
Fix
huiyuxie Jan 20, 2025
944b7c3
Fix
huiyuxie Jan 20, 2025
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20 changes: 8 additions & 12 deletions src/solvers/dgsem/basis_lobatto_legendre.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -159,17 +159,15 @@ function MortarL2(basis::LobattoLegendreBasis)
RealT = real(basis)
nnodes_ = nnodes(basis)

forward_upper_ = calc_forward_upper(nnodes_, RealT)
forward_lower_ = calc_forward_lower(nnodes_, RealT)
reverse_upper_ = calc_reverse_upper(nnodes_, Val(:gauss), RealT)
reverse_lower_ = calc_reverse_lower(nnodes_, Val(:gauss), RealT)
forward_upper = calc_forward_upper(nnodes_, RealT)
forward_lower = calc_forward_lower(nnodes_, RealT)
reverse_upper = calc_reverse_upper(nnodes_, Val(:gauss), RealT)
reverse_lower = calc_reverse_lower(nnodes_, Val(:gauss), RealT)

# type conversions to get the requested real type and enable possible
# optimizations of runtime performance and latency

# Usually as fast as `SMatrix` but better for latency
forward_upper = Matrix{RealT}(forward_upper_)
forward_lower = Matrix{RealT}(forward_lower_)
# We keep the matrices above stored using the standard `Matrix` type
# since this is usually as fast as `SMatrix`
# (when using `let` in the volume integral/`@threaded`)
# and reduces latency

# TODO: Taal performance
# Check the performance of different implementations of `mortar_fluxes_to_elements!`
Expand All @@ -179,8 +177,6 @@ function MortarL2(basis::LobattoLegendreBasis)
# `@tullio` when the matrix sizes are not necessarily static.
# reverse_upper = SMatrix{nnodes_, nnodes_, RealT, nnodes_^2}(reverse_upper_)
# reverse_lower = SMatrix{nnodes_, nnodes_, RealT, nnodes_^2}(reverse_lower_)
reverse_upper = Matrix{RealT}(reverse_upper_)
reverse_lower = Matrix{RealT}(reverse_lower_)

LobattoLegendreMortarL2{RealT, nnodes_, typeof(forward_upper),
typeof(reverse_upper)}(forward_upper, forward_lower,
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20 changes: 10 additions & 10 deletions src/solvers/dgsem/l2projection.jl
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Expand Up @@ -29,9 +29,9 @@ function calc_forward_upper(n_nodes, RealT = Float64)
wbary = barycentric_weights(nodes)

# Calculate projection matrix (actually: interpolation)
operator = zeros(n_nodes, n_nodes)
operator = zeros(RealT, n_nodes, n_nodes)
for j in 1:n_nodes
poly = lagrange_interpolating_polynomials(1 / 2 * (nodes[j] + 1), nodes, wbary)
poly = lagrange_interpolating_polynomials(0.5f0 * (nodes[j] + 1), nodes, wbary)
for i in 1:n_nodes
operator[j, i] = poly[i]
end
Expand All @@ -49,9 +49,9 @@ function calc_forward_lower(n_nodes, RealT = Float64)
wbary = barycentric_weights(nodes)

# Calculate projection matrix (actually: interpolation)
operator = zeros(n_nodes, n_nodes)
operator = zeros(RealT, n_nodes, n_nodes)
for j in 1:n_nodes
poly = lagrange_interpolating_polynomials(1 / 2 * (nodes[j] - 1), nodes, wbary)
poly = lagrange_interpolating_polynomials(0.5f0 * (nodes[j] - 1), nodes, wbary)
for i in 1:n_nodes
operator[j, i] = poly[i]
end
Expand All @@ -70,12 +70,12 @@ function calc_reverse_upper(n_nodes, ::Val{:gauss}, RealT = Float64)
gauss_wbary = barycentric_weights(gauss_nodes)

# Calculate projection matrix (actually: discrete L2 projection with errors)
operator = zeros(n_nodes, n_nodes)
operator = zeros(RealT, n_nodes, n_nodes)
for j in 1:n_nodes
poly = lagrange_interpolating_polynomials(1 / 2 * (gauss_nodes[j] + 1),
poly = lagrange_interpolating_polynomials(0.5f0 * (gauss_nodes[j] + 1),
gauss_nodes, gauss_wbary)
for i in 1:n_nodes
operator[i, j] = 1 / 2 * poly[i] * gauss_weights[j] / gauss_weights[i]
operator[i, j] = 0.5f0 * poly[i] * gauss_weights[j] / gauss_weights[i]
end
end

Expand All @@ -97,12 +97,12 @@ function calc_reverse_lower(n_nodes, ::Val{:gauss}, RealT = Float64)
gauss_wbary = barycentric_weights(gauss_nodes)

# Calculate projection matrix (actually: discrete L2 projection with errors)
operator = zeros(n_nodes, n_nodes)
operator = zeros(RealT, n_nodes, n_nodes)
for j in 1:n_nodes
poly = lagrange_interpolating_polynomials(1 / 2 * (gauss_nodes[j] - 1),
poly = lagrange_interpolating_polynomials(0.5f0 * (gauss_nodes[j] - 1),
gauss_nodes, gauss_wbary)
for i in 1:n_nodes
operator[i, j] = 1 / 2 * poly[i] * gauss_weights[j] / gauss_weights[i]
operator[i, j] = 0.5f0 * poly[i] * gauss_weights[j] / gauss_weights[i]
end
end

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