In Gridap.jl, we have experienced a significant increase in compile times when moving from Julia 1.5 to Julia 1.6. To illustrate this, consider this example:
using Gridap function main() domain = (0,1,0,1,0,1); cells = (3,3,3); k = 2 γ = 10; h = 1/3 model = simplexify(CartesianDiscreteModel(domain,cells)) reffe = ReferenceFE(lagrangian,Float64,k) V = TestFESpace(model,reffe,dirichlet_tags="boundary") U = TrialFESpace(V) Ω = Triangulation(model) Γ = BoundaryTriangulation(model) Λ = SkeletonTriangulation(model) dΩ = Measure(Ω,2*k) dΓ = Measure(Γ,2*k) dΛ = Measure(Λ,2*k) n_Γ = get_normal_vector(Γ) n_Λ = get_normal_vector(Λ) a(u,v) = ∫( ∇(v)⋅∇(u) )dΩ + ∫( (γ/h)*v*u - v*(n_Γ⋅∇(u)) - (n_Γ⋅∇(v))*u )dΓ + ∫( (γ/h)*jump(v*n_Λ)⋅jump(u*n_Λ) - jump(v*n_Λ)⋅mean(∇(u)) - mean(∇(v))⋅jump(u*n_Λ) )dΛ l(v) = ∫( v )dΩ op = AffineFEOperator(a,l,U,V) uh = solve(op) end
takes 92.353941 (Julia 1.5) vs 221.752225 (Julia 1.6) in a fresh session. So 2.4x increase. This is a major problem for us since 92 seconds was already a long compilation time.
I believe that Julia (i.e. inference time) is not to blame for the sudden increase. By running this in Julia 1.6:
julia> using SnoopCompile julia> tinf = @snoopi_deep main() InferenceTimingNode: 165.115502/226.610818 on InferenceFrameInfo for Core.Compiler.Timings.ROOT() with 1385 direct children
I get that 226.61081 is total compile time and 165.115502 is time in all phases except inference. Thus, inference time alone (61 seconds) does not explain the increase.
Which actions we need to take to cut down the compile time that does not come from inference?
SnoopCompile Has a very nice tutorial on how to cut down inference times, but how to cut other phases?
We are looking forward for help since this is a major issue for us!