use SnoopPrecompile

Project.toml

@@ -27,6 +27,7 @@ Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
 Setfield = "efcf1570-3423-57d1-acb7-fd33fddbac46"
+SnoopPrecompile = "66db9d55-30c0-4569-8b51-7e840670fc0c"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 StartUpDG = "472ebc20-7c99-4d4b-9470-8fde4e9faa0f"
 Static = "aedffcd0-7271-4cad-89d0-dc628f76c6d3"
@@ -62,6 +63,7 @@ Reexport = "1.0"
 Requires = "1.1"
 SciMLBase = "1.65"
 Setfield = "0.8, 1"
+SnoopPrecompile = "1"
 StartUpDG = "0.14"
 Static = "0.3, 0.4, 0.5, 0.6, 0.7, 0.8"
 StaticArrays = "1"

src/Trixi.jl

@@ -283,8 +283,13 @@ function __init__()
 end
 
 
-include("auxiliary/precompile.jl")
-_precompile_manual_()
+# Use the old precompile statements on Julia v1.7 and only switch to the newer
+# ones based on SnoopPrecompile.jl for Julia v1.8 and newer.
+@static if VERSION < v"1.8"
+  include("auxiliary/precompile_old.jl")
+else
+  include("auxiliary/precompile_new.jl")
+end
 
 
 end

src/auxiliary/precompile_new.jl

@@ -0,0 +1,90 @@
+# This file is used to generate precompile statements. This increases the
+# precompilation time but decreases the time to first simulation - at least
+# when precompiled methods can be reused.
+# See https://timholy.github.io/SnoopCompile.jl/stable/snoop_pc/
+
+using SnoopPrecompile: @precompile_all_calls
+
+@precompile_all_calls begin
+  # 2D
+  let
+    equations = CompressibleEulerEquations2D(1.4)
+    solver = DGSEM(polydeg=3, surface_flux=flux_lax_friedrichs)
+
+    coordinates_min = (0.0, 0.0)
+    coordinates_max = (2.0, 2.0)
+    refinement_patches = (
+         (type="box", coordinates_min=(0.0, 0.0), coordinates_max=(1.0, 1.0)),
+       )
+    mesh = TreeMesh(coordinates_min, coordinates_max,
+                    initial_refinement_level=1,
+                    refinement_patches=refinement_patches,
+                    n_cells_max=100)
+
+    semi = SemidiscretizationHyperbolic(mesh, equations, initial_condition_convergence_test, solver,
+                                        source_terms=source_terms_convergence_test)
+
+    tspan = (0.0, 2.0)
+    ode = semidiscretize(semi, tspan)
+
+    du_ode = similar(ode.u0)
+    Trixi.rhs!(du_ode, ode.u0, semi, tspan[1])
+
+    summary_callback = SummaryCallback()
+
+    analysis_interval = 100
+    analysis_callback = AnalysisCallback(semi, interval=analysis_interval)
+    let u_ode = ode.u0
+      GC.@preserve u_ode du_ode begin
+        u = Trixi.wrap_array(u_ode, semi)
+        du = Trixi.wrap_array(du_ode, semi)
+        redirect_stdout(devnull) do
+          analysis_callback.affect!(devnull, du, u, u_ode, first(tspan), semi)
+        end
+      end
+    end
+
+    alive_callback = AliveCallback(analysis_interval=analysis_interval)
+  end
+
+  # 3D
+  let
+    equations = CompressibleEulerEquations3D(1.4)
+    solver = DGSEM(polydeg=3, surface_flux=flux_lax_friedrichs)
+
+    coordinates_min = (0.0, 0.0, 0.0)
+    coordinates_max = (2.0, 2.0, 2.0)
+    refinement_patches = (
+         (type="box", coordinates_min=(0.0, 0.0, 0.0), coordinates_max=(1.0, 1.0, 1.0)),
+       )
+    mesh = TreeMesh(coordinates_min, coordinates_max,
+                    initial_refinement_level=1,
+                    refinement_patches=refinement_patches,
+                    n_cells_max=100)
+
+    semi = SemidiscretizationHyperbolic(mesh, equations, initial_condition_convergence_test, solver,
+                                        source_terms=source_terms_convergence_test)
+
+    tspan = (0.0, 2.0)
+    ode = semidiscretize(semi, tspan)
+
+    du_ode = similar(ode.u0)
+    Trixi.rhs!(du_ode, ode.u0, semi, tspan[1])
+
+    summary_callback = SummaryCallback()
+
+    analysis_interval = 100
+    analysis_callback = AnalysisCallback(semi, interval=analysis_interval)
+    let u_ode = ode.u0
+      GC.@preserve u_ode du_ode begin
+        u = Trixi.wrap_array(u_ode, semi)
+        du = Trixi.wrap_array(du_ode, semi)
+        redirect_stdout(devnull) do
+          analysis_callback.affect!(devnull, du, u, u_ode, first(tspan), semi)
+        end
+      end
+    end
+
+    alive_callback = AliveCallback(analysis_interval=analysis_interval)
+  end
+end

src/auxiliary/precompile.jl → src/auxiliary/precompile_old.jl

@@ -1,10 +1,3 @@
-# By default, Julia/LLVM does not use fused multiply-add operations (FMAs).
-# Since these FMAs can increase the performance of many numerical algorithms,
-# we need to opt-in explicitly.
-# See https://ranocha.de/blog/Optimizing_EC_Trixi for further details.
-@muladd begin
-
-
 #=
 The code contained in this file is inspired by an analysis performed
 using SnoopCompile, although most of it is written manually.
@@ -476,6 +469,3 @@ function _precompile_manual_()
 
   return nothing
 end
-
-
-end # @muladd