comment + fmt

ext/TrixiConvexECOSExt.jl

@@ -11,16 +11,29 @@ else
     using ..ECOS
 end
 
+# Use other necessary libraries
+using LinearAlgebra: eigvals
+const MOI = Convex.MOI
+
 # Use all exported symbols to avoid having to rewrite `recipes_makie.jl`
+using Trixi
+
+# Use additional symbols that are not exported
 using Trixi: PolynomialOptimizer
 
+# Import functions such that they can be extended with new methods
 import .PolynomialOptimizer: filter_eigvals, bisection, undo_normalization!
 
-using LinearAlgebra: eigvals
-const MOI = Convex.MOI
+# 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
+#! format: noindent
 
 function stability_polynomials(cons_order, num_stage_evals, num_eig_vals,
-                               normalized_powered_eigvals_scaled, pnoms, gamma::Variable)
+                               normalized_powered_eigvals_scaled, pnoms,
+                               gamma::Variable)
     # Initialize with zero'th order (z^0) coefficient
     for i in 1:num_eig_vals
         pnoms[i] = 1.0
@@ -87,13 +100,16 @@ function bisection(cons_order, num_eig_vals, num_stage_evals, dtmax, dteps, eig_
             dt_k = dt^k
             for i in 1:num_eig_vals
                 normalized_powered_eigvals_scaled[i, k] = dt_k *
-                                                          normalized_powered_eigvals[i, k]
+                                                          normalized_powered_eigvals[i,
+                                                                                     k]
             end
         end
 
         # Use last optimal values for gamma in (potentially) next iteration
-        problem = minimize(stability_polynomials(cons_order, num_stage_evals, num_eig_vals,
-                                                 normalized_powered_eigvals_scaled, pnoms,
+        problem = minimize(stability_polynomials(cons_order, num_stage_evals,
+                                                 num_eig_vals,
+                                                 normalized_powered_eigvals_scaled,
+                                                 pnoms,
                                                  gamma))
 
         Convex.solve!(problem,
@@ -123,5 +139,6 @@ function bisection(cons_order, num_eig_vals, num_stage_evals, dtmax, dteps, eig_
 
     return evaluate(gamma), dt
 end
+end # @muladd
 
-end # module
+end # module TrixiConvexECOSExt

src/time_integration/paired_explicit_runge_kutta/methods_PERK2.jl

@@ -5,9 +5,8 @@
 using DelimitedFiles: readdlm
 using LinearAlgebra: eigvals
 using .PolynomialOptimizer
-import .PolynomialOptimizer: filter_eigvals, bisection, undo_normalization!
 
-#Fake change
+import .PolynomialOptimizer: filter_eigvals, undo_normalization!, bisection
 
 @muladd begin
 #! format: noindent

src/time_integration/paired_explicit_runge_kutta/polynomial_optimizer.jl

@@ -1,10 +1,11 @@
 module PolynomialOptimizer
 
-#using ECOS
-#using Convex
-#const MOI = Convex.MOI
+# implemented here
+export filter_eigvals, undo_normalization!
+
+# implemented in TrixiConvexExt and only usable when user adds Convex
+export stability_polynomials, bisection
 
-export filter_eigvals
 function filter_eigvals(eig_vals, threshold)
     filtered_eigvals_counter = 0
     filtered_eig_vals = Complex{Float64}[]
@@ -26,22 +27,15 @@ function filter_eigvals(eig_vals, threshold)
     return length(filtered_eig_vals), filtered_eig_vals
 end
 
-# implemented in TrixiConvexExt
-export stability_polynomials
-function stability_polynomials end
-
-export bisection
-function bisection end
-
-
-
-export undo_normalization!
-
 function undo_normalization!(cons_order, num_stage_evals, gamma_opt)
     for k in (cons_order + 1):num_stage_evals
         gamma_opt[k - cons_order] = gamma_opt[k - cons_order] / factorial(k)
     end
     return gamma_opt
 end
 
-end # module PolynomialOptimizer
+function stability_polynomials end
+
+function bisection end
+
+end # module PolynomialOptimizer