Optimize allocs

Author: gdalle

src/coloring.jl

@@ -138,21 +138,27 @@ Encode a set of 2-colored stars resulting from the [`star_coloring`](@ref) algor
 
 $TYPEDFIELDS
 """
-struct StarSet
+struct StarSet{V<:AbstractVector{Int}}
     "a mapping from edges (pair of vertices) to their star index"
     star::Vector{Int}
     "a mapping from star indices to their hub (undefined hubs for single-edge stars are the negative value of one of the vertices, picked arbitrarily)"
     hub::Vector{Int}
     "a mapping from star indices to the vector of their spokes"
-    spokes::Vector{Vector{Int}}
+    spokes::Vector{V}
 end
 
 function StarSet(
     g::AdjacencyGraph, star::Vector{Int}, hub::Vector{Int}, nb_spokes::Vector{Int}
 )
     (; S, edgeindex) = g
     # Create a list of spokes for each star, preallocating their sizes based on nb_spokes
-    spokes = [Vector{Int}(undef, ns) for ns in nb_spokes]
+    spokes_storage = Vector{Int}(undef, nb_edges(g))
+    spokes = Vector{typeof(view(spokes_storage, 1:0))}(undef, length(nb_spokes))
+    cum_nb_spokes = 0
+    for (s, ns) in enumerate(nb_spokes)
+        spokes[s] = view(spokes_storage, (cum_nb_spokes + 1):(cum_nb_spokes + ns))
+        cum_nb_spokes += ns
+    end
 
     # Reuse nb_spokes as counters to track the current index while filling the spokes
     fill!(nb_spokes, 0)

src/order.jl

@@ -209,6 +209,7 @@ function vertices(
         u = pop_next_candidate!(db; direction)
         direction == :low2high ? push!(π, u) : pushfirst!(π, u)
         for v in neighbors(g, u)
+            v == u && continue
             already_ordered(db, v) && continue
             update_bucket!(db, v; degtype, direction)
         end

test/order.jl

@@ -35,7 +35,7 @@ rng = StableRNG(63)
 end;
 
 @testset "RandomOrder" begin
-    A = sprand(rng, Bool, 10, 10, 0.5)
+    A = sparse(Symmetric(sprand(rng, Bool, 10, 10, 0.5)))
     ag = AdjacencyGraph(A)
     @test sort(vertices(ag, RandomOrder(rng))) == 1:10
     @test sort(vertices(ag, RandomOrder())) == 1:10
@@ -63,7 +63,7 @@ end;
 @testset "LargestFirst" begin
     A = sparse([
         0 1 0
-        1 0 0
+        1 0 1
         0 1 0
     ])
     ag = AdjacencyGraph(A)