some allocation tests in /scratch_code

CovidAlertVaccinationModel/Manifest.toml

@@ -1184,14 +1184,6 @@ git-tree-sha1 = "7c8974c7de377a2dc67e778017c78f96fc8f0fc6"
 uuid = "3d5dd08c-fd9d-11e8-17fa-ed2836048c2f"
 version = "0.20.16"
 
-[[VectorizedRNG]]
-deps = ["Distributed", "Random", "UnPack", "VectorizationBase"]
-git-tree-sha1 = "0a65169fbbf9c94d2136ba75946e73a88af3b491"
-repo-rev = "master"
-repo-url = "https://github.com/JuliaSIMD/VectorizedRNG.jl.git"
-uuid = "33b4df10-0173-11e9-2a0c-851a7edac40e"
-version = "0.2.11"
-
 [[VersionParsing]]
 git-tree-sha1 = "80229be1f670524750d905f8fc8148e5a8c4537f"
 uuid = "81def892-9a0e-5fdd-b105-ffc91e053289"

CovidAlertVaccinationModel/Project.toml

@@ -43,7 +43,6 @@ Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 ThreadsX = "ac1d9e8a-700a-412c-b207-f0111f4b6c0d"
 UnPack = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
-VectorizedRNG = "33b4df10-0173-11e9-2a0c-851a7edac40e"
 ZeroWeightedDistributions = "24733ad3-391a-4e41-8839-c7177de7dea4"
 
 [compat]

CovidAlertVaccinationModel/src/ABM/contact_vectors.jl

@@ -24,7 +24,6 @@ end
     return csum
 end
 
-
 """
     generate_contact_vectors!(ij_dist<:Distribution,ji_dist<:Distribution,i_to_j_contacts::Vector{T}, j_to_i_contacts::Vector{T})
 
@@ -42,10 +41,10 @@ function generate_contact_vectors!(ij_dist,ji_dist,i_to_j_contacts::AbstractVect
     csum = sum(i_to_j_contacts) - sum(j_to_i_contacts)
 
     inner_iter = 10
-    index_list_i = @MVector zeros(Int,inner_iter)
-    index_list_j = similar(index_list_i)
-    sample_list_i = @MVector zeros(T,inner_iter)
-    sample_list_j = similar(sample_list_i)
+    index_list_i = MArray{Tuple{10,},Int,1,10}(undef)
+    index_list_j = MArray{Tuple{10,},Int,1,10}(undef)
+    sample_list_i = MArray{Tuple{10,},Int,1,10}(undef)
+    sample_list_j = MArray{Tuple{10,},Int,1,10}(undef)
     
     while csum != 0
         sample!(Random.default_rng(Threads.threadid()),1:l_i,index_list_i)

CovidAlertVaccinationModel/src/ABM/mixing_graphs.jl

@@ -84,16 +84,16 @@ function time_dep_mixing_graphs(len,base_network,demographics,index_vectors,ws_m
 
     ws_static_edges = WeightedGraph(demographics,index_vectors,ws_matrix_tuple.daily,contact_time_distributions.ws)
     ws_weekly_edges = WeightedGraph(demographics,index_vectors,ws_matrix_tuple.twice_a_week,contact_time_distributions.ws)
-    ws_daily_edges = WeightedGraph(demographics,index_vectors,ws_matrix_tuple.otherwise,contact_time_distributions.ws)
+    ws_justonce_edges = WeightedGraph(demographics,index_vectors,ws_matrix_tuple.otherwise,contact_time_distributions.ws)
 
     rest_static_edges = WeightedGraph(demographics,index_vectors,rest_matrix_tuple.daily,contact_time_distributions.rest)
     rest_weekly_edges = WeightedGraph(demographics,index_vectors,rest_matrix_tuple.twice_a_week,contact_time_distributions.rest)
-    rest_daily_edges = WeightedGraph(demographics,index_vectors,rest_matrix_tuple.otherwise,contact_time_distributions.rest)
+    rest_justonce_edges = WeightedGraph(demographics,index_vectors,rest_matrix_tuple.otherwise,contact_time_distributions.rest)
    
-    inf_network_list = [home_static_edges,rest_static_edges,ws_daily_edges,rest_daily_edges] 
+    inf_network_list = [home_static_edges,rest_static_edges,ws_justonce_edges,rest_justonce_edges] 
     soc_network_list = [home_static_edges,rest_static_edges,ws_static_edges]
 
-    remade_graphs = (ws_daily_edges,rest_daily_edges)
+    remade_graphs = (ws_justonce_edges,rest_justonce_edges)
     resampled_graphs = (home_static_edges,rest_static_edges,ws_static_edges,rest_weekly_edges,ws_weekly_edges)
 
     infected_mixing_graph = TimeDepMixingGraph(len,remade_graphs,resampled_graphs,inf_network_list)

CovidAlertVaccinationModel/src/CovidAlertVaccinationModel.jl

@@ -26,7 +26,6 @@ import Pandas: read_csv
 using DataFrames
 using StaticArrays
 import LightGraphs.neighbors
-using VectorizedRNG
 
 export intervalsmodel, hh, ws, rest, abm
 

CovidAlertVaccinationModel/test/ABM/mixing_test.jl

@@ -131,6 +131,7 @@ end
                 fit!(mixing_dist[Int(demo_v), j],d)
             end
         end
+        display(mean.(mixing_dist))
         for i in eachindex(mixing_dist)
             @test mean(mixing_dist[i]) ≈ mean(dist[i])  atol = 0.2
         end

scratch_code/alloc_test.jl

+using StaticArrays
+using StrideArrays
+using BenchmarkTools
+using UnsafeArrays
+
+function g() #would like to reduce allocations here
+    l = rand(9000:10_000) #large random integer 
+    arr = Vector{Int}(undef,l) #array of length l
+
+    #do some stuff with arr
+    accum = 0
+    arr .= 1
+    for k in arr
+        accum += k + f(arr)
+    end
+
+    return accum #discard arr
+end
+function f(arr)
+    return arr[1]*2
+end
+
+
+@allocated g()
\ No newline at end of file

scratch_code/alloc_test2.jl

+using CovidAlertVaccinationModel
+using CovidAlertVaccinationModel:ModelSolution
+using UnPack
+using StrideArrays
+using Random,StatsBase
+using StaticArrays
+using BenchmarkTools
+
+
+
+"""
+Called in the inner loop of `generate_contact_vectors!`, this function removes the degree corresponding to kth entry of index_lists, replaces it with another randomly sampled degree, and returns `csum` adjusted to the new samples. 
+
+We repeat this function until `csum == 0`. 
+"""
+@inline function reindex!(k,csum,index_list_i,index_list_j,j_to_i_contacts,i_to_j_contacts,sample_list_i,sample_list_j)
+    i_index = index_list_i[k]
+    j_index = index_list_j[k]    
+    csum +=  j_to_i_contacts[j_index] - i_to_j_contacts[i_index] +  sample_list_i[k] -  sample_list_j[k]
+    i_to_j_contacts[i_index] = sample_list_i[k] 
+    j_to_i_contacts[j_index] = sample_list_j[k]    
+    return csum
+end
+
+"""
+    generate_contact_vectors!(ij_dist<:Distribution,ji_dist<:Distribution,i_to_j_contacts::Vector{T}, j_to_i_contacts::Vector{T})
+
+Fills i_to_j_contacts and j_to_i_contacts with degrees sampled from ij_dist and ji_dist, such that i_to_j_contacts and j_to_i_contacts have equal sum.
+
+Given `μz_i = mean(ij_dist)` and `μ_j = mean(ji_dist)`, these must satisfy `μ_i* length(i_to_j_contacts) == μ_j* length(j_to_i_contacts)`  
+"""
+function generate_contact_vectors!(ij_dist,ji_dist,i_to_j_contacts::AbstractVector{T}, j_to_i_contacts::AbstractVector{T}) where T
+    
+    rand!(Random.default_rng(Threads.threadid()),ij_dist,i_to_j_contacts)
+    rand!(Random.default_rng(Threads.threadid()),ji_dist,j_to_i_contacts)
+    l_i = length(i_to_j_contacts)
+    l_j = length(j_to_i_contacts)
+
+    csum = sum(i_to_j_contacts) - sum(j_to_i_contacts)
+
+    inner_iter = 10
+    index_list_i = MArray{Tuple{10,},Int,1,10}(undef)
+    index_list_j = MArray{Tuple{10,},Int,1,10}(undef)
+    sample_list_i = MArray{Tuple{10,},Int,1,10}(undef)
+    sample_list_j = MArray{Tuple{10,},Int,1,10}(undef)
+    
+    while csum != 0
+        GC.@preserve index_list_i index_list_j begin
+            sample!(Random.default_rng(Threads.threadid()),1:l_i,index_list_i)
+            sample!(Random.default_rng(Threads.threadid()),1:l_j,index_list_j)
+        end
+        rand!(Random.default_rng(Threads.threadid()),ij_dist,sample_list_i)
+        rand!(Random.default_rng(Threads.threadid()),ji_dist,sample_list_j)
+        @inbounds for i = 1:inner_iter
+            if csum != 0
+                csum = reindex!(i,csum,index_list_i,index_list_j,j_to_i_contacts,i_to_j_contacts,sample_list_i,sample_list_j)
+            end
+        end
+    end
+    return nothing
+end
+using CovidAlertVaccinationModel:GraphEdge
+function alloc_test()
+    model = ModelSolution(1, get_parameters(), 5000) 
+    @unpack demographics, index_vectors, rest_matrix_tuple,ws_matrix_tuple = model
+    dist = ws_matrix_tuple.daily
+    contact_array = [sizehint!(Vector{GraphEdge}(),length(index_vectors[i])*2) for i in 1:length(index_vectors),j in 1:length(index_vectors)]
+    fast_chung_lu!(index_vectors,contact_array,dist)
+    return contact_array
+end
+
+function fast_chung_lu!(index_vectors,contact_array,mixing_matrix)
+    tot = 0
+
+    num_degrees_alloc_ij = Array{Int}(undef,maximum(length.(index_vectors)))
+    num_degrees_alloc_ji = similar(num_degrees_alloc_ij)
+    stubs_alloc_i = similar(num_degrees_alloc_ij)
+    stubs_alloc_j = similar(num_degrees_alloc_ij)
+
+    for i in 1:3, j in 1:i #diagonal
+        if i != j 
+            num_degrees_ij = view(num_degrees_alloc_ij,1:length(index_vectors[i]))
+            num_degrees_ji = view(num_degrees_alloc_ji,1:length(index_vectors[j]))
+            generate_contact_vectors!(mixing_matrix[i,j],mixing_matrix[j,i],num_degrees_ij,num_degrees_ji)
+            num_edges = sum(num_degrees_ij)
+            stubs_i = view(stubs_alloc_i,1:num_edges)
+            stubs_j =  view(stubs_alloc_i,1:num_edges)
+            if num_edges>0
+                sample!(Random.default_rng(Threads.threadid()),index_vectors[i],Weights(num_degrees_ij./num_edges),stubs_i)
+                sample!(Random.default_rng(Threads.threadid()),index_vectors[j],Weights(num_degrees_ji./num_edges),stubs_j)
+                tot += num_edges
+            end    
+
+            contact_array[j,i] = GraphEdge.(stubs_i,stubs_j)
+        end
+    end
+    return tot
+end
+@btime alloc_test()
\ No newline at end of file