collision_benchmark

README.md

@@ -3,7 +3,7 @@
 This's an algorithm for solving **2D irregular nesting problems** (also known as cutting problems or packing problems).  
 The algorithm accepts arbitrary **binary raster masks** as inputs and is good at handling the nesting problems of many gadgets. The implementation is based on quadtree & gradient optimization. Also, it can be parallelized if you start `julia` with `julia --threads k`. This package is used by [WordCloud.jl](https://github.com/guo-yong-zhi/WordCloud.jl).  
 Examples: [collision detection](./examples/collision.jl), [dynamic collision detection](./examples/dynamiccollisions.jl), [packing](./examples/packing.jl)  
-Benchmark: [benchmark](https://github.com/guo-yong-zhi/WordCloud/blob/master/examples/benchmark.jl)  
+Benchmark: [collision benchmark](./examples/collision_benchmark.jl), [fit benchmark](https://github.com/guo-yong-zhi/WordCloud/blob/master/examples/benchmark.jl)  
 ***
 ```
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examples/collision_benchmark.jl

@@ -0,0 +1,109 @@
+@show Threads.nthreads()
+#Threads.nthreads() = 4
+versioninfo()
+# Julia Version 1.7.0
+# Commit 3bf9d17731 (2021-11-30 12:12 UTC)
+# Platform Info:
+#   OS: Linux (x86_64-pc-linux-gnu)
+#   CPU: Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz
+#   WORD_SIZE: 64
+#   LIBM: libopenlibm
+#   LLVM: libLLVM-12.0.1 (ORCJIT, skylake)
+# Environment:
+#   JULIA_NUM_THREADS = 4
+
+using Stuffing
+import WordCloud
+using Plots
+unicodeplots()
+using Random
+# Random.seed!(8)
+using BenchmarkTools
+function getqtrees(objs, n)
+    sz = round(Int, 35*sqrt(n)) #The area of mask increases linearly
+    objs = Iterators.flatten((repeat(objs, n÷length(objs)), objs[1:n%length(objs)]))
+    mqt, qts... = qtrees(trues(sz, sz), objs)
+    @assert length(qts) == n
+    place!(mqt, qts)
+    return qts
+end
+B = []
+N = []
+objs = [WordCloud.rendertext(randstring(rand(1:8)), 6randexp()+8, border=1, angle=rand(-90:1:90)) for i in 1:5000];
+for n in 1:50000
+    (n%10^floor(Int, log10(n)) == 0) || continue
+    display("="^10*"$n"*"="^10)
+    qts = getqtrees(objs, n)
+    b = @benchmark totalcollisions($qts)
+    push!(B, b.times)
+    push!(N, n)
+    T = mean.(B)/1000
+    p = plot(N, T, legend=nothing)
+    xaxis!("number")
+    yaxis!("time (μs)")
+    xlims!(0, N[end])
+    ylims!(0, maximum(T))
+#     title!(string(n))
+    display(p)
+end
+#              ┌────────────────────────────────────────┐ 
+#     1.6181e5 │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⠊│ 
+#              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠔⠊⠀⠀│ 
+#              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⠔⠁⠀⠀⠀⠀│ 
+#              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⠊⠀⠀⠀⠀⠀⠀⠀│ 
+#              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠔⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀│ 
+#              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⠒⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ 
+#              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⡠⠊⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ 
+#    time (μs) │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠔⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ 
+#              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⠊⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ 
+#              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠔⠊⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ 
+#              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⠒⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ 
+#              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⠔⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ 
+#              │⠀⠀⠀⠀⠀⠀⠀⠀⢀⠔⠊⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ 
+#              │⠀⠀⠀⠀⢀⣀⠖⠊⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ 
+#            0 │⣀⡠⠔⠒⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ 
+#              └────────────────────────────────────────┘ 
+#              ⠀0⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀number⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀50000⠀ 
+display([n=>mean(b)/1000 for (n,b) in zip(N, B)])
+# 41-element Vector{Pair{Int64, Float64}}:
+#      1 => 0.030337110753768843
+#      2 => 3.8218126875
+#      3 => 5.4353064
+#      4 => 7.285635033333333
+#      5 => 7.506913866666666
+#      6 => 7.1942441
+#      7 => 7.449355683333331
+#      8 => 7.4442965
+#      9 => 8.0257573
+#     10 => 9.063585866666667
+#     20 => 18.6085887
+#     30 => 27.1033925
+#     40 => 33.74430890000001
+#     50 => 45.8884247
+#     60 => 53.154100299999996
+#     70 => 61.5332195
+#     80 => 67.950358
+#     90 => 74.40709269999999
+#    100 => 94.4142978
+#    200 => 218.3041868
+#    300 => 368.377914
+#    400 => 545.2249844212836
+#    500 => 655.0181617918314
+#    600 => 750.8218311198309
+#    700 => 937.2056202936747
+#    800 => 1061.1064743234604
+#    900 => 1204.9898514755685
+#   1000 => 1303.2047123430962
+#   2000 => 2909.305055393586
+#   3000 => 4768.721655205349
+#   4000 => 6774.760210312076
+#   5000 => 8872.888227353464
+#   6000 => 11512.653802298852
+#   7000 => 13086.52932722513
+#   8000 => 16251.792931818181
+#   9000 => 18271.59549270073
+#  10000 => 21244.32552966102
+#  20000 => 52872.430073684205
+#  30000 => 88738.72978947368
+#  40000 => 125490.6995
+#  50000 => 161809.58080645162