rubydragon/metagraphoptimization.jl
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Make diagram generation faster, add tests for it, update some notebooks

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Anton Reinhard
2023-12-05 17:32:05 +01:00
parent f78cde613a
commit 86799644c4
12 changed files with 730 additions and 201 deletions
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notebooks/diagram_gen.ipynb
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@ -2,38 +2,30 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"using Revise; using QEDbase; using QEDprocesses; using MetagraphOptimization; using BenchmarkTools; using DataStructures"
]
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"using Revise; using QEDbase; using QEDprocesses; using MetagraphOptimization; using BenchmarkTools; using DataStructures\n",
"import MetagraphOptimization.gen_diagrams"
]
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Diagram 1: Initial Particles: [k_in_1, e_in_1, k_out_1, e_out_1]\n",
" Virtuality Level 1 Vertices: [k_out_1 + e_out_1 -> e_out_2, k_in_1 + e_in_1 -> e_in_2]\n",
" Tie: e_out_2 -- e_in_2\n",
"Diagram 1: Initial Particles: [k_i_1, e_i_1, k_o_1, e_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_1 + e_i_1 -> e_i_2, k_o_1 + e_o_1 -> e_o_2]\n",
" Tie: e_i_2 -- e_o_2\n",
"\n",
"Diagram 2: Initial Particles: [k_in_1, e_in_1, k_out_1, e_out_1]\n",
" Virtuality Level 1 Vertices: [k_in_1 + e_out_1 -> e_out_2, e_in_1 + k_out_1 -> e_in_2]\n",
" Tie: e_out_2 -- e_in_2\n",
"Diagram 2: Initial Particles: [k_i_1, e_i_1, k_o_1, e_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_1 + e_o_1 -> e_o_2, e_i_1 + k_o_1 -> e_i_2]\n",
" Tie: e_o_2 -- e_i_2\n",
"\n"
]
}
@ -53,22 +45,22 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"BenchmarkTools.Trial: 3077 samples with 1 evaluation.\n",
" Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m): \u001b[39m\u001b[36m\u001b[1m1.461 ms\u001b[22m\u001b[39m … \u001b[35m 3.180 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m0.00% … 47.86%\n",
" Time \u001b[90m(\u001b[39m\u001b[34m\u001b[1mmedian\u001b[22m\u001b[39m\u001b[90m): \u001b[39m\u001b[34m\u001b[1m1.557 ms \u001b[22m\u001b[39m\u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmedian\u001b[90m): \u001b[39m0.00%\n",
" Time \u001b[90m(\u001b[39m\u001b[32m\u001b[1mmean\u001b[22m\u001b[39m ± \u001b[32mσ\u001b[39m\u001b[90m): \u001b[39m\u001b[32m\u001b[1m1.624 ms\u001b[22m\u001b[39m ± \u001b[32m275.482 μs\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmean ± σ\u001b[90m): \u001b[39m3.59% ± 9.19%\n",
"BenchmarkTools.Trial: 6044 samples with 1 evaluation.\n",
" Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m): \u001b[39m\u001b[36m\u001b[1m490.857 μs\u001b[22m\u001b[39m … \u001b[35m 3.657 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m0.00% … 77.38%\n",
" Time \u001b[90m(\u001b[39m\u001b[34m\u001b[1mmedian\u001b[22m\u001b[39m\u001b[90m): \u001b[39m\u001b[34m\u001b[1m800.314 μs \u001b[22m\u001b[39m\u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmedian\u001b[90m): \u001b[39m0.00%\n",
" Time \u001b[90m(\u001b[39m\u001b[32m\u001b[1mmean\u001b[22m\u001b[39m ± \u001b[32mσ\u001b[39m\u001b[90m): \u001b[39m\u001b[32m\u001b[1m825.263 μs\u001b[22m\u001b[39m ± \u001b[32m208.306 μs\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmean ± σ\u001b[90m): \u001b[39m1.62% ± 5.53%\n",
"\n",
" \u001b[39m▃\u001b[39m▅\u001b[39m▇\u001b[39m█\u001b[34m█\u001b[39m\u001b[39m▇\u001b[39m▄\u001b[32m▃\u001b[39m\u001b[39m▁\u001b[39m▁\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▁\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▁\n",
" \u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[34m█\u001b[39m\u001b[39m█\u001b[39m█\u001b[32m█\u001b[39m\u001b[39m█\u001b[39m█\u001b[39m▇\u001b[39m▅\u001b[39m▃\u001b[39m▄\u001b[39m▅\u001b[39m▃\u001b[39m▄\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▃\u001b[39m▁\u001b[39m▁\u001b[39m▃\u001b[39m▅\u001b[39m▆\u001b[39m▅\u001b[39m▅\u001b[39m▃\u001b[39m▃\u001b[39m▃\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▃\u001b[39m▄\u001b[39m▃\u001b[39m▁\u001b[39m▃\u001b[39m▁\u001b[39m▁\u001b[39m▄\u001b[39m▅\u001b[39m▆\u001b[39m▇\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m▇\u001b[39m▇\u001b[39m \u001b[39m█\n",
" 1.46 ms\u001b[90m \u001b[39m\u001b[90mHistogram: \u001b[39m\u001b[90m\u001b[1mlog(\u001b[22m\u001b[39m\u001b[90mfrequency\u001b[39m\u001b[90m\u001b[1m)\u001b[22m\u001b[39m\u001b[90m by time\u001b[39m 2.85 ms \u001b[0m\u001b[1m<\u001b[22m\n",
" \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▃\u001b[39m█\u001b[39m▂\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▁\u001b[39m \u001b[39m▂\u001b[39m▃\u001b[39m▃\u001b[39m▂\u001b[39m▃\u001b[39m▃\u001b[39m▄\u001b[39m▅\u001b[34m▅\u001b[39m\u001b[39m▅\u001b[39m▃\u001b[32m▂\u001b[39m\u001b[39m▁\u001b[39m \u001b[39m▁\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▃\u001b[39m▆\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \n",
" \u001b[39m▂\u001b[39m▂\u001b[39m▁\u001b[39m▂\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▂\u001b[39m▂\u001b[39m▁\u001b[39m▁\u001b[39m▃\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m▇\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[34m█\u001b[39m\u001b[39m█\u001b[39m█\u001b[32m█\u001b[39m\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m▇\u001b[39m▆\u001b[39m▆\u001b[39m▅\u001b[39m▅\u001b[39m▄\u001b[39m▄\u001b[39m▄\u001b[39m▅\u001b[39m▇\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m▅\u001b[39m▄\u001b[39m▃\u001b[39m \u001b[39m▅\n",
" 491 μs\u001b[90m Histogram: frequency by time\u001b[39m 1.04 ms \u001b[0m\u001b[1m<\u001b[22m\n",
"\n",
" Memory estimate\u001b[90m: \u001b[39m\u001b[33m2.16 MiB\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m18208\u001b[39m."
" Memory estimate\u001b[90m: \u001b[39m\u001b[33m280.03 KiB\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m2709\u001b[39m."
]
},
"metadata": {},
@ -79,10 +71,10 @@
"output_type": "stream",
"text": [
"Found 6 Diagrams for 2-Photon Compton\n",
"Diagram 1: Initial Particles: [k_in_1, k_in_2, e_in_1, k_out_1, e_out_1]\n",
" Virtuality Level 1 Vertices: [k_in_1 + e_in_1 -> e_in_2, k_out_1 + e_out_1 -> e_out_2]\n",
" Virtuality Level 2 Vertices: [k_in_2 + e_in_2 -> e_in_3]\n",
" Tie: e_out_2 -- e_in_3\n",
"Diagram 1: Initial Particles: [k_i_1, k_i_2, e_i_1, k_o_1, e_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_1 + e_i_1 -> e_i_2, k_i_2 + e_o_1 -> e_o_2]\n",
" Virtuality Level 2 Vertices: [k_o_1 + e_i_2 -> e_i_3]\n",
" Tie: e_o_2 -- e_i_3\n",
"\n"
]
}
@ -100,22 +92,22 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"BenchmarkTools.Trial: 500 samples with 1 evaluation.\n",
" Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m): \u001b[39m\u001b[36m\u001b[1m 9.130 ms\u001b[22m\u001b[39m … \u001b[35m 16.858 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m0.00% … 11.40%\n",
" Time \u001b[90m(\u001b[39m\u001b[34m\u001b[1mmedian\u001b[22m\u001b[39m\u001b[90m): \u001b[39m\u001b[34m\u001b[1m 9.611 ms \u001b[22m\u001b[39m\u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmedian\u001b[90m): \u001b[39m0.00%\n",
" Time \u001b[90m(\u001b[39m\u001b[32m\u001b[1mmean\u001b[22m\u001b[39m ± \u001b[32mσ\u001b[39m\u001b[90m): \u001b[39m\u001b[32m\u001b[1m10.018 ms\u001b[22m\u001b[39m ± \u001b[32m802.928 μs\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmean ± σ\u001b[90m): \u001b[39m4.38% ± 5.79%\n",
"BenchmarkTools.Trial: 1167 samples with 1 evaluation.\n",
" Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m): \u001b[39m\u001b[36m\u001b[1m2.581 ms\u001b[22m\u001b[39m … \u001b[35m 7.394 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m0.00% … 38.39%\n",
" Time \u001b[90m(\u001b[39m\u001b[34m\u001b[1mmedian\u001b[22m\u001b[39m\u001b[90m): \u001b[39m\u001b[34m\u001b[1m4.278 ms \u001b[22m\u001b[39m\u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmedian\u001b[90m): \u001b[39m0.00%\n",
" Time \u001b[90m(\u001b[39m\u001b[32m\u001b[1mmean\u001b[22m\u001b[39m ± \u001b[32mσ\u001b[39m\u001b[90m): \u001b[39m\u001b[32m\u001b[1m4.284 ms\u001b[22m\u001b[39m ± \u001b[32m550.104 μs\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmean ± σ\u001b[90m): \u001b[39m1.84% ± 6.28%\n",
"\n",
" \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▃\u001b[39m▂\u001b[39m▅\u001b[39m█\u001b[39m▃\u001b[34m▂\u001b[39m\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[32m \u001b[39m\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \n",
" \u001b[39m▄\u001b[39m▃\u001b[39m▅\u001b[39m█\u001b[39m▇\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[34m█\u001b[39m\u001b[39m█\u001b[39m▆\u001b[39m▅\u001b[39m▄\u001b[39m▃\u001b[39m▃\u001b[39m▃\u001b[39m▃\u001b[32m▁\u001b[39m\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▂\u001b[39m▃\u001b[39m▂\u001b[39m▁\u001b[39m▃\u001b[39m▃\u001b[39m▄\u001b[39m▃\u001b[39m▅\u001b[39m█\u001b[39m▅\u001b[39m▅\u001b[39m▆\u001b[39m▅\u001b[39m▆\u001b[39m▅\u001b[39m▄\u001b[39m▄\u001b[39m▃\u001b[39m▃\u001b[39m▃\u001b[39m▃\u001b[39m▃\u001b[39m▁\u001b[39m▂\u001b[39m▃\u001b[39m▃\u001b[39m▁\u001b[39m▃\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▂\u001b[39m \u001b[39m▃\n",
" 9.13 ms\u001b[90m Histogram: frequency by time\u001b[39m 12 ms \u001b[0m\u001b[1m<\u001b[22m\n",
" \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▃\u001b[39m▃\u001b[39m▅\u001b[39m▅\u001b[34m▃\u001b[39m\u001b[39m▃\u001b[39m▇\u001b[39m█\u001b[39m▄\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \n",
" \u001b[39m▂\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▂\u001b[39m▂\u001b[39m▁\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m▄\u001b[39m█\u001b[39m▄\u001b[39m▄\u001b[39m▄\u001b[39m▃\u001b[39m▃\u001b[39m▄\u001b[39m▆\u001b[39m▇\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[34m█\u001b[39m\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m▆\u001b[39m▄\u001b[39m▃\u001b[39m▃\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▂\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▂\u001b[39m▃\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m \u001b[39m▃\n",
" 2.58 ms\u001b[90m Histogram: frequency by time\u001b[39m 6.46 ms \u001b[0m\u001b[1m<\u001b[22m\n",
"\n",
" Memory estimate\u001b[90m: \u001b[39m\u001b[33m14.19 MiB\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m117375\u001b[39m."
" Memory estimate\u001b[90m: \u001b[39m\u001b[33m1.71 MiB\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m15410\u001b[39m."
]
},
"metadata": {},
@ -126,10 +118,10 @@
"output_type": "stream",
"text": [
"Found 24 Diagrams for 3-Photon Compton\n",
"Diagram 1: Initial Particles: [k_in_1, k_in_2, k_in_3, e_in_1, k_out_1, e_out_1]\n",
" Virtuality Level 1 Vertices: [k_in_1 + e_in_1 -> e_in_2, k_in_2 + e_out_1 -> e_out_2]\n",
" Virtuality Level 2 Vertices: [k_in_3 + e_in_2 -> e_in_3, k_out_1 + e_out_2 -> e_out_3]\n",
" Tie: e_in_3 -- e_out_3\n",
"Diagram 1: Initial Particles: [k_i_1, k_i_2, k_i_3, e_i_1, k_o_1, e_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_2 + e_o_1 -> e_o_2, k_i_3 + e_i_1 -> e_i_2]\n",
" Virtuality Level 2 Vertices: [k_i_1 + e_o_2 -> e_o_3, k_o_1 + e_i_2 -> e_i_3]\n",
" Tie: e_o_3 -- e_i_3\n",
"\n"
]
}
@ -147,22 +139,22 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"BenchmarkTools.Trial: 27 samples with 1 evaluation.\n",
" Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m): \u001b[39m\u001b[36m\u001b[1m182.038 ms\u001b[22m\u001b[39m … \u001b[35m203.672 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m4.83% … 11.23%\n",
" Time \u001b[90m(\u001b[39m\u001b[34m\u001b[1mmedian\u001b[22m\u001b[39m\u001b[90m): \u001b[39m\u001b[34m\u001b[1m187.399 ms \u001b[22m\u001b[39m\u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmedian\u001b[90m): \u001b[39m7.11%\n",
" Time \u001b[90m(\u001b[39m\u001b[32m\u001b[1mmean\u001b[22m\u001b[39m ± \u001b[32mσ\u001b[39m\u001b[90m): \u001b[39m\u001b[32m\u001b[1m189.151 ms\u001b[22m\u001b[39m ± \u001b[32m 5.412 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmean ± σ\u001b[90m): \u001b[39m8.49% ± 2.73%\n",
"BenchmarkTools.Trial: 141 samples with 1 evaluation.\n",
" Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m): \u001b[39m\u001b[36m\u001b[1m31.255 ms\u001b[22m\u001b[39m … \u001b[35m42.658 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m0.00% … 4.92%\n",
" Time \u001b[90m(\u001b[39m\u001b[34m\u001b[1mmedian\u001b[22m\u001b[39m\u001b[90m): \u001b[39m\u001b[34m\u001b[1m35.749 ms \u001b[22m\u001b[39m\u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmedian\u001b[90m): \u001b[39m4.34%\n",
" Time \u001b[90m(\u001b[39m\u001b[32m\u001b[1mmean\u001b[22m\u001b[39m ± \u001b[32mσ\u001b[39m\u001b[90m): \u001b[39m\u001b[32m\u001b[1m35.690 ms\u001b[22m\u001b[39m ± \u001b[32m 2.009 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmean ± σ\u001b[90m): \u001b[39m3.04% ± 2.83%\n",
"\n",
" \u001b[39m▃\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▃\u001b[39m \u001b[39m \u001b[34m█\u001b[39m\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[32m \u001b[39m\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \n",
" \u001b[39m█\u001b[39m▇\u001b[39m▁\u001b[39m▇\u001b[39m▁\u001b[39m▇\u001b[39m▁\u001b[39m▇\u001b[39m▇\u001b[39m▁\u001b[39m█\u001b[39m▁\u001b[39m▇\u001b[34m█\u001b[39m\u001b[39m▁\u001b[39m▇\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[32m▇\u001b[39m\u001b[39m▇\u001b[39m▁\u001b[39m▇\u001b[39m▇\u001b[39m▁\u001b[39m▇\u001b[39m▇\u001b[39m▇\u001b[39m▁\u001b[39m▇\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▇\u001b[39m▇\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▇\u001b[39m▇\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▇\u001b[39m \u001b[39m▁\n",
" 182 ms\u001b[90m Histogram: frequency by time\u001b[39m 204 ms \u001b[0m\u001b[1m<\u001b[22m\n",
" \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▆\u001b[39m▁\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▃\u001b[39m▁\u001b[39m▁\u001b[39m \u001b[39m \u001b[39m▁\u001b[39m \u001b[39m▃\u001b[39m▁\u001b[39m▃\u001b[39m▁\u001b[39m \u001b[39m█\u001b[34m▆\u001b[39m\u001b[39m▁\u001b[39m▁\u001b[39m▆\u001b[39m▁\u001b[39m▁\u001b[39m▃\u001b[39m \u001b[39m▁\u001b[39m \u001b[39m▃\u001b[39m▆\u001b[39m▁\u001b[39m▆\u001b[39m█\u001b[39m \u001b[39m \u001b[39m \u001b[39m▁\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▁\u001b[39m \u001b[39m \u001b[39m \u001b[39m \n",
" \u001b[39m▇\u001b[39m▄\u001b[39m▄\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▄\u001b[39m▇\u001b[39m▇\u001b[39m▄\u001b[39m▄\u001b[39m▄\u001b[39m▇\u001b[39m▄\u001b[39m█\u001b[39m█\u001b[39m▇\u001b[39m▄\u001b[39m▇\u001b[39m▇\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m▇\u001b[39m▁\u001b[39m█\u001b[39m▄\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m▇\u001b[39m█\u001b[34m█\u001b[39m\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m▄\u001b[39m█\u001b[39m▇\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m▇\u001b[39m▇\u001b[39m▁\u001b[39m█\u001b[39m▄\u001b[39m▁\u001b[39m▄\u001b[39m▇\u001b[39m█\u001b[39m▇\u001b[39m▄\u001b[39m \u001b[39m▄\n",
" 31.3 ms\u001b[90m Histogram: frequency by time\u001b[39m 39.2 ms \u001b[0m\u001b[1m<\u001b[22m\n",
"\n",
" Memory estimate\u001b[90m: \u001b[39m\u001b[33m417.57 MiB\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m3203645\u001b[39m."
" Memory estimate\u001b[90m: \u001b[39m\u001b[33m23.29 MiB\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m171048\u001b[39m."
]
},
"metadata": {},
@ -173,11 +165,11 @@
"output_type": "stream",
"text": [
"Found 120 Diagrams for 4-Photon Compton\n",
"Diagram 1: Initial Particles: [k_in_1, k_in_2, k_in_3, k_in_4, e_in_1, k_out_1, e_out_1]\n",
" Virtuality Level 1 Vertices: [k_in_3 + e_in_1 -> e_in_2, k_in_4 + e_out_1 -> e_out_2]\n",
" Virtuality Level 2 Vertices: [k_in_2 + e_out_2 -> e_out_3, k_out_1 + e_in_2 -> e_in_3]\n",
" Virtuality Level 3 Vertices: [k_in_1 + e_in_3 -> e_in_4]\n",
" Tie: e_out_3 -- e_in_4\n",
"Diagram 1: Initial Particles: [k_i_1, k_i_2, k_i_3, k_i_4, e_i_1, k_o_1, e_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_1 + e_o_1 -> e_o_2, e_i_1 + k_o_1 -> e_i_2]\n",
" Virtuality Level 2 Vertices: [k_i_3 + e_o_2 -> e_o_3, k_i_2 + e_i_2 -> e_i_3]\n",
" Virtuality Level 3 Vertices: [k_i_4 + e_o_3 -> e_o_4]\n",
" Tie: e_i_3 -- e_o_4\n",
"\n"
]
}
@ -195,22 +187,22 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"BenchmarkTools.Trial: 2 samples with 1 evaluation.\n",
" Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m): \u001b[39m\u001b[36m\u001b[1m3.210 s\u001b[22m\u001b[39m … \u001b[35m 3.254 s\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m10.57% … 11.76%\n",
" Time \u001b[90m(\u001b[39m\u001b[34m\u001b[1mmedian\u001b[22m\u001b[39m\u001b[90m): \u001b[39m\u001b[34m\u001b[1m3.232 s \u001b[22m\u001b[39m\u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmedian\u001b[90m): \u001b[39m11.17%\n",
" Time \u001b[90m(\u001b[39m\u001b[32m\u001b[1mmean\u001b[22m\u001b[39m ± \u001b[32mσ\u001b[39m\u001b[90m): \u001b[39m\u001b[32m\u001b[1m3.232 s\u001b[22m\u001b[39m ± \u001b[32m30.898 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmean ± σ\u001b[90m): \u001b[39m11.17% ± 0.84%\n",
"BenchmarkTools.Trial: 10 samples with 1 evaluation.\n",
" Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m): \u001b[39m\u001b[36m\u001b[1m471.789 ms\u001b[22m\u001b[39m … \u001b[35m527.196 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m6.00% … 7.35%\n",
" Time \u001b[90m(\u001b[39m\u001b[34m\u001b[1mmedian\u001b[22m\u001b[39m\u001b[90m): \u001b[39m\u001b[34m\u001b[1m499.068 ms \u001b[22m\u001b[39m\u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmedian\u001b[90m): \u001b[39m6.98%\n",
" Time \u001b[90m(\u001b[39m\u001b[32m\u001b[1mmean\u001b[22m\u001b[39m ± \u001b[32mσ\u001b[39m\u001b[90m): \u001b[39m\u001b[32m\u001b[1m502.132 ms\u001b[22m\u001b[39m ± \u001b[32m 17.383 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmean ± σ\u001b[90m): \u001b[39m6.79% ± 0.77%\n",
"\n",
" \u001b[34m█\u001b[39m\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[32m \u001b[39m\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m█\u001b[39m \u001b[39m \n",
" \u001b[34m█\u001b[39m\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[32m▁\u001b[39m\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m█\u001b[39m \u001b[39m▁\n",
" 3.21 s\u001b[90m Histogram: frequency by time\u001b[39m 3.25 s \u001b[0m\u001b[1m<\u001b[22m\n",
" \u001b[39m▁\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m█\u001b[39m▁\u001b[39m \u001b[34m▁\u001b[39m\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[32m \u001b[39m\u001b[39m \u001b[39m▁\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▁\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▁\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▁\u001b[39m▁\u001b[39m \u001b[39m \n",
" \u001b[39m█\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m█\u001b[39m█\u001b[39m▁\u001b[34m█\u001b[39m\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[32m▁\u001b[39m\u001b[39m▁\u001b[39m█\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m█\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m█\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m█\u001b[39m█\u001b[39m \u001b[39m▁\n",
" 472 ms\u001b[90m Histogram: frequency by time\u001b[39m 527 ms \u001b[0m\u001b[1m<\u001b[22m\n",
"\n",
" Memory estimate\u001b[90m: \u001b[39m\u001b[33m8.12 GiB\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m67276764\u001b[39m."
" Memory estimate\u001b[90m: \u001b[39m\u001b[33m627.12 MiB\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m3747679\u001b[39m."
]
},
"metadata": {},
@ -221,11 +213,11 @@
"output_type": "stream",
"text": [
"Found 720 Diagrams for 5-Photon Compton\n",
"Diagram 1: Initial Particles: [k_in_1, k_in_2, k_in_3, k_in_4, k_in_5, e_in_1, k_out_1, e_out_1]\n",
" Virtuality Level 1 Vertices: [k_in_3 + e_in_1 -> e_in_2, k_in_4 + e_out_1 -> e_out_2]\n",
" Virtuality Level 2 Vertices: [k_in_2 + e_out_2 -> e_out_3, k_in_5 + e_in_2 -> e_in_3]\n",
" Virtuality Level 3 Vertices: [k_in_1 + e_out_3 -> e_out_4, k_out_1 + e_in_3 -> e_in_4]\n",
" Tie: e_out_4 -- e_in_4\n",
"Diagram 1: Initial Particles: [k_i_1, k_i_2, k_i_3, k_i_4, k_i_5, e_i_1, k_o_1, e_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_1 + e_i_1 -> e_i_2, k_i_4 + e_o_1 -> e_o_2]\n",
" Virtuality Level 2 Vertices: [k_i_3 + e_i_2 -> e_i_3, k_i_5 + e_o_2 -> e_o_3]\n",
" Virtuality Level 3 Vertices: [k_i_2 + e_i_3 -> e_i_4, k_o_1 + e_o_3 -> e_o_4]\n",
" Tie: e_i_4 -- e_o_4\n",
"\n"
]
}
@ -243,20 +235,20 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Diagram 1: Initial Particles: [p_in_1, e_in_1, p_out_1, e_out_1]\n",
" Virtuality Level 1 Vertices: [p_out_1 + e_out_1 -> k_out_2, p_in_1 + e_in_1 -> k_out_1]\n",
" Tie: k_out_2 -- k_out_1\n",
"Diagram 1: Initial Particles: [p_i_1, e_i_1, e_o_1, p_o_1]\n",
" Virtuality Level 1 Vertices: [p_i_1 + e_i_1 -> k_o_2, e_o_1 + p_o_1 -> k_o_1]\n",
" Tie: k_o_2 -- k_o_1\n",
"\n",
"Diagram 2: Initial Particles: [p_in_1, e_in_1, p_out_1, e_out_1]\n",
" Virtuality Level 1 Vertices: [p_in_1 + p_out_1 -> k_out_2, e_in_1 + e_out_1 -> k_out_1]\n",
" Tie: k_out_2 -- k_out_1\n",
"Diagram 2: Initial Particles: [p_i_1, e_i_1, e_o_1, p_o_1]\n",
" Virtuality Level 1 Vertices: [p_i_1 + p_o_1 -> k_o_1, e_i_1 + e_o_1 -> k_o_2]\n",
" Tie: k_o_1 -- k_o_2\n",
"\n"
]
}
@ -276,20 +268,20 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Diagram 1: Initial Particles: [e_in_1, e_in_2, e_out_1, e_out_2]\n",
" Virtuality Level 1 Vertices: [e_in_1 + e_out_1 -> k_out_1, e_in_2 + e_out_2 -> k_out_2]\n",
" Tie: k_out_1 -- k_out_2\n",
"Diagram 1: Initial Particles: [e_i_1, e_i_2, e_o_1, e_o_2]\n",
" Virtuality Level 1 Vertices: [e_i_2 + e_o_2 -> k_o_2, e_i_1 + e_o_1 -> k_o_1]\n",
" Tie: k_o_2 -- k_o_1\n",
"\n",
"Diagram 2: Initial Particles: [e_in_1, e_in_2, e_out_1, e_out_2]\n",
" Virtuality Level 1 Vertices: [e_in_1 + e_out_2 -> k_out_1, e_in_2 + e_out_1 -> k_out_2]\n",
" Tie: k_out_1 -- k_out_2\n",
"Diagram 2: Initial Particles: [e_i_1, e_i_2, e_o_1, e_o_2]\n",
" Virtuality Level 1 Vertices: [e_i_1 + e_o_2 -> k_o_1, e_i_2 + e_o_1 -> k_o_2]\n",
" Tie: k_o_1 -- k_o_2\n",
"\n"
]
}
@ -309,20 +301,20 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Diagram 1: Initial Particles: [p_in_1, e_in_1, k_out_1, k_out_2]\n",
" Virtuality Level 1 Vertices: [e_in_1 + k_out_1 -> e_in_2, p_in_1 + k_out_2 -> e_out_1]\n",
" Tie: e_in_2 -- e_out_1\n",
"Diagram 1: Initial Particles: [p_i_1, e_i_1, k_o_1, k_o_2]\n",
" Virtuality Level 1 Vertices: [e_i_1 + k_o_2 -> e_i_2, p_i_1 + k_o_1 -> e_o_1]\n",
" Tie: e_i_2 -- e_o_1\n",
"\n",
"Diagram 2: Initial Particles: [p_in_1, e_in_1, k_out_1, k_out_2]\n",
" Virtuality Level 1 Vertices: [e_in_1 + k_out_2 -> e_in_2, p_in_1 + k_out_1 -> e_out_1]\n",
" Tie: e_in_2 -- e_out_1\n",
"Diagram 2: Initial Particles: [p_i_1, e_i_1, k_o_1, k_o_2]\n",
" Virtuality Level 1 Vertices: [e_i_1 + k_o_1 -> e_i_2, p_i_1 + k_o_2 -> e_o_1]\n",
" Tie: e_i_2 -- e_o_1\n",
"\n"
]
}
@ -342,20 +334,20 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Diagram 1: Initial Particles: [k_in_1, k_in_2, p_out_1, e_out_1]\n",
" Virtuality Level 1 Vertices: [k_in_1 + e_out_1 -> e_out_2, k_in_2 + p_out_1 -> e_in_1]\n",
" Tie: e_out_2 -- e_in_1\n",
"Diagram 1: Initial Particles: [k_i_1, k_i_2, e_o_1, p_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_1 + p_o_1 -> e_i_1, k_i_2 + e_o_1 -> e_o_2]\n",
" Tie: e_i_1 -- e_o_2\n",
"\n",
"Diagram 2: Initial Particles: [k_in_1, k_in_2, p_out_1, e_out_1]\n",
" Virtuality Level 1 Vertices: [k_in_1 + p_out_1 -> e_in_1, k_in_2 + e_out_1 -> e_out_2]\n",
" Tie: e_in_1 -- e_out_2\n",
"Diagram 2: Initial Particles: [k_i_1, k_i_2, e_o_1, p_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_1 + e_o_1 -> e_o_2, k_i_2 + p_o_1 -> e_i_1]\n",
" Tie: e_o_2 -- e_i_1\n",
"\n"
]
}
@ -375,7 +367,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 47,
"metadata": {},
"outputs": [
{
@ -383,45 +375,45 @@
"output_type": "stream",
"text": [
"Found 8 diagrams:\n",
"Diagram 1: Initial Particles: [k_in_1, e_in_1, p_out_1, e_out_1, e_out_2]\n",
" Virtuality Level 1 Vertices: [e_in_1 + e_out_1 -> k_out_1, k_in_1 + p_out_1 -> e_in_2]\n",
" Virtuality Level 2 Vertices: [e_out_2 + k_out_1 -> e_out_3]\n",
" Tie: e_in_2 -- e_out_3\n",
"Diagram 1: Initial Particles: [k_i_1, e_i_1, e_o_1, e_o_2, p_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_1 + e_o_1 -> e_o_3, e_i_1 + e_o_2 -> k_o_1]\n",
" Virtuality Level 2 Vertices: [p_o_1 + k_o_1 -> e_i_2]\n",
" Tie: e_o_3 -- e_i_2\n",
"\n",
"Diagram 2: Initial Particles: [k_in_1, e_in_1, p_out_1, e_out_1, e_out_2]\n",
" Virtuality Level 1 Vertices: [k_in_1 + e_out_1 -> e_out_3, p_out_1 + e_out_2 -> k_out_1]\n",
" Virtuality Level 2 Vertices: [e_in_1 + e_out_3 -> k_out_2]\n",
" Tie: k_out_1 -- k_out_2\n",
"Diagram 2: Initial Particles: [k_i_1, e_i_1, e_o_1, e_o_2, p_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_1 + p_o_1 -> e_i_2, e_i_1 + e_o_2 -> k_o_1]\n",
" Virtuality Level 2 Vertices: [e_o_1 + e_i_2 -> k_o_2]\n",
" Tie: k_o_1 -- k_o_2\n",
"\n",
"Diagram 3: Initial Particles: [k_in_1, e_in_1, p_out_1, e_out_1, e_out_2]\n",
" Virtuality Level 1 Vertices: [p_out_1 + e_out_1 -> k_out_1, k_in_1 + e_out_2 -> e_out_3]\n",
" Virtuality Level 2 Vertices: [e_in_1 + k_out_1 -> e_in_2]\n",
" Tie: e_out_3 -- e_in_2\n",
"Diagram 3: Initial Particles: [k_i_1, e_i_1, e_o_1, e_o_2, p_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_1 + e_o_2 -> e_o_3, e_i_1 + e_o_1 -> k_o_1]\n",
" Virtuality Level 2 Vertices: [p_o_1 + e_o_3 -> k_o_2]\n",
" Tie: k_o_1 -- k_o_2\n",
"\n",
"Diagram 4: Initial Particles: [k_in_1, e_in_1, p_out_1, e_out_1, e_out_2]\n",
" Virtuality Level 1 Vertices: [p_out_1 + e_out_2 -> k_out_1, k_in_1 + e_in_1 -> e_in_2]\n",
" Virtuality Level 2 Vertices: [e_out_1 + k_out_1 -> e_out_3]\n",
" Tie: e_in_2 -- e_out_3\n",
"Diagram 4: Initial Particles: [k_i_1, e_i_1, e_o_1, e_o_2, p_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_1 + e_i_1 -> e_i_2, e_o_2 + p_o_1 -> k_o_1]\n",
" Virtuality Level 2 Vertices: [e_o_1 + e_i_2 -> k_o_2]\n",
" Tie: k_o_1 -- k_o_2\n",
"\n",
"Diagram 5: Initial Particles: [k_in_1, e_in_1, p_out_1, e_out_1, e_out_2]\n",
" Virtuality Level 1 Vertices: [e_in_1 + e_out_1 -> k_out_1, k_in_1 + e_out_2 -> e_out_3]\n",
" Virtuality Level 2 Vertices: [p_out_1 + k_out_1 -> e_in_2]\n",
" Tie: e_out_3 -- e_in_2\n",
"Diagram 5: Initial Particles: [k_i_1, e_i_1, e_o_1, e_o_2, p_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_1 + e_o_1 -> e_o_3, e_o_2 + p_o_1 -> k_o_1]\n",
" Virtuality Level 2 Vertices: [e_i_1 + k_o_1 -> e_i_2]\n",
" Tie: e_o_3 -- e_i_2\n",
"\n",
"Diagram 6: Initial Particles: [k_in_1, e_in_1, p_out_1, e_out_1, e_out_2]\n",
" Virtuality Level 1 Vertices: [k_in_1 + p_out_1 -> e_in_2, e_in_1 + e_out_2 -> k_out_1]\n",
" Virtuality Level 2 Vertices: [e_out_1 + k_out_1 -> e_out_3]\n",
" Tie: e_in_2 -- e_out_3\n",
"Diagram 6: Initial Particles: [k_i_1, e_i_1, e_o_1, e_o_2, p_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_1 + e_o_2 -> e_o_3, e_o_1 + p_o_1 -> k_o_1]\n",
" Virtuality Level 2 Vertices: [e_i_1 + e_o_3 -> k_o_2]\n",
" Tie: k_o_1 -- k_o_2\n",
"\n",
"Diagram 7: Initial Particles: [k_in_1, e_in_1, p_out_1, e_out_1, e_out_2]\n",
" Virtuality Level 1 Vertices: [p_out_1 + e_out_1 -> k_out_1, k_in_1 + e_in_1 -> e_in_2]\n",
" Virtuality Level 2 Vertices: [e_out_2 + k_out_1 -> e_out_3]\n",
" Tie: e_in_2 -- e_out_3\n",
"Diagram 7: Initial Particles: [k_i_1, e_i_1, e_o_1, e_o_2, p_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_1 + p_o_1 -> e_i_2, e_i_1 + e_o_1 -> k_o_1]\n",
" Virtuality Level 2 Vertices: [e_o_2 + k_o_1 -> e_o_3]\n",
" Tie: e_i_2 -- e_o_3\n",
"\n",
"Diagram 8: Initial Particles: [k_in_1, e_in_1, p_out_1, e_out_1, e_out_2]\n",
" Virtuality Level 1 Vertices: [k_in_1 + e_out_1 -> e_out_3, e_in_1 + e_out_2 -> k_out_1]\n",
" Virtuality Level 2 Vertices: [p_out_1 + k_out_1 -> e_in_2]\n",
" Tie: e_out_3 -- e_in_2\n",
"Diagram 8: Initial Particles: [k_i_1, e_i_1, e_o_1, e_o_2, p_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_1 + e_i_1 -> e_i_2, e_o_1 + p_o_1 -> k_o_1]\n",
" Virtuality Level 2 Vertices: [e_o_2 + k_o_1 -> e_o_3]\n",
" Tie: e_i_2 -- e_o_3\n",
"\n"
]
}

111
notebooks/diagram_gen_profiling.ipynb Normal file
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@ -0,0 +1,111 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "595a07c5-0ecc-4f3e-8cbe-63fc64b456da",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mPrecompiling MetagraphOptimization [3e869610-d48d-4942-ba70-c1b702a33ca4]\n"
]
},
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using BenchmarkTools; using Profile; using PProf; using Revise; using MetagraphOptimization;\n",
"Threads.nthreads()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "163f84be-1e2e-480e-9944-1fa4e0eedf3b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 1 NUMA nodes\n",
"CUDA is non-functional\n"
]
},
{
"data": {
"text/plain": [
"QED Process: 'ke->kkkkke'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"machine = get_machine_info()\n",
"model = QEDModel()\n",
"process = parse_process(\"ke->kkkkke\", model)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "6c2eef40-5df0-4396-8e62-5204c4de61f3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"profile.pb.gz\""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Main binary filename not available.\n",
"Serving web UI on http://localhost:57599\n"
]
}
],
"source": [
"gen_graph(parse_process(\"ke->kke\", model))\n",
"Profile.clear()\n",
"@profile gen_graph(process)\n",
"pprof()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 1.9.4",
"language": "julia",
"name": "julia-1.9"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.9.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

129
notebooks/large_compton.ipynb Normal file
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@ -0,0 +1,129 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"12"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"using MetagraphOptimization\n",
"using BenchmarkTools\n",
"\n",
"Threads.nthreads()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Graph:\n",
" Nodes: Total: 131069, DataTask: 65539, ComputeTaskQED_Sum: 1, \n",
" ComputeTaskQED_V: 35280, ComputeTaskQED_S2: 5040, ComputeTaskQED_U: 9, \n",
" ComputeTaskQED_S1: 25200\n",
" Edges: 176419\n",
" Total Compute Effort: 549370.0\n",
" Total Data Transfer: 1.0645344e7\n",
" Total Compute Intensity: 0.05160659909158408\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"machine = get_machine_info()\n",
"model = QEDModel()\n",
"process = parse_process(\"ke->kkkkkke\", model)\n",
"\n",
"inputs = [gen_process_input(process) for _ in 1:1e3];\n",
"graph = gen_graph(process)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Graph:\n",
" Nodes: Total: 14783, DataTask: 7396, ComputeTaskQED_Sum: 1, \n",
" ComputeTaskQED_V: 1819, ComputeTaskQED_S2: 5040, ComputeTaskQED_U: 9, \n",
" ComputeTaskQED_S1: 518\n",
" Edges: 26672\n",
" Total Compute Effort: 77102.0\n",
" Total Data Transfer: 5.063616e6\n",
" Total Compute Intensity: 0.015226668056977465\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"optimizer = ReductionOptimizer()\n",
"\n",
"optimize_to_fixpoint!(optimizer, graph)\n",
"graph"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Calculated 15537.0 results/s, 1295.0 results/s per thread for QED Process: 'ke->kkkkkke' (12 threads)\n"
]
}
],
"source": [
"compute_compton_reduced = get_compute_function(graph, process, machine)\n",
"outputs = [zero(ComplexF64) for _ in 1:1e6]\n",
"\n",
"bench_result = @benchmark begin\n",
" Threads.@threads :static for i in eachindex(inputs)\n",
" outputs[i] = compute_compton_reduced(inputs[i])\n",
" end\n",
"end\n",
"\n",
"rate = length(inputs) / (mean(bench_result.times) / 1.0e9)\n",
"rate_per_thread = rate / Threads.nthreads()\n",
"println(\"Calculated $(round(rate)) results/s, $(round(rate_per_thread)) results/s per thread for $(process) ($(Threads.nthreads()) threads)\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 1.9.4",
"language": "julia",
"name": "julia-1.9"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.9.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}