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analysis.ipynb
{
"cells": [
{
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "\u001b[1m22×3 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m nworks \u001b[0m\u001b[1m executor \u001b[0m\u001b[1m trial \u001b[0m\n\u001b[1m \u001b[0m│\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\u001b[90m Trial… \u001b[0m\n─────┼───────────────────────────────────────────\n 1 │ 20 WorkStealingEx Trial(1.787 ms)\n 2 │ 20 ThreadedEx Trial(6.172 ms)\n 3 │ 25 WorkStealingEx Trial(1.817 ms)\n 4 │ 25 ThreadedEx Trial(5.048 ms)\n 5 │ 35 WorkStealingEx Trial(2.841 ms)\n 6 │ 35 ThreadedEx Trial(6.023 ms)\n 7 │ 5 WorkStealingEx Trial(1.471 ms)\n 8 │ 5 ThreadedEx Trial(5.516 ms)\n ⋮ │ ⋮ ⋮ ⋮\n 16 │ 45 ThreadedEx Trial(6.907 ms)\n 17 │ 10 WorkStealingEx Trial(1.475 ms)\n 18 │ 10 ThreadedEx Trial(5.616 ms)\n 19 │ 0 WorkStealingEx Trial(838.562 μs)\n 20 │ 0 ThreadedEx Trial(3.907 ms)\n 21 │ 30 WorkStealingEx Trial(1.841 ms)\n 22 │ 30 ThreadedEx Trial(6.459 ms)\n\u001b[36m 7 rows omitted\u001b[0m",
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>nworks</th><th>executor</th><th>trial</th></tr><tr><th></th><th>Int64</th><th>String</th><th>Trial…</th></tr></thead><tbody><p>22 rows × 3 columns</p><tr><th>1</th><td>20</td><td>WorkStealingEx</td><td>1.787 ms</td></tr><tr><th>2</th><td>20</td><td>ThreadedEx</td><td>6.172 ms</td></tr><tr><th>3</th><td>25</td><td>WorkStealingEx</td><td>1.817 ms</td></tr><tr><th>4</th><td>25</td><td>ThreadedEx</td><td>5.048 ms</td></tr><tr><th>5</th><td>35</td><td>WorkStealingEx</td><td>2.841 ms</td></tr><tr><th>6</th><td>35</td><td>ThreadedEx</td><td>6.023 ms</td></tr><tr><th>7</th><td>5</td><td>WorkStealingEx</td><td>1.471 ms</td></tr><tr><th>8</th><td>5</td><td>ThreadedEx</td><td>5.516 ms</td></tr><tr><th>9</th><td>15</td><td>WorkStealingEx</td><td>1.590 ms</td></tr><tr><th>10</th><td>15</td><td>ThreadedEx</td><td>5.790 ms</td></tr><tr><th>11</th><td>50</td><td>WorkStealingEx</td><td>3.826 ms</td></tr><tr><th>12</th><td>50</td><td>ThreadedEx</td><td>5.891 ms</td></tr><tr><th>13</th><td>40</td><td>WorkStealingEx</td><td>2.821 ms</td></tr><tr><th>14</th><td>40</td><td>ThreadedEx</td><td>4.900 ms</td></tr><tr><th>15</th><td>45</td><td>WorkStealingEx</td><td>2.838 ms</td></tr><tr><th>16</th><td>45</td><td>ThreadedEx</td><td>6.907 ms</td></tr><tr><th>17</th><td>10</td><td>WorkStealingEx</td><td>1.475 ms</td></tr><tr><th>18</th><td>10</td><td>ThreadedEx</td><td>5.616 ms</td></tr><tr><th>19</th><td>0</td><td>WorkStealingEx</td><td>838.562 μs</td></tr><tr><th>20</th><td>0</td><td>ThreadedEx</td><td>3.907 ms</td></tr><tr><th>21</th><td>30</td><td>WorkStealingEx</td><td>1.841 ms</td></tr><tr><th>22</th><td>30</td><td>ThreadedEx</td><td>6.459 ms</td></tr></tbody></table>"
]
},
"metadata": {},
"execution_count": 1
}
],
"cell_type": "code",
"source": [
"using BenchmarkTools\n",
"using DataFrames\n",
"using Statistics\n",
"using Transducers\n",
"using VegaLite\n",
"\n",
"resultpath = joinpath(@__DIR__, \"result.json\")\n",
"result, = BenchmarkTools.load(resultpath)\n",
"\n",
"df_raw =\n",
" BenchmarkTools.leaves(result) |>\n",
" Map() do ((nworks, ex), trial)\n",
" (nworks = parse(Int, nworks), executor = ex, trial = trial)\n",
" end |>\n",
" DataFrame"
],
"metadata": {},
"execution_count": 1
},
{
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "\u001b[1m44×5 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m nworks \u001b[0m\u001b[1m executor \u001b[0m\u001b[1m memory \u001b[0m\u001b[1m time_stat \u001b[0m\u001b[1m time_ns \u001b[0m\n\u001b[1m \u001b[0m│\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\u001b[90m Float64 \u001b[0m\n─────┼────────────────────────────────────────────────────────\n 1 │ 20 WorkStealingEx 11230240 minimum 1.78651e6\n 2 │ 20 ThreadedEx 6682896 minimum 6.17157e6\n 3 │ 25 WorkStealingEx 11231680 minimum 1.81712e6\n 4 │ 25 ThreadedEx 6685184 minimum 5.04755e6\n 5 │ 35 WorkStealingEx 11234496 minimum 2.84102e6\n 6 │ 35 ThreadedEx 6685696 minimum 6.02276e6\n 7 │ 5 WorkStealingEx 11315536 minimum 1.47101e6\n 8 │ 5 ThreadedEx 6769712 minimum 5.51611e6\n ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮\n 38 │ 45 ThreadedEx 6688224 median 8.77428e6\n 39 │ 10 WorkStealingEx 11280656 median 1.88606e6\n 40 │ 10 ThreadedEx 6730480 median 7.84968e6\n 41 │ 0 WorkStealingEx 11346672 median 1.2702e6\n 42 │ 0 ThreadedEx 6802016 median 6.8833e6\n 43 │ 30 WorkStealingEx 11233120 median 2.2381e6\n 44 │ 30 ThreadedEx 6685328 median 8.54813e6\n\u001b[36m 29 rows omitted\u001b[0m",
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>nworks</th><th>executor</th><th>memory</th><th>time_stat</th><th>time_ns</th></tr><tr><th></th><th>Int64</th><th>String</th><th>Int64</th><th>String</th><th>Float64</th></tr></thead><tbody><p>44 rows × 5 columns</p><tr><th>1</th><td>20</td><td>WorkStealingEx</td><td>11230240</td><td>minimum</td><td>1.78651e6</td></tr><tr><th>2</th><td>20</td><td>ThreadedEx</td><td>6682896</td><td>minimum</td><td>6.17157e6</td></tr><tr><th>3</th><td>25</td><td>WorkStealingEx</td><td>11231680</td><td>minimum</td><td>1.81712e6</td></tr><tr><th>4</th><td>25</td><td>ThreadedEx</td><td>6685184</td><td>minimum</td><td>5.04755e6</td></tr><tr><th>5</th><td>35</td><td>WorkStealingEx</td><td>11234496</td><td>minimum</td><td>2.84102e6</td></tr><tr><th>6</th><td>35</td><td>ThreadedEx</td><td>6685696</td><td>minimum</td><td>6.02276e6</td></tr><tr><th>7</th><td>5</td><td>WorkStealingEx</td><td>11315536</td><td>minimum</td><td>1.47101e6</td></tr><tr><th>8</th><td>5</td><td>ThreadedEx</td><td>6769712</td><td>minimum</td><td>5.51611e6</td></tr><tr><th>9</th><td>15</td><td>WorkStealingEx</td><td>11241776</td><td>minimum</td><td>1.59026e6</td></tr><tr><th>10</th><td>15</td><td>ThreadedEx</td><td>6689872</td><td>minimum</td><td>5.79008e6</td></tr><tr><th>11</th><td>50</td><td>WorkStealingEx</td><td>11238368</td><td>minimum</td><td>3.82622e6</td></tr><tr><th>12</th><td>50</td><td>ThreadedEx</td><td>6689216</td><td>minimum</td><td>5.89149e6</td></tr><tr><th>13</th><td>40</td><td>WorkStealingEx</td><td>11235776</td><td>minimum</td><td>2.82139e6</td></tr><tr><th>14</th><td>40</td><td>ThreadedEx</td><td>6687328</td><td>minimum</td><td>4.89968e6</td></tr><tr><th>15</th><td>45</td><td>WorkStealingEx</td><td>11237056</td><td>minimum</td><td>2.83812e6</td></tr><tr><th>16</th><td>45</td><td>ThreadedEx</td><td>6688224</td><td>minimum</td><td>6.90663e6</td></tr><tr><th>17</th><td>10</td><td>WorkStealingEx</td><td>11280656</td><td>minimum</td><td>1.47544e6</td></tr><tr><th>18</th><td>10</td><td>ThreadedEx</td><td>6730480</td><td>minimum</td><td>5.61629e6</td></tr><tr><th>19</th><td>0</td><td>WorkStealingEx</td><td>11346672</td><td>minimum</td><td>838562.0</td></tr><tr><th>20</th><td>0</td><td>ThreadedEx</td><td>6802016</td><td>minimum</td><td>3.90678e6</td></tr><tr><th>21</th><td>30</td><td>WorkStealingEx</td><td>11233120</td><td>minimum</td><td>1.8411e6</td></tr><tr><th>22</th><td>30</td><td>ThreadedEx</td><td>6685328</td><td>minimum</td><td>6.45944e6</td></tr><tr><th>23</th><td>20</td><td>WorkStealingEx</td><td>11230240</td><td>median</td><td>2.23865e6</td></tr><tr><th>24</th><td>20</td><td>ThreadedEx</td><td>6682896</td><td>median</td><td>8.62916e6</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td></tr></tbody></table>"
]
},
"metadata": {},
"execution_count": 2
}
],
"cell_type": "code",
"source": [
"begin\n",
" df_tmp = select(df_raw, Not(:trial))\n",
" df_tmp[!, :minimum] = map(trial -> minimum(trial).time, df_raw.trial)\n",
" df_tmp[!, :median] = map(trial -> median(trial).time, df_raw.trial)\n",
" df_tmp[!, :memory] = map(trial -> trial.memory, df_raw.trial)\n",
" df_stats = stack(\n",
" df_tmp,\n",
" [:minimum, :median],\n",
" variable_name = :time_stat,\n",
" value_name = :time_ns,\n",
" )\n",
"end"
],
"metadata": {},
"execution_count": 2
},
{
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "\u001b[1m22×3 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m nworks \u001b[0m\u001b[1m time_stat \u001b[0m\u001b[1m speedup \u001b[0m\n\u001b[1m \u001b[0m│\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\u001b[90m Float64 \u001b[0m\n─────┼────────────────────────────\n 1 │ 20 minimum 3.45454\n 2 │ 25 minimum 2.77777\n 3 │ 35 minimum 2.11993\n 4 │ 5 minimum 3.74989\n 5 │ 15 minimum 3.64095\n 6 │ 50 minimum 1.53977\n 7 │ 40 minimum 1.73662\n 8 │ 45 minimum 2.43352\n ⋮ │ ⋮ ⋮ ⋮\n 16 │ 15 median 4.09793\n 17 │ 50 median 1.97676\n 18 │ 40 median 2.47478\n 19 │ 45 median 2.71579\n 20 │ 10 median 4.16194\n 21 │ 0 median 5.41905\n 22 │ 30 median 3.81937\n\u001b[36m 7 rows omitted\u001b[0m",
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>nworks</th><th>time_stat</th><th>speedup</th></tr><tr><th></th><th>Int64</th><th>String</th><th>Float64</th></tr></thead><tbody><p>22 rows × 3 columns</p><tr><th>1</th><td>20</td><td>minimum</td><td>3.45454</td></tr><tr><th>2</th><td>25</td><td>minimum</td><td>2.77777</td></tr><tr><th>3</th><td>35</td><td>minimum</td><td>2.11993</td></tr><tr><th>4</th><td>5</td><td>minimum</td><td>3.74989</td></tr><tr><th>5</th><td>15</td><td>minimum</td><td>3.64095</td></tr><tr><th>6</th><td>50</td><td>minimum</td><td>1.53977</td></tr><tr><th>7</th><td>40</td><td>minimum</td><td>1.73662</td></tr><tr><th>8</th><td>45</td><td>minimum</td><td>2.43352</td></tr><tr><th>9</th><td>10</td><td>minimum</td><td>3.80653</td></tr><tr><th>10</th><td>0</td><td>minimum</td><td>4.6589</td></tr><tr><th>11</th><td>30</td><td>minimum</td><td>3.50846</td></tr><tr><th>12</th><td>20</td><td>median</td><td>3.85463</td></tr><tr><th>13</th><td>25</td><td>median</td><td>3.65468</td></tr><tr><th>14</th><td>35</td><td>median</td><td>2.64295</td></tr><tr><th>15</th><td>5</td><td>median</td><td>4.12985</td></tr><tr><th>16</th><td>15</td><td>median</td><td>4.09793</td></tr><tr><th>17</th><td>50</td><td>median</td><td>1.97676</td></tr><tr><th>18</th><td>40</td><td>median</td><td>2.47478</td></tr><tr><th>19</th><td>45</td><td>median</td><td>2.71579</td></tr><tr><th>20</th><td>10</td><td>median</td><td>4.16194</td></tr><tr><th>21</th><td>0</td><td>median</td><td>5.41905</td></tr><tr><th>22</th><td>30</td><td>median</td><td>3.81937</td></tr></tbody></table>"
]
},
"metadata": {},
"execution_count": 3
}
],
"cell_type": "code",
"source": [
"df = combine(groupby(df_stats, [:nworks, :time_stat])) do group\n",
" d = Dict(zip(group.executor, group.time_ns))\n",
" (speedup = d[\"ThreadedEx\"] / d[\"WorkStealingEx\"],)\n",
"end"
],
"metadata": {},
"execution_count": 3
},
{
"outputs": [],
"cell_type": "code",
"source": [
"plt1 = @vlplot(\n",
" layer = [\n",
" {\n",
" mark = {type = :line, point = true},\n",
" encoding = {\n",
" x = {field = :nworks},\n",
" y = {field = :speedup, axis = {title = \"Speedup (T_default / T_WS)\"}},\n",
" color = {field = :time_stat},\n",
" },\n",
" },\n",
" {\n",
" mark = :rule,\n",
" encoding = {y = {datum = 1}},\n",
" },\n",
" ],\n",
" data = df,\n",
" width = 400,\n",
" height = 200,\n",
")\n",
"\n",
"plt2 = @vlplot(\n",
" mark = {type = :line, point = true},\n",
" x = :nworks,\n",
" y = {field = :time_ns, axis = {title = \"Time [ns]\"}},\n",
" color = {field = :time_stat},\n",
" column = :executor,\n",
" data = df_stats,\n",
")\n",
"nothing"
],
"metadata": {},
"execution_count": 4
},
{
"cell_type": "markdown",
"source": [
"## Notes\n",
"\n",
"#### Summary\n",
"\n",
"Peformance of parallel reduce with wildly skewed run-time distribution is\n",
"benchmarked with the default `ThreadedEx` executor and `WorkStealingEx`\n",
"executor. `WorkStealingEx` consistently performces better than `ThreadedEx`\n",
"(if the run-time distribution is unbalanced enough). Furthermore, the\n",
"run-time of `WorkStealingEx` is much more conisstent than `ThreadedEx`.\n",
"\n",
"#### Benchmarked problem\n",
"\n",
"The following problem is benchmarked\n",
"\n",
"```julia\n",
"xs = 1:2^13\n",
"Folds.sum($f, xs, $Executor(basesize = 1))\n",
"```\n",
"\n",
"where `f` spins for 100 μs for `nworks` items (i.e., every `length(xs) ÷\n",
"nworks`) in the input collection `xs`.\n",
"\n",
"#### Speedup"
],
"metadata": {}
},
{
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "@vlplot(\n layer=[\n {\n mark={\n type=\"line\",\n point=true\n },\n encoding={\n x={\n field=\"nworks\"\n },\n y={\n field=\"speedup\",\n axis={\n title=\"Speedup (T_default / T_WS)\"\n }\n },\n color={\n field=\"time_stat\"\n }\n }\n },\n {\n mark=\"rule\",\n encoding={\n y={\n datum=1\n }\n }\n }\n ],\n data={\n values=...\n },\n width=400,\n height=200\n)",
"image/png": 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",
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stroke-width=\"2\" opacity=\"1\"></path></g><g class=\"mark-rule role-mark layer_1_marks\" role=\"graphics-symbol\" aria-roledescription=\"rule mark container\"><line transform=\"translate(400,163.63636363636363)\" x2=\"-400\" y2=\"0\" stroke=\"black\"></line><line transform=\"translate(400,163.63636363636363)\" x2=\"-400\" y2=\"0\" stroke=\"black\"></line><line transform=\"translate(400,163.63636363636363)\" x2=\"-400\" y2=\"0\" stroke=\"black\"></line><line transform=\"translate(400,163.63636363636363)\" x2=\"-400\" y2=\"0\" stroke=\"black\"></line><line transform=\"translate(400,163.63636363636363)\" x2=\"-400\" y2=\"0\" stroke=\"black\"></line><line transform=\"translate(400,163.63636363636363)\" x2=\"-400\" y2=\"0\" stroke=\"black\"></line><line transform=\"translate(400,163.63636363636363)\" x2=\"-400\" y2=\"0\" stroke=\"black\"></line><line transform=\"translate(400,163.63636363636363)\" x2=\"-400\" y2=\"0\" stroke=\"black\"></line><line transform=\"translate(400,163.63636363636363)\" x2=\"-400\" y2=\"0\" stroke=\"black\"></line><line transform=\"translate(400,163.63636363636363)\" x2=\"-400\" y2=\"0\" stroke=\"black\"></line><line transform=\"translate(400,163.63636363636363)\" x2=\"-400\" y2=\"0\" stroke=\"black\"></line><line transform=\"translate(400,163.63636363636363)\" x2=\"-400\" y2=\"0\" stroke=\"black\"></line><line transform=\"translate(400,163.63636363636363)\" x2=\"-400\" y2=\"0\" stroke=\"black\"></line><line transform=\"translate(400,163.63636363636363)\" x2=\"-400\" y2=\"0\" stroke=\"black\"></line><line transform=\"translate(400,163.63636363636363)\" x2=\"-400\" y2=\"0\" stroke=\"black\"></line><line transform=\"translate(400,163.63636363636363)\" x2=\"-400\" y2=\"0\" stroke=\"black\"></line><line transform=\"translate(400,163.63636363636363)\" x2=\"-400\" y2=\"0\" stroke=\"black\"></line><line transform=\"translate(400,163.63636363636363)\" x2=\"-400\" y2=\"0\" stroke=\"black\"></line><line transform=\"translate(400,163.63636363636363)\" x2=\"-400\" y2=\"0\" stroke=\"black\"></line><line transform=\"translate(400,163.63636363636363)\" x2=\"-400\" y2=\"0\" stroke=\"black\"></line><line transform=\"translate(400,163.63636363636363)\" x2=\"-400\" y2=\"0\" stroke=\"black\"></line><line transform=\"translate(400,163.63636363636363)\" x2=\"-400\" y2=\"0\" stroke=\"black\"></line></g><g class=\"mark-group role-legend\" role=\"graphics-symbol\" aria-roledescription=\"legend\" aria-label=\"Symbol legend titled 'time_stat' for fill color and stroke color with 2 values: median, minimum\"><g transform=\"translate(420,0)\"><path class=\"background\" aria-hidden=\"true\" d=\"M0,0h64v45h-64Z\" pointer-events=\"none\"></path><g><g class=\"mark-group role-legend-entry\"><g transform=\"translate(0,16)\"><path class=\"background\" aria-hidden=\"true\" d=\"M0,0h0v0h0Z\" pointer-events=\"none\"></path><g><g class=\"mark-group role-scope\" role=\"graphics-object\" aria-roledescription=\"group mark container\"><g transform=\"translate(0,0)\"><path class=\"background\" aria-hidden=\"true\" d=\"M0,0h64v12.5h-64Z\" pointer-events=\"none\" opacity=\"1\"></path><g><g class=\"mark-symbol role-legend-symbol\" pointer-events=\"none\"><path transform=\"translate(6,6)\" d=\"M5,0A5,5,0,1,1,-5,0A5,5,0,1,1,5,0\" fill=\"#4c78a8\" stroke=\"#4c78a8\" stroke-width=\"1.5\" opacity=\"1\"></path></g><g class=\"mark-text role-legend-label\" pointer-events=\"none\"><text text-anchor=\"start\" transform=\"translate(16,9)\" font-family=\"sans-serif\" font-size=\"10px\" fill=\"#000\" opacity=\"1\">median</text></g></g><path class=\"foreground\" aria-hidden=\"true\" d=\"\" pointer-events=\"none\" display=\"none\"></path></g><g transform=\"translate(0,16)\"><path class=\"background\" aria-hidden=\"true\" d=\"M0,0h64v12.5h-64Z\" pointer-events=\"none\" opacity=\"1\"></path><g><g class=\"mark-symbol role-legend-symbol\" pointer-events=\"none\"><path transform=\"translate(6,6)\" d=\"M5,0A5,5,0,1,1,-5,0A5,5,0,1,1,5,0\" fill=\"#f58518\" stroke=\"#f58518\" stroke-width=\"1.5\" opacity=\"1\"></path></g><g class=\"mark-text role-legend-label\" pointer-events=\"none\"><text text-anchor=\"start\" transform=\"translate(16,9)\" font-family=\"sans-serif\" font-size=\"10px\" fill=\"#000\" opacity=\"1\">minimum</text></g></g><path class=\"foreground\" aria-hidden=\"true\" d=\"\" pointer-events=\"none\" display=\"none\"></path></g></g></g><path class=\"foreground\" aria-hidden=\"true\" d=\"\" pointer-events=\"none\" display=\"none\"></path></g></g><g class=\"mark-text role-legend-title\" pointer-events=\"none\"><text text-anchor=\"start\" transform=\"translate(0,9)\" font-family=\"sans-serif\" font-size=\"11px\" font-weight=\"bold\" fill=\"#000\" opacity=\"1\">time_stat</text></g></g><path class=\"foreground\" aria-hidden=\"true\" d=\"\" pointer-events=\"none\" display=\"none\"></path></g></g></g><path class=\"foreground\" aria-hidden=\"true\" d=\"\" display=\"none\"></path></g></g></g></svg>\n"
]
},
"metadata": {},
"execution_count": 5
}
],
"cell_type": "code",
"source": [
"plt1"
],
"metadata": {},
"execution_count": 5
},
{
"cell_type": "markdown",
"source": [
"For this range of parameter, `WorkStealingEx` performs better than\n",
"`ThreadedEx`. Note that the run-time distribution is _very_ skewed. The\n",
"largest tried `nworks` is 50; i.e., only `50 /2^13 * 100 = 0.6` % of the\n",
"items are actually compute-intensive."
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"#### Run-time"
],
"metadata": {}
},
{
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "@vlplot(\n mark={\n type=\"line\",\n point=true\n },\n data={\n values=...\n },\n encoding={\n x={\n field=\"nworks\"\n },\n y={\n field=\"time_ns\",\n axis={\n title=\"Time [ns]\"\n }\n },\n color={\n field=\"time_stat\"\n },\n column={\n field=\"executor\"\n }\n }\n)",
"image/png": 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d=\"M2.7386127875258306,0A2.7386127875258306,2.7386127875258306,0,1,1,-2.7386127875258306,0A2.7386127875258306,2.7386127875258306,0,1,1,2.7386127875258306,0\" fill=\"#4c78a8\" stroke-width=\"2\" opacity=\"1\"></path><path aria-label=\"nworks: 10; Time [ns]: 1886063; time_stat: median\" role=\"graphics-symbol\" aria-roledescription=\"point\" transform=\"translate(40,158.08748888888888)\" d=\"M2.7386127875258306,0A2.7386127875258306,2.7386127875258306,0,1,1,-2.7386127875258306,0A2.7386127875258306,2.7386127875258306,0,1,1,2.7386127875258306,0\" fill=\"#4c78a8\" stroke-width=\"2\" opacity=\"1\"></path><path aria-label=\"nworks: 0; Time [ns]: 1270203.5; time_stat: median\" role=\"graphics-symbol\" aria-roledescription=\"point\" transform=\"translate(0,171.77325555555555)\" d=\"M2.7386127875258306,0A2.7386127875258306,2.7386127875258306,0,1,1,-2.7386127875258306,0A2.7386127875258306,2.7386127875258306,0,1,1,2.7386127875258306,0\" fill=\"#4c78a8\" stroke-width=\"2\" opacity=\"1\"></path><path aria-label=\"nworks: 30; Time [ns]: 2238100; time_stat: median\" role=\"graphics-symbol\" aria-roledescription=\"point\" transform=\"translate(120,150.26444444444445)\" d=\"M2.7386127875258306,0A2.7386127875258306,2.7386127875258306,0,1,1,-2.7386127875258306,0A2.7386127875258306,2.7386127875258306,0,1,1,2.7386127875258306,0\" fill=\"#4c78a8\" stroke-width=\"2\" opacity=\"1\"></path></g></g><path class=\"foreground\" aria-hidden=\"true\" d=\"\" display=\"none\"></path></g></g><g class=\"mark-group role-legend\" role=\"graphics-symbol\" aria-roledescription=\"legend\" aria-label=\"Symbol legend titled 'time_stat' for fill color and stroke color with 2 values: median, minimum\"><g transform=\"translate(450,0)\"><path class=\"background\" aria-hidden=\"true\" d=\"M0,0h64v45h-64Z\" pointer-events=\"none\"></path><g><g class=\"mark-group role-legend-entry\"><g transform=\"translate(0,16)\"><path class=\"background\" aria-hidden=\"true\" d=\"M0,0h0v0h0Z\" pointer-events=\"none\"></path><g><g class=\"mark-group role-scope\" role=\"graphics-object\" aria-roledescription=\"group mark container\"><g transform=\"translate(0,0)\"><path class=\"background\" aria-hidden=\"true\" d=\"M0,0h64v12.5h-64Z\" pointer-events=\"none\" opacity=\"1\"></path><g><g class=\"mark-symbol role-legend-symbol\" pointer-events=\"none\"><path transform=\"translate(6,6)\" d=\"M5,0A5,5,0,1,1,-5,0A5,5,0,1,1,5,0\" fill=\"#4c78a8\" stroke=\"#4c78a8\" stroke-width=\"1.5\" opacity=\"1\"></path></g><g class=\"mark-text role-legend-label\" pointer-events=\"none\"><text text-anchor=\"start\" transform=\"translate(16,9)\" font-family=\"sans-serif\" font-size=\"10px\" fill=\"#000\" opacity=\"1\">median</text></g></g><path class=\"foreground\" aria-hidden=\"true\" d=\"\" pointer-events=\"none\" display=\"none\"></path></g><g transform=\"translate(0,16)\"><path class=\"background\" aria-hidden=\"true\" d=\"M0,0h64v12.5h-64Z\" pointer-events=\"none\" opacity=\"1\"></path><g><g class=\"mark-symbol role-legend-symbol\" pointer-events=\"none\"><path transform=\"translate(6,6)\" d=\"M5,0A5,5,0,1,1,-5,0A5,5,0,1,1,5,0\" fill=\"#f58518\" stroke=\"#f58518\" stroke-width=\"1.5\" opacity=\"1\"></path></g><g class=\"mark-text role-legend-label\" pointer-events=\"none\"><text text-anchor=\"start\" transform=\"translate(16,9)\" font-family=\"sans-serif\" font-size=\"10px\" fill=\"#000\" opacity=\"1\">minimum</text></g></g><path class=\"foreground\" aria-hidden=\"true\" d=\"\" pointer-events=\"none\" display=\"none\"></path></g></g></g><path class=\"foreground\" aria-hidden=\"true\" d=\"\" pointer-events=\"none\" display=\"none\"></path></g></g><g class=\"mark-text role-legend-title\" pointer-events=\"none\"><text text-anchor=\"start\" transform=\"translate(0,9)\" font-family=\"sans-serif\" font-size=\"11px\" font-weight=\"bold\" fill=\"#000\" opacity=\"1\">time_stat</text></g></g><path class=\"foreground\" aria-hidden=\"true\" d=\"\" pointer-events=\"none\" display=\"none\"></path></g></g></g><path class=\"foreground\" aria-hidden=\"true\" d=\"\" display=\"none\"></path></g></g></g></svg>\n"
]
},
"metadata": {},
"execution_count": 6
}
],
"cell_type": "code",
"source": [
"plt2"
],
"metadata": {},
"execution_count": 6
},
{
"cell_type": "markdown",
"source": [
"Note the big difference in the minimum and median run-time of `ThreadedEx`\n",
"(left). This is probably due to the randomization by Julia's paralel task\n",
"runtime. On the other hand, the performance of `WorkStealingEx` (right) is\n",
"more consistent (smaller difference between minimum and median)."
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"---\n",
"\n",
"*This notebook was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*"
],
"metadata": {}
}
],
"nbformat_minor": 3,
"metadata": {
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.5.2"
},
"kernelspec": {
"name": "julia-1.5",
"display_name": "Julia 1.5.2",
"language": "julia"
}
},
"nbformat": 4
}
Raw
analysis.jl
using BenchmarkTools
using DataFrames
using Statistics
using Transducers
using VegaLite
resultpath = joinpath(@__DIR__, "result.json")
result, = BenchmarkTools.load(resultpath)
df_raw =
BenchmarkTools.leaves(result) |>
Map() do ((nworks, ex), trial)
(nworks = parse(Int, nworks), executor = ex, trial = trial)
end |>
DataFrame
#-
begin
df_tmp = select(df_raw, Not(:trial))
df_tmp[!, :minimum] = map(trial -> minimum(trial).time, df_raw.trial)
df_tmp[!, :median] = map(trial -> median(trial).time, df_raw.trial)
df_tmp[!, :memory] = map(trial -> trial.memory, df_raw.trial)
df_stats = stack(
df_tmp,
[:minimum, :median],
variable_name = :time_stat,
value_name = :time_ns,
)
end
#-
df = combine(groupby(df_stats, [:nworks, :time_stat])) do group
d = Dict(zip(group.executor, group.time_ns))
(speedup = d["ThreadedEx"] / d["WorkStealingEx"],)
end
#-
plt1 = @vlplot(
layer = [
{
mark = {type = :line, point = true},
encoding = {
x = {field = :nworks},
y = {field = :speedup, axis = {title = "Speedup (T_default / T_WS)"}},
color = {field = :time_stat},
},
},
{
mark = :rule,
encoding = {y = {datum = 1}},
},
],
data = df,
width = 400,
height = 200,
)
plt2 = @vlplot(
mark = {type = :line, point = true},
x = :nworks,
y = {field = :time_ns, axis = {title = "Time [ns]"}},
color = {field = :time_stat},
column = :executor,
data = df_stats,
)
nothing
# ## Notes
#
# #### Summary
#
# Peformance of parallel reduce with wildly skewed run-time distribution is
# benchmarked with the default `ThreadedEx` executor and `WorkStealingEx`
# executor. `WorkStealingEx` consistently performces better than `ThreadedEx`
# (if the run-time distribution is unbalanced enough). Furthermore, the
# run-time of `WorkStealingEx` is much more conisstent than `ThreadedEx`.
#
# #### Benchmarked problem
#
# The following problem is benchmarked
#
# ```julia
# xs = 1:2^13
# Folds.sum($f, xs, $Executor(basesize = 1))
# ```
#
# where `f` spins for 100 μs for `nworks` items (i.e., every `length(xs) ÷
# nworks`) in the input collection `xs`.
#
# #### Speedup
plt1
# For this range of parameter, `WorkStealingEx` performs better than
# `ThreadedEx`. Note that the run-time distribution is _very_ skewed. The
# largest tried `nworks` is 50; i.e., only `50 /2^13 * 100 = 0.6` % of the
# items are actually compute-intensive.
# #### Run-time
plt2
# Note the big difference in the minimum and median run-time of `ThreadedEx`
# (left). This is probably due to the randomization by Julia's paralel task
# runtime. On the other hand, the performance of `WorkStealingEx` (right) is
# more consistent (smaller difference between minimum and median).
Raw
benchmarks.jl
using BenchmarkTools
using Folds
using FoldsThreads
const SUITE = BenchmarkGroup()
const DATA_LENGTH = 2^13
function unbalanced_work(nworks, data_length)
if nworks == 0
space = typemax(Int)
else
space = cld(data_length, nworks)
end
function work(i)
n = 0
if mod(i, space) == 0
# Sping for 100 μs
t = time_ns() + 100_0000
while t > time_ns()
n += 1
end
end
return n
end
end
for nworks in 0:5:51
f = unbalanced_work(nworks, DATA_LENGTH)
xs = 1:DATA_LENGTH
s1 = SUITE[nworks] = BenchmarkGroup()
s1["ThreadedEx"] =
@benchmarkable(Folds.sum($f, $xs, ThreadedEx(basesize = 1, stoppable = false)))
s1["WorkStealingEx"] = @benchmarkable(Folds.sum($f, $xs, WorkStealingEx(basesize = 1)))
end
Raw
Makefile
JULIA = julia --startup-file=no
export JULIA_NUM_THREADS = 32
export JULIA_LOAD_PATH = @
export JULIA_PROJECT = $(PWD)
.PHONY: rebuild
analysis.ipynb: analysis.jl result.json
$(MAKE) rebuild
rebuild:
$(JULIA) -e 'using Literate; Literate.notebook("analysis.jl")'
result.json:
$(JULIA) runbenchmarks.jl $@
Raw
Manifest.toml
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deps = ["Base64", "DataStructures", "DataValues", "Dates", "FileIO", "FilePaths", "IteratorInterfaceExtensions", "JSON", "MacroTools", "NodeJS", "Pkg", "REPL", "Random", "Setfield", "TableTraits", "TableTraitsUtils", "URIParser", "Vega"]
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git-tree-sha1 = "320228915c8debb12cb434c59057290f0834dbf6"
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Raw
Project.toml
[deps]
BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
Folds = "41a02a25-b8f0-4f67-bc48-60067656b558"
FoldsThreads = "9c68100b-dfe1-47cf-94c8-95104e173443"
Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
Transducers = "28d57a85-8fef-5791-bfe6-a80928e7c999"
VegaLite = "112f6efa-9a02-5b7d-90c0-432ed331239a"
Raw
result.json
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Raw
runbenchmarks.jl
@info "Using $(Threads.nthreads()) threads"
resultpath, = ARGS
mkpath(dirname(resultpath))
using BenchmarkTools
include("benchmarks.jl")
_, time, = @timed result = run(SUITE; verbose = true)
BenchmarkTools.save(resultpath, result)
write(joinpath(dirname(resultpath), "time"), string(time))
Raw
time
140.603282209
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