mirror of
https://git.rwth-aachen.de/acs/public/villas/node/
synced 2025-03-30 00:00:11 +01:00
77cc61f945
* node-infiniband-benchmark.sh now regenerates the config file every loop. Furthermore, it does not save the source log anymore but instead routes the generated signal to a file node. * evaluate_logs.ipynb is now able to evaluate the signal output. Furthermore the analysis of signal times and missed signals is enhanced in this commit. The plot already looks pretty good, but the alignment of text in matplotlib is terrible, so there is still some work to do.
618 lines
22 KiB
Text
618 lines
22 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Benchmark"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 82,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Settings\n",
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"root_dir = '/home/dennis/VILLASnode/tests/benchmarks'\n",
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"benchmark_dir = 'benchmarks_20180803_11-43-33'"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load all files\n",
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"### Results\n",
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"...\n",
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"\n",
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"### Source log\n",
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"..."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 83,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Loaded input file: /home/dennis/VILLASnode/tests/benchmarks/benchmarks_20180803_11-43-33/0_TCP-3-50000-500000_input.csv\n",
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"Loaded input file: /home/dennis/VILLASnode/tests/benchmarks/benchmarks_20180803_11-43-33/1_UDP-3-50000-500000_input.csv\n",
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"Loaded output file: /home/dennis/VILLASnode/tests/benchmarks/benchmarks_20180803_11-43-33/0_TCP-3-50000-500000_output.csv\n",
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"Loaded output file: /home/dennis/VILLASnode/tests/benchmarks/benchmarks_20180803_11-43-33/1_UDP-3-50000-500000_output.csv\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"import os\n",
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"import re\n",
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"\n",
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"# First, source log\n",
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"\n",
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"# Initialize arrays\n",
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"files_array = []\n",
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"input_paths = []\n",
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"input_array = []\n",
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"output_paths = []\n",
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"output_array = []\n",
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"\n",
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"# Save complete path of files in an array\n",
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"for subdir, dirs, files in os.walk(root_dir+'/'+benchmark_dir):\n",
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" for file in files:\n",
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" # Regex to match .csv files\n",
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" if re.match(r'.*?_input.csv', file, re.M|re.I):\n",
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" input_paths.append(os.path.join(subdir, file))\n",
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" # Regex to match .log files\n",
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" elif re.match(r'.*?_output.csv', file, re.M|re.I):\n",
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" output_paths.append(os.path.join(subdir, file))\n",
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" \n",
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" filles_array = files\n",
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"\n",
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"# INPUT\n",
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"# Loop through array with result files and save the comma separated data into a new array\n",
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"for file in input_paths:\n",
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" print(\"Loaded input file: {}\".format(file))\n",
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" \n",
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" # Load file \n",
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" input_array.append(np.genfromtxt(file, delimiter=','))\n",
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" \n",
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"# OUTPUT\n",
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"# Loop through array with result files and save the comma separated data into a new array\n",
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"for file in output_paths:\n",
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" print(\"Loaded output file: {}\".format(file))\n",
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" \n",
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" # Load file \n",
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" output_array.append(np.genfromtxt(file, delimiter=','))\n",
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"\n",
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"# Small sanity check, are arrays of the same size?\n",
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"if len(input_array) != len(output_array):\n",
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" print(\"Error: There should be as many input files as there are output files!\")\n",
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" exit();"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Save characteristics of tests\n",
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"All important settings are contained in the name of the file. We will save them in a separate array. The structure of the name is as follows:\n",
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"\n",
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"```bash\n",
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"root_dir/benchmarks_${DATE}/${ID}_${MODE}-${VALUES IN SMP}-${RATE}-${SENT SMPS}\n",
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"```\n",
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"\n",
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"Thus, we will structure it in the settings_array as follows:\n",
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"\n",
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"* `settings_array[*][0] = ID`\n",
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"* `settings_array[*][1] = MODE`\n",
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"* `settings_array[*][2] = VALUES IN SAMPLE`\n",
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"* `settings_array[*][3] = RATE`\n",
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"* `settings_array[*][4] = TOTAL NUMBER OF SAMPLES`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 84,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"['0', 'TCP', '3', '50000', '500000']\n",
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"['1', 'UDP', '3', '50000', '500000']\n"
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]
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}
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],
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"source": [
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"# Array with settings\n",
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"settings_array = []\n",
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"\n",
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"for file in output_paths:\n",
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" settings = []\n",
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" \n",
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" matchObj = re.match(r'.*?(\\d*)_(\\w*)-(\\d*)-(\\d*)-(\\d*)_output.csv', file, re.M|re.I)\n",
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"\n",
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" # Fill values to array\n",
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" if matchObj:\n",
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" for i in range(0,5):\n",
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" settings.append(matchObj.group(i+1))\n",
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" \n",
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" # Append array to big array\n",
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" settings_array.append(settings)\n",
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" \n",
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" print(settings)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Get missed steps from source node\n",
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"..."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 85,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Test 0 missed 1784 (0.36%) of 500000 samples that were intended to send at the send side.\n",
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"Test 1 missed 1930 (0.39%) of 500000 samples that were intended to send at the send side.\n"
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]
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}
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],
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"source": [
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"# Number of missing samples at receive side\n",
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"missed_send_arr = []\n",
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"# Percentage of missed samples\n",
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"perc_miss_send_arr = []\n",
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"\n",
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"# Generate real total and number of missing samples.\n",
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"# Print percentage of missed samples\n",
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"for (i, csv_vec) in enumerate(input_array):\n",
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" # Get number of missing samples\n",
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" missed_send_arr.append(int(settings_array[i][4]) - len(csv_vec))\n",
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" \n",
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" # Take percentage\n",
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" perc_miss_send_arr.append(round(missed_send_arr[i] / int(settings_array[i][4]) * 100, 2))\n",
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" \n",
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" print(\"Test {} missed {} ({}%) of {} samples that were intended to send at the send side.\"\n",
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" .format(settings_array[i][0], missed_send_arr[i], perc_miss_send_arr[i], settings_array[i][4]))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Get missed steps from destination node\n",
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"..."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 88,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Test 0 missed 2010 (0.4%) of 500000 samples that were intended to send.\n",
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"Test 1 missed 2125 (0.43%) of 500000 samples that were intended to send.\n"
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]
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}
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],
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"source": [
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"# Number of missing samples at receive side\n",
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"missed_recv_arr = []\n",
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"# Percentage of missed samples\n",
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"perc_miss_recv_arr = []\n",
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"\n",
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"# Generate real total and number of missing samples.\n",
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"# Print percentage of missed samples\n",
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"for (i, csv_vec) in enumerate(output_array):\n",
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" # Get number of missing samples\n",
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" missed_recv_arr.append(int(settings_array[i][4]) - len(csv_vec))\n",
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" \n",
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" # Take percentage\n",
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" perc_miss_recv_arr.append(round(missed_recv_arr[i] / int(settings_array[i][4]) * 100, 2))\n",
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" \n",
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" print(\"Test {} missed {} ({}%) of {} samples that were intended to send.\"\n",
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" .format(settings_array[i][0], missed_recv_arr[i], perc_miss_recv_arr[i], settings_array[i][4]))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Check first and second sample from receive & destination node\n",
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"..."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 89,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"First and second sample of test 0: 0.0 and 1728.0, respectively\n",
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"First and second sample of test 1: 0.0 and 1904.0, respectively\n",
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"\n",
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"First and second sample of test 0: 1978.0 and 1979.0, respectively\n",
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"First and second sample of test 1: 2123.0 and 2124.0, respectively\n"
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]
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}
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],
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"source": [
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"# Check first and second sample\n",
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"# INPUT\n",
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"first_second_smp_input = []\n",
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"\n",
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"for (i, csv_vec) in enumerate(input_array):\n",
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" first_second_smp_input.append([csv_vec[0][3], csv_vec[1][3]])\n",
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" print(\"First and second sample of test {}: {} and {}, respectively\".format(settings_array[i][0],\n",
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" first_second_smp_input[i][0],\n",
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" first_second_smp_input[i][1]))\n",
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"\n",
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"print(\"\")\n",
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"# OUTPUT\n",
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"first_second_smp_output = []\n",
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"\n",
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"for (i, csv_vec) in enumerate(output_array):\n",
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" first_second_smp_output.append([csv_vec[0][3], csv_vec[1][3]])\n",
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" print(\"First and second sample of test {}: {} and {}, respectively\".format(settings_array[i][0],\n",
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" first_second_smp_output[i][0],\n",
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" first_second_smp_output[i][1]))\n",
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" "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Compare input and output data\n",
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"..."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 186,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"226 samples were never transferred\n",
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"0 of these errors occured after the first sample arrived at the receive side.\n",
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"\n",
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"195 samples were never transferred\n",
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"0 of these errors occured after the first sample arrived at the receive side.\n",
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"\n"
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]
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}
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],
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"source": [
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"missing_seq = []\n",
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"never_trans_total_arr = []\n",
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"never_trans_after_arr = []\n",
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"\n",
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"# Small check if formats of arrays make sense\n",
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"if len(input_array) < len(output_array):\n",
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" print(\"Error: Something went wrong! The length of the input array should always be bigger than or equal to the length of the output array\")\n",
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" exit();\n",
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" \n",
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"# Loop through input_array, since this is always bigger or equal to output array\n",
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"for (i, csv_vec) in enumerate(input_array):\n",
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" k = 0\n",
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" missing_seq.append([])\n",
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" for (j, line) in enumerate(csv_vec):\n",
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" if line[3] != output_array[i][k][3]:\n",
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" missing_seq[i].append(line[3])\n",
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" else:\n",
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" k += 1\n",
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" \n",
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" never_trans_total_arr.append(len(missing_seq[i]))\n",
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" \n",
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" never_trans_after_arr.append(np.sum(missing_seq[i] > first_second_smp_output[i][0]))\n",
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" \n",
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" print(\"{} samples were never transferred\".format(never_trans_total_arr[i]))\n",
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" print(\"{} of these errors occured after the first sample arrived at the receive side.\".format(never_trans_after_arr[i]))\n",
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"\n",
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" print(\"\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": true
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},
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"source": [
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"## Plot data\n",
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"..."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 244,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Define Fancy Box function we use\n",
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"def plot_fancy_box(bottom, height):\n",
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" top = bottom + height\n",
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" \n",
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" p = FancyBboxPatch((left, bottom),\n",
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" width,\n",
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" height,\n",
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" boxstyle=\"round, pad=0.005\",\n",
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" \n",
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" ec=\"#dbdbdb\", \n",
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" fc=\"white\", \n",
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" alpha=0.85,\n",
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" transform=ax.transAxes\n",
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" )\n",
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" \n",
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" ax.add_patch(p)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"from matplotlib.font_manager import FontProperties\n",
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"from matplotlib.patches import FancyBboxPatch\n",
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"import os\n",
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"\n",
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"# Start creating plots\n",
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"for (i, csv_vec) in enumerate(output_array):\n",
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" # Create figure\n",
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" fig = plt.figure(num=None, figsize=(12, 8), dpi=90, facecolor='w', edgecolor='k')\n",
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"\n",
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" # Add plot and set title\n",
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" ax = fig.add_subplot(111)\n",
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" \n",
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" # Set subtitle \n",
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" title = \"Test {} — Mode: {}, Values in Sample: {}, Rate: {}, Total number of Samples: {}\".format(settings_array[i][0],\n",
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" settings_array[i][1], \n",
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" settings_array[i][2], \n",
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" settings_array[i][3], \n",
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" settings_array[i][4])\n",
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" ax.set_title(title, fontsize=12, pad=12, loc='center')\n",
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" \n",
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" # Set grid\n",
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" ax.set_axisbelow(True)\n",
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" ax.grid(True, linestyle='--')\n",
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"\n",
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" bins = 10000\n",
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" x_limit=0.00005\n",
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" \n",
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" # Data in plot\n",
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" # http://www.color-hex.com/color-palette/33602\n",
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" csv_vec_t = csv_vec.transpose()\n",
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" \n",
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" ax.hist(csv_vec_t[2] * 1e6, label='t_sent -> t_recv offset ',\n",
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" edgecolor='black',\n",
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" bins=bins,\n",
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" color='#00549f')\n",
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"\n",
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" # Set axis and calculate values above limit\n",
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" plt.xlim([0,x_limit])\n",
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" \n",
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"\n",
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" plt.xticks(np.arange(0, 50, 2), fontsize=10)\n",
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" from matplotlib.ticker import MultipleLocator\n",
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" minorLocator = MultipleLocator(1)\n",
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" ax.xaxis.set_minor_locator(minorLocator)\n",
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" \n",
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" plt.yticks(fontsize=11)\n",
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"\n",
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" # Labels\n",
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" ax.set_xlabel('time [µs]', fontsize=13, labelpad=20)\n",
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" ax.set_ylabel('frequency', fontsize=13, labelpad=20)\n",
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" ax.set_yscale('log')\n",
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"\n",
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" # Create text for offset\n",
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" off_smaller_2p5us = round((np.size(csv_vec_t[2][csv_vec_t[2] < 0.0000025]) / np.size(csv_vec_t[2])) * 100, 2)\n",
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" off_smaller_5us = round((np.size(csv_vec_t[2][csv_vec_t[2] < 0.000005]) / np.size(csv_vec_t[2])) * 100, 2)\n",
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" off_bigger_50us = round((np.size(csv_vec_t[2][csv_vec_t[2] > x_limit]) / np.size(csv_vec_t[2])) * 100, 2)\n",
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" \n",
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" offset_text = 'offset < {0}: {1:2.2f}% of samples\\n'.format('2.5µs', off_smaller_2p5us)\n",
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" offset_text += 'offset < {0}: {1:2.2f}% of samples\\n'.format('5µs', off_smaller_5us)\n",
|
|
" offset_text += 'offset > {0}: {1:2.2f}% of samples'.format('50µs', off_bigger_50us)\n",
|
|
" \n",
|
|
" \n",
|
|
" start_nr_text = '{0: <25} {1: <8} {2: <9}\\n'.format(\"1st, 2nd (input):\",\n",
|
|
" int(first_second_smp_input[i][0]),\n",
|
|
" int(first_second_smp_input[i][1]))\n",
|
|
" start_nr_text += '{0: <24} {1: <8} {2: <9}'.format(\"1st, 2nd (output):\",\n",
|
|
" int(first_second_smp_output[i][0]),\n",
|
|
" int(first_second_smp_output[i][1]))\n",
|
|
" \n",
|
|
" \n",
|
|
" \n",
|
|
" # Create text for missed steps\n",
|
|
" missed_text = '{0} {1:>5} ({2:>2.2f}%)\\n'.format(\"missing at send side:\",\n",
|
|
" missed_send_arr[i],\n",
|
|
" perc_miss_send_arr[i])\n",
|
|
" missed_text += '{0} {1:>6} ({2:>2.2f}%)'.format(\"missing at recv side:\",\n",
|
|
" missed_recv_arr[i],\n",
|
|
" perc_miss_recv_arr[i])\n",
|
|
" \n",
|
|
" # Create text for missed steps\n",
|
|
" never_transferred = '{} {}\\n'.format(\"total:\", never_trans_total_arr[i])\n",
|
|
" never_transferred += '{0} {1:<28}'.format(\"after connection established:\", never_trans_after_arr[i])\n",
|
|
" \n",
|
|
" # Create text for extrema\n",
|
|
" extrema_text_column_1 = 'min: {0:4.2f}{1:<8}\\n'.format(round(np.min(csv_vec_t[2]) * 1e6, 2), 'µs')\n",
|
|
" extrema_text_column_1 += 'max: {0:4.2f}{1:<8}'.format(round(np.max(csv_vec_t[2]) * 1e6, 2), 'µs')\n",
|
|
" \n",
|
|
" extrema_text_2 = 'average: {0:4.2f}{1:>}\\n'.format(round(np.average(csv_vec_t[2]) * 1e6, 2), 'µs')\n",
|
|
" extrema_text_2 += 'median : {0:4.2f}{1:>}'.format(round(np.median(csv_vec_t[2]) * 1e6, 2), 'µs') \n",
|
|
" \n",
|
|
" # bbox accepts FancyBboxPatch prop dict\n",
|
|
" font_header = FontProperties()\n",
|
|
" font_header.set_weight('bold')\n",
|
|
" font_header.set_size(10)\n",
|
|
" \n",
|
|
" \n",
|
|
" # Set box constraints\n",
|
|
" left, width = .705, .295\n",
|
|
" right = left + width\n",
|
|
" \n",
|
|
" plot_fancy_box(bottom = 0.28, height = 0.72)\n",
|
|
"\n",
|
|
" # Set box constraints\n",
|
|
" left, width = .715, .275\n",
|
|
" right = left + width\n",
|
|
" \n",
|
|
" #Create legend\n",
|
|
" ax.text(0.986, 0.985, \"Legend\",\n",
|
|
" verticalalignment='top', horizontalalignment='right',\n",
|
|
" transform=ax.transAxes,\n",
|
|
" color='black', fontproperties = font_header)\n",
|
|
"\n",
|
|
" \n",
|
|
" ax.legend(loc=1, bbox_to_anchor=(1, 0.965), fontsize = 10)\n",
|
|
"\n",
|
|
" \n",
|
|
" # Offset boxes\n",
|
|
" plot_fancy_box(bottom = 0.7775, height = 0.0825)\n",
|
|
" \n",
|
|
" ax.text(0.986, 0.892, \"Offset Ranges\",\n",
|
|
" verticalalignment='top', horizontalalignment='right',\n",
|
|
" transform=ax.transAxes,\n",
|
|
" color='black', fontproperties = font_header)\n",
|
|
" ax.text(left, 0.857, offset_text,\n",
|
|
" verticalalignment='top', horizontalalignment='left',\n",
|
|
" transform=ax.transAxes,\n",
|
|
" color='black', fontsize=10)\n",
|
|
" \n",
|
|
" # Start Nr\n",
|
|
" plot_fancy_box(bottom = 0.6575, height = 0.055)\n",
|
|
" \n",
|
|
" ax.text(0.986, 0.7475, \"Sequence numbers in CSV files\",\n",
|
|
" verticalalignment='top', horizontalalignment='right',\n",
|
|
" transform=ax.transAxes,\n",
|
|
" color='black', fontproperties = font_header)\n",
|
|
" ax.text(left, 0.7075, start_nr_text,\n",
|
|
" verticalalignment='top', horizontalalignment='left',\n",
|
|
" transform=ax.transAxes,\n",
|
|
" color='black', fontsize=10)\n",
|
|
" \n",
|
|
" # Missed steps\n",
|
|
" plot_fancy_box(bottom = 0.5375, height = 0.055)\n",
|
|
" \n",
|
|
" ax.text(0.986, 0.6275, \"Missed steps\",\n",
|
|
" verticalalignment='top', horizontalalignment='right',\n",
|
|
" transform=ax.transAxes,\n",
|
|
" color='black', fontproperties = font_header)\n",
|
|
" ax.text(left, 0.5875, missed_text,\n",
|
|
" verticalalignment='top', horizontalalignment='left',\n",
|
|
" transform=ax.transAxes,\n",
|
|
" color='black', fontsize=10)\n",
|
|
" \n",
|
|
" # Never transferred\n",
|
|
" plot_fancy_box(bottom = 0.4175, height = 0.055)\n",
|
|
" \n",
|
|
" ax.text(0.986, 0.5075, \"Samples not transferred\",\n",
|
|
" verticalalignment='top', horizontalalignment='right',\n",
|
|
" transform=ax.transAxes,\n",
|
|
" color='black', fontproperties = font_header)\n",
|
|
" ax.text(left, 0.4675, never_transferred,\n",
|
|
" verticalalignment='top', \n",
|
|
" horizontalalignment='left',\n",
|
|
" transform=ax.transAxes,\n",
|
|
" color='black', fontsize=10)\n",
|
|
" \n",
|
|
" # Extrema\n",
|
|
" plot_fancy_box(bottom = 0.2975, height = 0.055)\n",
|
|
" \n",
|
|
" \n",
|
|
" ax.text(0.986, 0.3875, \"Extrema\",\n",
|
|
" verticalalignment='top', horizontalalignment='right',\n",
|
|
" transform=ax.transAxes,\n",
|
|
" color='black', fontproperties = font_header)\n",
|
|
" ax.text(left, 0.3475, extrema_text_1,\n",
|
|
" verticalalignment='top', horizontalalignment='left',\n",
|
|
" transform=ax.transAxes,\n",
|
|
" color='black', fontsize=10)\n",
|
|
" ax.text(0.85, 0.3475, extrema_text_2,\n",
|
|
" verticalalignment='top', horizontalalignment='left',\n",
|
|
" transform=ax.transAxes,\n",
|
|
" color='black', fontsize=10)\n",
|
|
" \n",
|
|
"\n",
|
|
" #Show plot\n",
|
|
" plt.show()\n",
|
|
"\n",
|
|
" \n",
|
|
" #Save plot\n",
|
|
" fig.savefig('./plots/{}_{}.png'.format(settings_array[i][0], settings_array[i][1]))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.6.3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|