Integration with latest jupyter script and sample csv result

benchmarks/lockhammer/README.rst

@@ -38,13 +38,52 @@ as possible).
 Detailed information about each run is printed to stderr while a CSV summary
 is printed to stdout.
 
+Software Dependencies
+---------------------
+
++ gcc
++ python3
++ sh python3 module
++ yaml python3 module
+
 Usage
 =====
 
 The build system will generate a separate lockhammer binary for each test with
 the format lh_[testname]. Each lockhammer binary accepts the following options:
-        [-t threads]    Number of threads to exercise, default online cores
-        [-a acquires]   Number of acquisitions per thread, default 50000
-        [-c critical]   Critical section in loop iterations, default 0
-        [-p parallel]   Parallelizable section in loop iterations, default 0
-        [-s]            Run in safe mode, default no
+::
+    [-t threads]    Number of threads to exercise, default online cores
+    [-a acquires]   Number of acquisitions per thread, default 50000
+    [-c critical]   Critical section in loop iterations, default 0
+    [-p parallel]   Parallelizable section in loop iterations, default 0
+    [-s]            Run in safe mode with normal priority threads instead of RT_FIFO priority, default no
+
+
+Plotting
+========
+
+The default plotting script utilizes jupyter-notebook, matplotlib, seaborn
+and pandas under python3 environment. For Ubuntu on x86_64 machine, the
+following packages have to be installed:
+apt install build-essential python3 python3-pip jupyter-notebook
+
+For aarch64 machine, additional packages are also needed:
+apt install pkg-config libfreetype6-dev python3-scipy
+
+Then pip3 can install all plotting related libraries with the following cmd:
+pip3 install matplotlib seaborn pandas numpy
+
+Note, seaborn has to be installed without scipy as dependency on aarch64:
+pip3 install seaborn --no-dependencies
+
+The jupyter-notebook can be started with:
+jupyter-notebook --ip 0.0.0.0 --port=8888
+
+Now any browser should be able to access the jupyter notebook called:
+lockhammer-jupyter-notebook.ipynb
+
+Start a browser, with IP address set to the jupyter server IP and port 8888:
+e.g. http://example.test.com:8888
+
+Click the notebook named lockhammer-jupyter-notebook.ipynb, run each cell one
+by one and jupyter should be able to generate the png graph locally.

benchmarks/lockhammer/scripts/lh_sweeptest_cfg.yaml

@@ -52,7 +52,7 @@ globalcfg:
 ##
 sweeptest:
     enabled: True
-    safemode: False
+    safemode: True
     cmd:
         - lh_cas_event_mutex
         - lh_cas_lockref

benchmarks/lockhammer/scripts/lockhammer-jupyter-notebook.ipynb

@@ -0,0 +1,232 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# License"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) 2018, ARM Limited. All rights reserved.\n",
+    "\n",
+    "SPDX-License-Identifier: BSD-3-Clause\n",
+    "\n",
+    "Redistribution and use in source and binary forms, with or without\n",
+    "modification, are permitted provided that the following conditions are met:\n",
+    "\n",
+    "Redistributions of source code must retain the above copyright notice, this\n",
+    "list of conditions and the following disclaimer.\n",
+    "\n",
+    "Redistributions in binary form must reproduce the above copyright notice, this\n",
+    "list of conditions and the following disclaimer in the documentation and/or\n",
+    "other materials provided with the distribution.\n",
+    "\n",
+    "Neither the name of ARM Limited nor the names of its contributors may be used\n",
+    "to endorse or promote products derived from this software without specific\n",
+    "prior written permission.\n",
+    "\n",
+    "THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n",
+    "AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n",
+    "IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n",
+    "DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\n",
+    "FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n",
+    "DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n",
+    "SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n",
+    "CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR\n",
+    "TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n",
+    "OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Prerequisite Libraries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- ## python3\n",
+    "`apt install python3 python3-pip`\n",
+    "- ## jupyter-notebook\n",
+    "`apt install jupyter-notebook`\n",
+    "- ## matplotlib seaborn pandas numpy\n",
+    "`pip3 install matplotlib seaborn pandas numpy`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Matplotlib and Seaborn Settings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "sns.set()\n",
+    "\n",
+    "# default 12 colors and markers\n",
+    "default_palette = [\n",
+    "    '#765f97', #Purple\n",
+    "    '#1b9e77', #Dark Green\n",
+    "    '#8c5c20', #Brown\n",
+    "    '#0038bd', #Blue\n",
+    "    '#cf364a', #Red\n",
+    "    '#343434', #Jet Black\n",
+    "    '#878681', #Titanium Gray\n",
+    "    '#f561dd', #Magenta\n",
+    "    '#a6cee3', #Calico Blue\n",
+    "    '#dea0dd', #Plum\n",
+    "    '#7fc97f', #Grass Green\n",
+    "    '#fdc086', #Pale Yellow\n",
+    "    ]\n",
+    "\n",
+    "default_markers=['^', '*', 'd', 'x', 'D', 'o', 'v', 's', 'p', '>', '<', '.']\n",
+    "default_marker_size = 100\n",
+    "\n",
+    "# seaborn settings\n",
+    "sns.set(context=\"notebook\", style=\"darkgrid\", font_scale=2, rc={\"lines.linewidth\": 3, \"xtick.major.size\": 4, \"ytick.major.size\": 4})\n",
+    "sns.set_palette(default_palette)\n",
+    "sns.palplot(sns.color_palette())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Lockhammer Common Settings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# common variables\n",
+    "lock_workloads = [\"cas_event_mutex\", \"cas_lockref\", \"cas_rw_lock\", \"incdec_refcount\", \"osq_lock\", \"queued_spinlock\", \"ticket_spinlock\", \"jvm_objectmonitor\", \"swap_mutex\", \"tbb_spin_rw_mutex\", \"event_mutex\", \"empty\"]\n",
+    "lock_hosts = [\"x86-1\", \"x86-2\", \"x86-3\", \"x86-4\"]\n",
+    "lock_parameters = [[\"-c\", \"200ns\", \"-p\", \"1000ns\"]]\n",
+    "exectx_count_max = 88\n",
+    "exectx_count_gap = 4\n",
+    "lh_csv_result_header = [\"num_threads\", \"avg_exectx\", \"scheduled_time_per_access\", \"real_time_per_access\", \"access_rate\", \"avg_lock_depth\",\n",
+    "                        \"date\", \"fqdn\", \"exec\", \"t\", \"tv\", \"a\", \"av\", \"c\", \"cv\", \"p\", \"pv\", \"o\", \"ov\"]\n",
+    "\n",
+    "param_name = \"contended_system_latency (ns)\"\n",
+    "lock_yaxis_name = param_name\n",
+    "\n",
+    "# lockhammer-all.csv.xz is a xz-compressed aggregated file of different machines' raw csv result\n",
+    "raw_csv_filename = \"lockhammer-all.csv.xz\"\n",
+    "raw_df = pd.read_csv(raw_csv_filename, sep=', ', header=None, names=lh_csv_result_header, engine='python')\n",
+    "\n",
+    "# common functions\n",
+    "def plot_lines_only(dataf):\n",
+    "    # each test repeat 9 times, but we only plot the median latency (access_rate)\n",
+    "    median_list = []\n",
+    "    for hst, grp0 in dataf.groupby(\"host\"):\n",
+    "        for nth, grp1 in grp0.groupby(\"num_threads\"):\n",
+    "            median_list.append({\"host\": hst, \"num_threads\": nth, param_name: grp1.median()[param_name]})\n",
+    "    median_df = pd.DataFrame(median_list)\n",
+    "    \n",
+    "    from matplotlib.colors import ListedColormap\n",
+    "    cmap = ListedColormap(sns.color_palette(default_palette).as_hex())\n",
+    "    for i, (hst, grp2) in enumerate(median_df.groupby(\"host\")):\n",
+    "        plt.plot(\"num_threads\", param_name, data=grp2, color=cmap(i))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Lockhammer all workloads, raw contended system latency, 2018.11.06."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# use lmplot (not catplot) to plot raw system latencies\n",
+    "for param in lock_parameters:\n",
+    "    for workload in lock_workloads:\n",
+    "        tidy_df = pd.DataFrame()\n",
+    "        for sut in sorted(lock_hosts):\n",
+    "            host_df = raw_df.loc[(raw_df['fqdn'].str.startswith(sut) & raw_df['exec'].str.endswith(workload))]\n",
+    "            test_df = host_df.loc[(host_df['cv'] == param[1]) & (host_df['pv'] == param[3])]\n",
+    "            copy_df = test_df.copy()\n",
+    "            copy_df['host'] = sut\n",
+    "            all_df = pd.melt(copy_df, id_vars=['host', 'num_threads'], value_vars=['access_rate'], value_name=param_name)\n",
+    "            tidy_df = pd.concat([tidy_df, all_df])\n",
+    "        \n",
+    "        # because lmplot doesn't plot lines, we have to use plot_lines_only to plot them\n",
+    "        sns.lmplot(x=\"num_threads\", y=param_name, hue=\"host\", data=tidy_df, x_estimator=np.median, x_ci=50,\n",
+    "                          height=10, aspect=2, fit_reg=False, markers=default_markers[:len(lock_hosts)], scatter_kws={\"s\": default_marker_size})\n",
+    "        \n",
+    "        # plot lines which connect lmplot dots\n",
+    "        plot_lines_only(tidy_df)\n",
+    "        \n",
+    "        # change title / axis and save the figure\n",
+    "        plt.title(\"lockhammer workload: {}, critical_time: {}, parallel_time: {}\".format(workload, param[1], param[3]))\n",
+    "        plt.xlim(0, exectx_count_max)\n",
+    "        plt.xticks(np.arange(0, exectx_count_max+1, exectx_count_gap))\n",
+    "        plt.savefig(\"github_lockhammer_all_common_20181106_{}_{}_{}.png\".format(workload, param[1], param[3]))"
+   ]
+  },
+  {
+   "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
+}

benchmarks/lockhammer/scripts/test_lockhammer.py

@@ -320,7 +320,7 @@ def generate_sweeptest(className, lhCfg):
 
     execDir = default_value(globalCfg, 'execdir', os.path.join("..", "build"))
     logFile = default_value(globalCfg, 'logfile', None)
-    safeMode = default_value(sweepCfg, 'safemode', False)
+    safeMode = default_value(sweepCfg, 'safemode', True)
     repeatCnt = default_value(sweepCfg, 'repeat', 7)
     sweepArgu = default_value(sweepCfg, 'sweepargu', 't')
     arguMax = default_value(sweepCfg, 'argumax', 0)