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April 17, 2020 21:17
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/measurements/MUL_decode_latency.ipynb
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| { | |
| "cells": [ | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "-" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "# Init" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "#importing Pandas\nimport pandas as pd\n#importing plotly and cufflinks in offline mode for dataframe-based plotting\nimport cufflinks as cf\nimport plotly.offline\ncf.go_offline()\ncf.set_config_file(offline=True, world_readable=True)", | |
| "execution_count": 1, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/html": " <script type=\"text/javascript\">\n window.PlotlyConfig = {MathJaxConfig: 'local'};\n if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n if (typeof require !== 'undefined') {\n require.undef(\"plotly\");\n requirejs.config({\n paths: {\n 'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n }\n });\n require(['plotly'], function(Plotly) {\n window._Plotly = Plotly;\n });\n }\n </script>\n " | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/html": " <script type=\"text/javascript\">\n window.PlotlyConfig = {MathJaxConfig: 'local'};\n if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n if (typeof require !== 'undefined') {\n require.undef(\"plotly\");\n requirejs.config({\n paths: {\n 'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n }\n });\n require(['plotly'], function(Plotly) {\n window._Plotly = Plotly;\n });\n }\n </script>\n " | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/html": " <script type=\"text/javascript\">\n window.PlotlyConfig = {MathJaxConfig: 'local'};\n if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n if (typeof require !== 'undefined') {\n require.undef(\"plotly\");\n requirejs.config({\n paths: {\n 'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n }\n });\n require(['plotly'], function(Plotly) {\n window._Plotly = Plotly;\n });\n }\n </script>\n " | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "import logzero", | |
| "execution_count": 2, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "from logzero import logger\nlogger.setLevel(\"WARN\")", | |
| "execution_count": 3, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "import os\nimport getpass\nimport tempfile\nimport subprocess", | |
| "execution_count": 4, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "from tqdm import tqdm", | |
| "execution_count": 5, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "import numpy as np\nfrom matplotlib import pyplot as plt", | |
| "execution_count": 6, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "", | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "with open(os.path.expanduser(\"~/mydoom.txt\"), \"r\") as pwdfile:\n password = pwdfile.read()", | |
| "execution_count": 7, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "def nanoBench_sudo(command):\n # insert kernel module\n command = command.split()\n\n cmd1 = subprocess.Popen(['echo',password], stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n cmd2 = subprocess.Popen(['sudo','-S'] + command, cwd=\"../nanoBench/\", stdin=cmd1.stdout, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n #print(cmd1.stderr.read().decode())\n\n #print(cmd1.stdout.read().decode())\n\n print(cmd2.stderr.read().decode())\n\n print(cmd2.stdout.read().decode())", | |
| "execution_count": 8, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "# insert kernel module\nnanoBench_sudo(\"insmod ./kernel/nb.ko\")", | |
| "execution_count": 9, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": "[sudo] password for xren: insmod: ERROR: could not insert module ./kernel/nb.ko: File exists\n\n\n", | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "# disable HyperThreading\nnanoBench_sudo(\"./disable-HT.sh\")", | |
| "execution_count": 10, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": "\n\n", | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "def attempt_round(f, tolerance=1/10000):\n \"\"\"\n Attempt to round float to integer with tolerance.\n \n Fixes cosmetic problem of e.g. 1000 displaying as 9999.999999999\n \"\"\"\n f = float(f)\n if abs(f-round(f)) < tolerance:\n return round(f)\n else:\n print(f\"attempt_round failed for {f}\")\n return f", | |
| "execution_count": 11, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "import sys", | |
| "execution_count": 12, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "sys.version", | |
| "execution_count": 13, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 13, | |
| "data": { | |
| "text/plain": "'3.7.5 (default, Nov 20 2019, 09:21:52) \\n[GCC 9.2.1 20191008]'" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "loop_count = 3000", | |
| "execution_count": 14, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "scrolled": true, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "with tempfile.TemporaryDirectory(prefix=\"nb_measure_\") as td:\n print(td+\"/ll\")", | |
| "execution_count": 15, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": "/tmp/nb_measure_5gag230i/ll\n", | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "import pandas as pd", | |
| "execution_count": 16, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "## Measure function" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "def measure(\n asm,\n unroll_count,\n loop_count = loop_count,\n warm_up_count=100,\n n_measurements=30,\n per_block=False,\n config = None,):\n \n if os.path.isfile(config):\n with open(config) as f:\n config = f.read()\n \n with tempfile.TemporaryDirectory(prefix=\"nb_measure_\") as td:\n logger.debug(f\"Temp directory: {td}\")\n \n with open(td+\"/config.txt\", \"w\") as config_file:\n config_file.write(config)\n \n with open(td+\"/code.nasm\", \"w\") as nasm_file:\n nasm_file.write(\"BITS 64\\n\"+asm)\n \n subprocess.check_call(\n [\n \"nasm\",\n \"-f\",\n \"bin\",\n nasm_file.name,\n \"-o\",\n td+\"/code.bin\"\n ]\n )\n \n with open(td+\"/code.bin\", \"rb\") as code_binary_file:\n code_bytes = code_binary_file.read()\n logger.debug(f\"Code length: {len(code_bytes)}\")\n\n args = [\"-median\", \"-basic_mode\"]\n\n kwargs = dict(\n unroll_count = unroll_count,\n loop_count = loop_count,\n warm_up_count = warm_up_count,\n n_measurements = n_measurements,\n config = td + \"/config.txt\",\n code = td+\"/code.bin\"\n )\n\n\n l_kwargs = []\n for k, v in kwargs.items():\n l_kwargs.append(\"-\"+k)\n l_kwargs.append(str(v))\n\n\n\n command = [\n \"../nanoBench/kernel-nanoBench.sh\",\n *l_kwargs,\n *args]\n\n cmd1 = subprocess.Popen(['echo',password], stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n cmd2 = subprocess.Popen(['sudo','-S'] + command, cwd=\"../nanoBench/\", stdin=cmd1.stdout, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n\n output = cmd2.stdout.read().decode()\n\n try:\n d = dict(\n i for i in map(lambda x: x.split(\": \"), output.split(\"\\n\"))\n if len(i) == 2\n )\n \n float(d[\"RDTSC\"])\n except:\n print(\"Error:\")\n print(\"Sudo stderr:\" + cmd1.stderr.read().decode())\n\n print(\"Nanobench stderr:\" +cmd2.stderr.read().decode())\n \n \n \n d = {k: float(v) for k,v in d.items()}\n\n if not per_block:\n d = {k: attempt_round(loop_count*unroll_count*v) for k,v in d.items()}\n\n return d\n return (d[\"IDQ.DSB_UOPS\"], d[\"IDQ.MITE_UOPS\"], d[\"DSB2MITE_SWITCHES.PENALTY_CYCLES\"])", | |
| "execution_count": 17, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "## NOPs padded to length\nwith prefices" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "def nop_66(length, assembler = \"nasm\"):\n assert length >= 1\n if assembler == \"gas\" or assembler == \"as\":\n return \".byte \" + \"0x66, \" * (length - 1) + \"0x90\"\n elif assembler == \"nasm\":\n return \"db \" + \"66H, \" * (length - 1) + \"90H\"\n\ndef nop_3E(length):\n assert length >= 1\n return \".byte \" + \"0x3E, \" * (length - 1) + \"0x90\"", | |
| "execution_count": 18, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "nop_66(3)", | |
| "execution_count": 19, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 19, | |
| "data": { | |
| "text/plain": "'db 66H, 66H, 90H'" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "## MSR Setup" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "\n\ncfg1 = \"\"\"\n# Performance monitoring events for processors based on Skylake, Kaby Lake and Coffee Lake microarchitectures.\n# Applies to processors with DisplayFamily_DisplayModel of 06_4EH, 06_5EH, 06_8EH, and 06_9EH.\n# See Table 19-5 of Intel's \"System Programming Guide\" (Jan. 2019)\n\n\n79.08 IDQ.DSB_UOPS\n79.04 IDQ.MITE_UOPS\n79.18.CMSK=1 IDQ.ALL_DSB_CYCLES_ANY_UOPS\n87.01 ILD_STALL.LCP\n\"\"\"\n", | |
| "execution_count": 20, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "The assembly code to use" | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "# Only Mul instructions" | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "measure mul instructions to confirm that they decode into 3 uops." | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "d = measure(\n \"MUL RAX\",\n unroll_count=100,\n per_block=1,\n config=cfg1,\n loop_count=300, warm_up_count=50)\nd", | |
| "execution_count": 21, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 21, | |
| "data": { | |
| "text/plain": "{'RDTSC': 2.31,\n 'Instructions retired': 1.02,\n 'Core cycles': 3.01,\n 'Reference cycles': 2.31,\n 'IDQ.DSB_UOPS': 0.0,\n 'IDQ.MITE_UOPS': 2.01,\n 'IDQ.ALL_DSB_CYCLES_ANY_UOPS': 0.0,\n 'ILD_STALL.LCP': 0.0}" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "d = measure(\n \"MUL RAX\",\n unroll_count=100,\n per_block=1,\n config=cfg1,\n loop_count=300, warm_up_count=50)\nd", | |
| "execution_count": 22, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 22, | |
| "data": { | |
| "text/plain": "{'RDTSC': 2.23,\n 'Instructions retired': 1.02,\n 'Core cycles': 3.02,\n 'Reference cycles': 2.23,\n 'IDQ.DSB_UOPS': 0.0,\n 'IDQ.MITE_UOPS': 2.01,\n 'IDQ.ALL_DSB_CYCLES_ANY_UOPS': 0.0,\n 'ILD_STALL.LCP': 0.0}" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "# Measuring & Graphing" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "def jumpaddr(n, opcode_length):\n addr_length = 4\n jump_addr = n * 32 - (opcode_length + addr_length)\n jump_bytes = [0]*4 # 4-byte address\n for i in range(4):\n jump_bytes[i] = jump_addr // (0x100 ** i) % 0x100\n tobyte = lambda x: \"{0:#0{1}x}\".format(x,4)\n \n \n return \", \".join(list(map(tobyte,jump_bytes)))", | |
| "execution_count": 23, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "def jumpblock(n):\n jumpins = \"db 0xE9, \"+jumpaddr(n,opcode_length=1)\n return f\"\"\"\n {jumpins}\n {nop_66(12)}\n {nop_66(15)}\n\"\"\"", | |
| "execution_count": 24, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "def jumpblock_66(n):\n prefix = \"0x66, \"*10 # 10 byte prefix + 1 byte jump op + 4 byte jump addr = 15 byte max ins length\n jumpins = \"db \" + prefix+ \"0xE9, \"+jumpaddr(n, opcode_length=11)\n return f\"\"\"\n {jumpins}\n {nop_66(15)}\n {nop_66(2)}\n\"\"\"", | |
| "execution_count": 25, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "def jumpblock_mul32(n):\n prefix = \"0xF7, 0xE0, \"\n jumpins = \"db \" + prefix+ \"0xE9, \"+jumpaddr(n, opcode_length=3)\n return f\"\"\"\n \n {jumpins}\n {nop_66(15)}\n {nop_66(10)}\n\"\"\" # 2+1+4+15+10=32", | |
| "execution_count": 26, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "nopblock =f\"\"\"\n {nop_66(5)}\n {nop_66(12)}\n {nop_66(15)}\n\"\"\"", | |
| "execution_count": 27, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "def suffix_keys(d, suffix):\n return {\"\".join((k,suffix)):v for k,v in d.items()}", | |
| "execution_count": 28, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "\n\ncfg_dsb_uops_only = \"\"\"\n79.08 IDQ.DSB_UOPS\n\"\"\"\n\ndef diff(body, spacer, body_rpt):\n d_0 = measure(\n body*body_rpt +body*body_rpt,\n unroll_count=1,\n per_block=1,\n config=cfg1,\n loop_count=3000)\n d_0 = suffix_keys(d_0, \"_0\")\n \n d_1 = measure(\n body*body_rpt + spacer + body*body_rpt,\n unroll_count=1,\n per_block=1,\n config=cfg1,\n loop_count=3000)\n d_1 = suffix_keys(d_1, \"_1\")\n \n d = {**d_0, **d_1}\n \n d[\"BODY_RPT\"] = body_rpt\n return d", | |
| "execution_count": 29, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "def difftable(body, spacer, rpt_max, even_rpt_only=False):\n rpt_range = range(0,rpt_max, 2 if even_rpt_only else 1)\n df = (diff(body, spacer, i) for i in tqdm(rpt_range))\n df = pd.DataFrame.from_records(df)\n return df\n ", | |
| "execution_count": 30, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "def comparison_graph(df, name):\n df1= pd.DataFrame()\n df1[\"BODY_RPT\"] = df[\"BODY_RPT\"]\n df1=df1.set_index(\"BODY_RPT\",drop=True)\n df1[\"v0\"] = df[f\"{name}_0\"]\n df1[\"v1\"] = df[f\"{name}_1\"]\n df1.iplot()", | |
| "execution_count": 31, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "# Test on one-set zebras" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "\n\nasm1 = jumpblock(32)*32\ndf1=difftable(asm1, nopblock, 8, even_rpt_only=False)\ndf1\n", | |
| "execution_count": 32, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": "100%|██████████| 8/8 [00:00<00:00, 11.32it/s]\n", | |
| "name": "stderr" | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 32, | |
| "data": { | |
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| "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>RDTSC_0</th>\n <th>Instructions retired_0</th>\n <th>Core cycles_0</th>\n <th>Reference cycles_0</th>\n <th>IDQ.DSB_UOPS_0</th>\n <th>IDQ.MITE_UOPS_0</th>\n <th>IDQ.ALL_DSB_CYCLES_ANY_UOPS_0</th>\n <th>ILD_STALL.LCP_0</th>\n <th>RDTSC_1</th>\n <th>Instructions retired_1</th>\n <th>Core cycles_1</th>\n <th>Reference cycles_1</th>\n <th>IDQ.DSB_UOPS_1</th>\n <th>IDQ.MITE_UOPS_1</th>\n <th>IDQ.ALL_DSB_CYCLES_ANY_UOPS_1</th>\n <th>ILD_STALL.LCP_1</th>\n <th>BODY_RPT</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0.65</td>\n <td>2.0</td>\n <td>1.01</td>\n <td>0.66</td>\n <td>1.01</td>\n <td>1.01</td>\n <td>1.01</td>\n <td>0.0</td>\n <td>1.43</td>\n <td>5.0</td>\n <td>2.00</td>\n <td>1.41</td>\n <td>4.01</td>\n <td>1.01</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2.50</td>\n <td>4.0</td>\n <td>3.01</td>\n <td>2.52</td>\n <td>3.01</td>\n <td>3.01</td>\n <td>3.01</td>\n <td>0.0</td>\n <td>3.33</td>\n <td>7.0</td>\n <td>4.00</td>\n <td>3.35</td>\n <td>6.01</td>\n <td>3.02</td>\n <td>3.0</td>\n <td>0.0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>5.26</td>\n <td>6.0</td>\n <td>6.32</td>\n <td>5.26</td>\n <td>5.00</td>\n <td>6.00</td>\n <td>5.00</td>\n <td>0.0</td>\n <td>4.13</td>\n <td>9.0</td>\n <td>6.00</td>\n <td>4.15</td>\n <td>8.00</td>\n <td>6.33</td>\n <td>5.0</td>\n <td>0.0</td>\n <td>2</td>\n </tr>\n <tr>\n <th>3</th>\n <td>7.56</td>\n <td>8.0</td>\n <td>10.98</td>\n <td>7.55</td>\n <td>7.00</td>\n <td>10.99</td>\n <td>7.00</td>\n <td>0.0</td>\n <td>5.51</td>\n <td>11.0</td>\n <td>8.00</td>\n <td>5.53</td>\n <td>10.01</td>\n <td>7.01</td>\n <td>7.0</td>\n <td>0.0</td>\n <td>3</td>\n </tr>\n <tr>\n <th>4</th>\n <td>10.32</td>\n <td>10.0</td>\n <td>14.98</td>\n <td>10.35</td>\n <td>7.58</td>\n <td>13.57</td>\n <td>7.58</td>\n <td>0.0</td>\n <td>6.24</td>\n <td>13.0</td>\n <td>10.00</td>\n <td>6.25</td>\n <td>12.00</td>\n <td>11.00</td>\n <td>9.0</td>\n <td>0.0</td>\n <td>4</td>\n </tr>\n <tr>\n <th>5</th>\n <td>13.09</td>\n <td>12.0</td>\n <td>20.97</td>\n <td>13.09</td>\n <td>7.35</td>\n <td>17.34</td>\n <td>7.35</td>\n <td>0.0</td>\n <td>8.11</td>\n <td>15.0</td>\n <td>13.00</td>\n <td>8.11</td>\n <td>14.00</td>\n <td>14.00</td>\n <td>11.0</td>\n <td>0.0</td>\n <td>5</td>\n </tr>\n <tr>\n <th>6</th>\n <td>16.20</td>\n <td>14.0</td>\n <td>25.97</td>\n <td>16.21</td>\n <td>6.32</td>\n <td>19.29</td>\n <td>6.32</td>\n <td>0.0</td>\n <td>10.61</td>\n <td>17.0</td>\n <td>16.99</td>\n <td>10.60</td>\n <td>16.00</td>\n <td>20.99</td>\n <td>13.0</td>\n <td>0.0</td>\n <td>6</td>\n </tr>\n <tr>\n <th>7</th>\n <td>18.70</td>\n <td>16.0</td>\n <td>29.97</td>\n <td>18.70</td>\n <td>5.72</td>\n <td>20.70</td>\n <td>5.72</td>\n <td>0.0</td>\n <td>14.34</td>\n <td>19.0</td>\n <td>22.99</td>\n <td>14.36</td>\n <td>18.00</td>\n <td>23.00</td>\n <td>15.0</td>\n <td>0.0</td>\n <td>7</td>\n </tr>\n </tbody>\n</table>\n</div>" | |
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| "source": "asm2 = jumpblock_mul32(32)*32\ndf2=difftable(asm2, nopblock, 8, even_rpt_only=False)\ndf2", | |
| "execution_count": 33, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": "100%|██████████| 8/8 [00:00<00:00, 10.77it/s]\n", | |
| "name": "stderr" | |
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| { | |
| "output_type": "execute_result", | |
| "execution_count": 33, | |
| "data": { | |
| "text/plain": " RDTSC_0 Instructions retired_0 Core cycles_0 Reference cycles_0 \\\n0 0.65 2.0 1.01 0.66 \n1 6.29 6.0 8.50 6.28 \n2 12.96 10.0 16.88 12.95 \n3 15.74 14.0 25.24 15.74 \n4 21.76 18.0 33.79 21.77 \n5 26.32 22.0 42.18 26.31 \n6 31.30 26.0 50.16 31.29 \n7 36.69 30.0 58.80 36.69 \n\n IDQ.DSB_UOPS_0 IDQ.MITE_UOPS_0 IDQ.ALL_DSB_CYCLES_ANY_UOPS_0 \\\n0 1.01 1.01 1.01 \n1 9.01 8.50 3.00 \n2 17.02 16.88 5.00 \n3 25.02 25.24 7.00 \n4 26.71 31.85 7.27 \n5 26.86 38.35 7.16 \n6 27.34 44.37 7.20 \n7 27.71 51.02 7.28 \n\n ILD_STALL.LCP_0 RDTSC_1 Instructions retired_1 Core cycles_1 \\\n0 0.0 1.38 5.0 2.00 \n1 0.0 6.34 9.0 8.57 \n2 0.0 11.28 13.0 16.95 \n3 0.0 15.87 17.0 25.45 \n4 0.0 21.03 21.0 33.71 \n5 0.0 26.41 25.0 42.33 \n6 0.0 31.37 29.0 50.26 \n7 0.0 35.67 33.0 58.94 \n\n Reference cycles_1 IDQ.DSB_UOPS_1 IDQ.MITE_UOPS_1 \\\n0 1.38 4.01 1.01 \n1 6.34 12.02 8.57 \n2 11.29 20.01 16.98 \n3 15.88 28.02 25.49 \n4 21.03 36.02 33.73 \n5 26.42 44.02 42.45 \n6 31.37 52.01 50.32 \n7 35.69 60.00 58.93 \n\n IDQ.ALL_DSB_CYCLES_ANY_UOPS_1 ILD_STALL.LCP_1 BODY_RPT \n0 1.00 0.0 0 \n1 3.01 0.0 1 \n2 5.00 0.0 2 \n3 7.01 0.0 3 \n4 9.01 0.0 4 \n5 11.01 0.0 5 \n6 13.00 0.0 6 \n7 15.00 0.0 7 ", | |
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| "cell_type": "code", | |
| "source": "df1.loc[5].sort_index()", | |
| "execution_count": 34, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 34, | |
| "data": { | |
| "text/plain": "BODY_RPT 5.00\nCore cycles_0 20.97\nCore cycles_1 13.00\nIDQ.ALL_DSB_CYCLES_ANY_UOPS_0 7.35\nIDQ.ALL_DSB_CYCLES_ANY_UOPS_1 11.00\nIDQ.DSB_UOPS_0 7.35\nIDQ.DSB_UOPS_1 14.00\nIDQ.MITE_UOPS_0 17.34\nIDQ.MITE_UOPS_1 14.00\nILD_STALL.LCP_0 0.00\nILD_STALL.LCP_1 0.00\nInstructions retired_0 12.00\nInstructions retired_1 15.00\nRDTSC_0 13.09\nRDTSC_1 8.11\nReference cycles_0 13.09\nReference cycles_1 8.11\nName: 5, dtype: float64" | |
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| { | |
| "output_type": "execute_result", | |
| "execution_count": 35, | |
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| "text/plain": "BODY_RPT 5.00\nCore cycles_0 42.18\nCore cycles_1 42.33\nIDQ.ALL_DSB_CYCLES_ANY_UOPS_0 7.16\nIDQ.ALL_DSB_CYCLES_ANY_UOPS_1 11.01\nIDQ.DSB_UOPS_0 26.86\nIDQ.DSB_UOPS_1 44.02\nIDQ.MITE_UOPS_0 38.35\nIDQ.MITE_UOPS_1 42.45\nILD_STALL.LCP_0 0.00\nILD_STALL.LCP_1 0.00\nInstructions retired_0 22.00\nInstructions retired_1 25.00\nRDTSC_0 26.32\nRDTSC_1 26.41\nReference cycles_0 26.31\nReference cycles_1 26.42\nName: 5, dtype: float64" | |
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| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "# Testing on 16-set zebras (all odd / even sets)" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "asm1 = jumpblock(2)*2\ndf1=difftable(asm1, nopblock, 128, even_rpt_only=False)", | |
| "execution_count": 40, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": "100%|██████████| 128/128 [00:22<00:00, 5.58it/s]\n", | |
| "name": "stderr" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "asm2 = jumpblock_mul32(2)*2\ndf2=difftable(asm2, nopblock, 128, even_rpt_only=False)", | |
| "execution_count": 41, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": "100%|██████████| 128/128 [00:40<00:00, 3.16it/s]\n", | |
| "name": "stderr" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "## MITE_UOPS" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "comparison_graph(df1, \"IDQ.DSB_UOPS\")", | |
| "execution_count": 42, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
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| "linkText": "Export to plot.ly", | |
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| "showLink": true | |
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