diff options
-rw-r--r-- | benchmark/COMPARISON | 8 | ||||
-rw-r--r-- | benchmark/README.md | 51 | ||||
-rw-r--r-- | benchmark/benchmark-results.jsonl | 415 | ||||
-rw-r--r-- | benchmark/benchmark.ipynb | 1445 | ||||
-rw-r--r-- | benchmark/benchmark.py | 281 |
5 files changed, 2196 insertions, 4 deletions
diff --git a/benchmark/COMPARISON b/benchmark/COMPARISON index 55ab94b4..fd41282e 100644 --- a/benchmark/COMPARISON +++ b/benchmark/COMPARISON @@ -1,4 +1,4 @@ -CPU | Mz | exec/s | afl-*-config | -========================================|======|========|==============| -CPU 12th Gen Intel(R) Core(TM) i7-1270P | 4200 | 12750 | both | -AMD EPYC 7282 16-Core Processor | 3190 | 10060 | both | +CPU | MHz | threads | singlecore | multicore | afl-*-config | +====================================================|=======|=========|============|===========|==============| +Apple Mac Studio M2 Ultra 2023, Linux VM guest | 3500 | 16 | 163570 | 1157465 | both | +Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz | 4995 | 16 | 120064 | 1168943 | both | diff --git a/benchmark/README.md b/benchmark/README.md new file mode 100644 index 00000000..e37abad2 --- /dev/null +++ b/benchmark/README.md @@ -0,0 +1,51 @@ +# American Fuzzy Lop plus plus (AFL++) + +## benchmarking + +This directory contains benchmarking tools that allow you to compare one machine +with another in terms of raw ability to execute a fuzzing target repeatedly. + +To achieve this, we use a sample program ("test-instr.c") where each path is +equally likely, supply it a single seed, and tell AFL to exit after one run of +deterministic mutations against that seed. + +Usage example: + +``` +cd aflplusplus/benchmark +python3 benchmark.py + [*] Ready, starting benchmark... + [*] Compiling the test-instr-persist-shmem fuzzing harness for the benchmark to use. + [*] singlecore test-instr-persist-shmem run 1 of 2, execs/s: 124883.62 + [*] singlecore test-instr-persist-shmem run 2 of 2, execs/s: 126704.93 + [*] Average execs/sec for this test across all runs was: 125794.28 + [*] Using 16 fuzzers for multicore fuzzing (use --fuzzers to override). + [*] multicore test-instr-persist-shmem run 1 of 2, execs/s: 1179822.66 + [*] multicore test-instr-persist-shmem run 2 of 2, execs/s: 1175584.09 + [*] Average execs/sec for this test across all runs was: 1177703.38 + [*] Results have been written to the benchmark-results.jsonl file. + [*] Results have been written to the COMPARISON file. +``` + +By default, the script will use a number of parallel fuzzers equal to your +available CPUs/threads (change with `--fuzzers`), and will perform each test +three times and average the result (change with `--runs`). + +The script will use multicore fuzzing instead of singlecore by default (change +with `--mode singlecore`) and use a persistent-mode shared memory harness for +optimal speed (change with `--target test-instr`). + +Feel free to submit the resulting line for your CPU added to the COMPARISON +file back to aflplusplus in a pull request. + +Each run writes results to [benchmark-results.jsonl](benchmark-results.jsonl) +in [JSON Lines](https://jsonlines.org/) format, ready to be pulled in to other +tools such as [jq -cs](https://jqlang.github.io/jq/) or +[pandas](https://pandas.pydata.org/) for analysis. + +## Data analysis + +There is sample data in [benchmark-results.jsonl](benchmark-results.jsonl), and +a Jupyter notebook for exploring the results and suggesting their meaning at +[benchmark.ipynb](benchmark.ipynb). + diff --git a/benchmark/benchmark-results.jsonl b/benchmark/benchmark-results.jsonl new file mode 100644 index 00000000..f3dd60be --- /dev/null +++ b/benchmark/benchmark-results.jsonl @@ -0,0 +1,415 @@ +{"config": {"afl_persistent_config": true, "afl_system_config": true, "afl_version": "++4.09a", "comment": "i9-9900k, 16GB DDR4-3000, Arch Linux", "compiler": "clang version 16.0.6", "target_arch": "x86_64-pc-linux-gnu"}, "hardware": {"cpu_fastest_core_mhz": 4788.77, "cpu_model": "Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz", "cpu_threads": 16}, "targets": {"test-instr": {"singlecore": {"execs_per_sec": 9845.64, "execs_total": 98545, "fuzzers_used": 1}}}} +{"config": {"afl_persistent_config": true, "afl_system_config": true, "afl_version": "++4.09a", "comment": "i9-9900k, 16GB DDR4-3000, Arch Linux", "compiler": "clang version 16.0.6", "target_arch": "x86_64-pc-linux-gnu"}, "hardware": {"cpu_fastest_core_mhz": 4989.281, "cpu_model": "Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz", "cpu_threads": 16}, "targets": {"test-instr-persist-shmem": {"singlecore": {"execs_per_sec": 125682.73, "execs_total": 1257330, "fuzzers_used": 1}}}} +{"config": {"afl_persistent_config": true, "afl_system_config": true, "afl_version": "++4.09a", "comment": "i9-9900k, 16GB DDR4-3000, Arch Linux", "compiler": "clang version 16.0.6", "target_arch": "x86_64-pc-linux-gnu"}, "hardware": {"cpu_fastest_core_mhz": 4799.415, "cpu_model": "Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz", "cpu_threads": 16}, "targets": {"test-instr-persist-shmem": {"multicore": {"execs_per_sec": 120293.77, "execs_total": 1203058, "fuzzers_used": 1}}}} +{"config": {"afl_persistent_config": true, "afl_system_config": true, "afl_version": "++4.09a", "comment": "i9-9900k, 16GB DDR4-3000, Arch Linux", "compiler": "clang version 16.0.6", "target_arch": "x86_64-pc-linux-gnu"}, "hardware": {"cpu_fastest_core_mhz": 4703.293, "cpu_model": "Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz", "cpu_threads": 16}, "targets": {"test-instr-persist-shmem": {"multicore": {"execs_per_sec": 231429.96, "execs_total": 2314531, "fuzzers_used": 2}}}} +{"config": {"afl_persistent_config": true, "afl_system_config": true, "afl_version": "++4.09a", "comment": "i9-9900k, 16GB DDR4-3000, Arch Linux", "compiler": "clang version 16.0.6", "target_arch": "x86_64-pc-linux-gnu"}, "hardware": {"cpu_fastest_core_mhz": 4800.375, "cpu_model": "Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz", "cpu_threads": 16}, "targets": {"test-instr-persist-shmem": {"multicore": {"execs_per_sec": 346759.33, "execs_total": 3468290, "fuzzers_used": 3}}}} +{"config": {"afl_persistent_config": true, "afl_system_config": true, "afl_version": "++4.09a", "comment": "i9-9900k, 16GB DDR4-3000, Arch Linux", "compiler": "clang version 16.0.6", "target_arch": "x86_64-pc-linux-gnu"}, "hardware": {"cpu_fastest_core_mhz": 4915.27, "cpu_model": "Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz", "cpu_threads": 16}, "targets": {"test-instr-persist-shmem": {"multicore": {"execs_per_sec": 455340.06, "execs_total": 4554427, "fuzzers_used": 4}}}} +{"config": {"afl_persistent_config": true, "afl_system_config": true, "afl_version": "++4.09a", "comment": "i9-9900k, 16GB DDR4-3000, Arch Linux", "compiler": "clang version 16.0.6", "target_arch": "x86_64-pc-linux-gnu"}, "hardware": {"cpu_fastest_core_mhz": 4701.051, "cpu_model": "Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz", "cpu_threads": 16}, "targets": {"test-instr-persist-shmem": {"multicore": {"execs_per_sec": 568405.15, "execs_total": 5685076, "fuzzers_used": 5}}}} +{"config": {"afl_persistent_config": true, "afl_system_config": true, "afl_version": "++4.09a", "comment": "i9-9900k, 16GB DDR4-3000, Arch Linux", "compiler": "clang version 16.0.6", "target_arch": "x86_64-pc-linux-gnu"}, "hardware": {"cpu_fastest_core_mhz": 4704.999, 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"target_arch": "x86_64-amazon-linux-gnu"}, "hardware": {"cpu_fastest_core_mhz": 3437.101, "cpu_model": "AMD EPYC 7R13 Processor", "cpu_threads": 192}, "targets": {"test-instr-persist-shmem": {"multicore": {"execs_per_sec": 474259.76, "execs_total": 4745505, "fuzzers_used": 191}}}} +{"config": {"afl_persistent_config": true, "afl_system_config": true, "afl_version": "++4.09a", "comment": "AWS EC2 r6a.48xlarge spot instance", "compiler": "clang version 15.0.7 (Amazon Linux 15.0.7-3.amzn2023.0.1)", "target_arch": "x86_64-amazon-linux-gnu"}, "hardware": {"cpu_fastest_core_mhz": 3599.17, "cpu_model": "AMD EPYC 7R13 Processor", "cpu_threads": 192}, "targets": {"test-instr-persist-shmem": {"multicore": {"execs_per_sec": 479848.23, "execs_total": 4801111, "fuzzers_used": 192}}}} diff --git a/benchmark/benchmark.ipynb b/benchmark/benchmark.ipynb new file mode 100644 index 00000000..aea2e0f1 --- /dev/null +++ b/benchmark/benchmark.ipynb @@ -0,0 +1,1445 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [], + "source": [ + "# benchmark.ipynb\n", + "# Part of the aflplusplus project, requires an ipynb (Jupyter) editor or viewer.\n", + "# Author: Chris Ball <chris@printf.net>\n", + "import json\n", + "import pandas as pd\n", + "with open(\"benchmark-results.jsonl\") as f:\n", + " lines = f.read().splitlines()\n", + "json_lines = [json.loads(line) for line in lines]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Translate the JSON Lines entries into a single pandas DataFrame\n", + "\n", + "We have JSON Lines in [benchmark-results.jsonl](benchmark-results.jsonl) that look like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"config\": {\n", + " \"afl_persistent_config\": true,\n", + " \"afl_system_config\": true,\n", + " \"afl_version\": \"++4.09a\",\n", + " \"comment\": \"i9-9900k, 16GB DDR4-3000, Arch Linux\",\n", + " \"compiler\": \"clang version 16.0.6\",\n", + " \"target_arch\": \"x86_64-pc-linux-gnu\"\n", + " },\n", + " \"hardware\": {\n", + " \"cpu_fastest_core_mhz\": 4788.77,\n", + " \"cpu_model\": \"Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz\",\n", + " \"cpu_threads\": 16\n", + " },\n", + " \"targets\": {\n", + " \"test-instr\": {\n", + " \"singlecore\": {\n", + " \"execs_per_sec\": 9845.64,\n", + " \"execs_total\": 98545,\n", + " \"fuzzers_used\": 1\n", + " }\n", + " }\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "print(json.dumps(json.loads(lines[0]), indent=2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The [pd.json_normalize()](https://pandas.pydata.org/docs/reference/api/pandas.json_normalize.html]) method translates this into a flat table that we can perform queries against:" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [ + { + "data": { + "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>config.afl_persistent_config</th>\n", + " <th>config.afl_system_config</th>\n", + " <th>config.afl_version</th>\n", + " <th>config.comment</th>\n", + " <th>config.compiler</th>\n", + " <th>config.target_arch</th>\n", + " <th>hardware.cpu_fastest_core_mhz</th>\n", + " <th>hardware.cpu_model</th>\n", + " <th>hardware.cpu_threads</th>\n", + " <th>targets.test-instr.singlecore.execs_per_sec</th>\n", + " <th>...</th>\n", + " <th>targets.test-instr.singlecore.fuzzers_used</th>\n", + " <th>targets.test-instr-persist-shmem.singlecore.execs_per_sec</th>\n", + " <th>targets.test-instr-persist-shmem.singlecore.execs_total</th>\n", + " <th>targets.test-instr-persist-shmem.singlecore.fuzzers_used</th>\n", + " <th>targets.test-instr-persist-shmem.multicore.execs_per_sec</th>\n", + " <th>targets.test-instr-persist-shmem.multicore.execs_total</th>\n", + " <th>targets.test-instr-persist-shmem.multicore.fuzzers_used</th>\n", + " <th>targets.test-instr.multicore.execs_per_sec</th>\n", + " <th>targets.test-instr.multicore.execs_total</th>\n", + " <th>targets.test-instr.multicore.fuzzers_used</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>True</td>\n", + " <td>True</td>\n", + " <td>++4.09a</td>\n", + " <td>i9-9900k, 16GB DDR4-3000, Arch Linux</td>\n", + " <td>clang version 16.0.6</td>\n", + " <td>x86_64-pc-linux-gnu</td>\n", + " <td>4788.770</td>\n", + " <td>Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz</td>\n", + " <td>16</td>\n", + " <td>9845.64</td>\n", + " <td>...</td>\n", + " <td>1.0</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>True</td>\n", + " <td>True</td>\n", + " <td>++4.09a</td>\n", + " <td>i9-9900k, 16GB DDR4-3000, Arch Linux</td>\n", + " <td>clang version 16.0.6</td>\n", + " <td>x86_64-pc-linux-gnu</td>\n", + " <td>4989.281</td>\n", + " <td>Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz</td>\n", + " <td>16</td>\n", + " <td>NaN</td>\n", + " <td>...</td>\n", + " <td>NaN</td>\n", + " <td>125682.73</td>\n", + " <td>1257330.0</td>\n", + " <td>1.0</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>True</td>\n", + " <td>True</td>\n", + " <td>++4.09a</td>\n", + " <td>i9-9900k, 16GB DDR4-3000, Arch Linux</td>\n", + " <td>clang version 16.0.6</td>\n", + " <td>x86_64-pc-linux-gnu</td>\n", + " <td>4799.415</td>\n", + " <td>Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz</td>\n", + " <td>16</td>\n", + " <td>NaN</td>\n", + " <td>...</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>120293.77</td>\n", + " <td>1203058.0</td>\n", + " <td>1.0</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>True</td>\n", + " <td>True</td>\n", + " <td>++4.09a</td>\n", + " <td>i9-9900k, 16GB DDR4-3000, Arch Linux</td>\n", + " <td>clang version 16.0.6</td>\n", + " <td>x86_64-pc-linux-gnu</td>\n", + " <td>4703.293</td>\n", + " <td>Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz</td>\n", + " <td>16</td>\n", + " <td>NaN</td>\n", + " <td>...</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>231429.96</td>\n", + " <td>2314531.0</td>\n", + " <td>2.0</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>True</td>\n", + " <td>True</td>\n", + " <td>++4.09a</td>\n", + " <td>i9-9900k, 16GB DDR4-3000, Arch Linux</td>\n", + " <td>clang version 16.0.6</td>\n", + " <td>x86_64-pc-linux-gnu</td>\n", + " <td>4800.375</td>\n", + " <td>Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz</td>\n", + " <td>16</td>\n", + " <td>NaN</td>\n", + " <td>...</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>346759.33</td>\n", + " <td>3468290.0</td>\n", + " <td>3.0</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>5 rows × 21 columns</p>\n", + "</div>" + ], + "text/plain": [ + " config.afl_persistent_config config.afl_system_config config.afl_version \\\n", + "0 True True ++4.09a \n", + "1 True True ++4.09a \n", + "2 True True ++4.09a \n", + "3 True True ++4.09a \n", + "4 True True ++4.09a \n", + "\n", + " config.comment config.compiler \\\n", + "0 i9-9900k, 16GB DDR4-3000, Arch Linux clang version 16.0.6 \n", + "1 i9-9900k, 16GB DDR4-3000, Arch Linux clang version 16.0.6 \n", + "2 i9-9900k, 16GB DDR4-3000, Arch Linux clang version 16.0.6 \n", + "3 i9-9900k, 16GB DDR4-3000, Arch Linux clang version 16.0.6 \n", + "4 i9-9900k, 16GB DDR4-3000, Arch Linux clang version 16.0.6 \n", + "\n", + " config.target_arch hardware.cpu_fastest_core_mhz \\\n", + "0 x86_64-pc-linux-gnu 4788.770 \n", + "1 x86_64-pc-linux-gnu 4989.281 \n", + "2 x86_64-pc-linux-gnu 4799.415 \n", + "3 x86_64-pc-linux-gnu 4703.293 \n", + "4 x86_64-pc-linux-gnu 4800.375 \n", + "\n", + " hardware.cpu_model hardware.cpu_threads \\\n", + "0 Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz 16 \n", + "1 Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz 16 \n", + "2 Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz 16 \n", + "3 Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz 16 \n", + "4 Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz 16 \n", + "\n", + " targets.test-instr.singlecore.execs_per_sec ... \\\n", + "0 9845.64 ... \n", + "1 NaN ... \n", + "2 NaN ... \n", + "3 NaN ... \n", + "4 NaN ... \n", + "\n", + " targets.test-instr.singlecore.fuzzers_used \\\n", + "0 1.0 \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + " targets.test-instr-persist-shmem.singlecore.execs_per_sec \\\n", + "0 NaN \n", + "1 125682.73 \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + " targets.test-instr-persist-shmem.singlecore.execs_total \\\n", + "0 NaN \n", + "1 1257330.0 \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + " targets.test-instr-persist-shmem.singlecore.fuzzers_used \\\n", + "0 NaN \n", + "1 1.0 \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + " targets.test-instr-persist-shmem.multicore.execs_per_sec \\\n", + "0 NaN \n", + "1 NaN \n", + "2 120293.77 \n", + "3 231429.96 \n", + "4 346759.33 \n", + "\n", + " targets.test-instr-persist-shmem.multicore.execs_total \\\n", + "0 NaN \n", + "1 NaN \n", + "2 1203058.0 \n", + "3 2314531.0 \n", + "4 3468290.0 \n", + "\n", + " targets.test-instr-persist-shmem.multicore.fuzzers_used \\\n", + "0 NaN \n", + "1 NaN \n", + "2 1.0 \n", + "3 2.0 \n", + "4 3.0 \n", + "\n", + " targets.test-instr.multicore.execs_per_sec \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + " targets.test-instr.multicore.execs_total \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + " targets.test-instr.multicore.fuzzers_used \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + "[5 rows x 21 columns]" + ] + }, + "execution_count": 144, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "df = pd.json_normalize(json_lines)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Graph prep\n", + "\n", + "We're looking for a line graph showing lines for each fuzz target, in both singlecore and multicore modes, in each config setting -- where the x-axis is number of cores, and the y-axis is execs_per_sec.\n", + "\n", + "First, a quick check that the number of rows matched what we'd intuitively expect:" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [], + "source": [ + "i7 = df.query(\"`config.comment` == 'i9-9900k, 16GB DDR4-3000, Arch Linux'\")\n", + "assert len(i7) == 185" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [], + "source": [ + "def build_graphdf_from_query(query: pd.DataFrame):\n", + " \"\"\"Build a table suitable for graphing from a subset of the dataframe.\"\"\"\n", + " graphdata = []\n", + " max_fuzzers = int(query[[\"targets.test-instr-persist-shmem.multicore.fuzzers_used\", \"targets.test-instr.multicore.fuzzers_used\"]].max(axis=1).max(axis=0))\n", + " for _, row in query.iterrows():\n", + " for target in [\"test-instr-persist-shmem\", \"test-instr\"]:\n", + " for mode in [\"multicore\", \"singlecore\"]:\n", + " label = \"\"\n", + " if not row[f\"targets.{target}.{mode}.execs_per_sec\"] > 0:\n", + " continue\n", + " execs_per_sec = row[f\"targets.{target}.{mode}.execs_per_sec\"]\n", + " parallel_fuzzers = row[f\"targets.{target}.{mode}.fuzzers_used\"]\n", + " afl_persistent_config = row[\"config.afl_persistent_config\"]\n", + " afl_system_config = row[\"config.afl_system_config\"]\n", + " if target == \"test-instr-persist-shmem\":\n", + " label += \"shmem\"\n", + " else:\n", + " label += \"base\"\n", + " if mode == \"multicore\":\n", + " label += \"-multicore\"\n", + " else:\n", + " label += \"-singlecore\"\n", + " if afl_persistent_config:\n", + " label += \"+persist-conf\"\n", + " if afl_system_config:\n", + " label += \"+system-conf\"\n", + " \n", + " if label == \"shmem-multicore+persist-conf+system-conf\":\n", + " graphdata.append({\"execs_per_sec\": execs_per_sec, \"parallel_fuzzers\": parallel_fuzzers, \"afl_persistent_config\": afl_persistent_config, \"afl_system_config\": afl_system_config, \"label\": \"Multicore: Persistent mode/shared memory + kernel config\"})\n", + " if label == \"shmem-multicore\":\n", + " graphdata.append({\"execs_per_sec\": execs_per_sec, \"parallel_fuzzers\": parallel_fuzzers, \"afl_persistent_config\": afl_persistent_config, \"afl_system_config\": afl_system_config, \"label\": \"Multicore: Persistent mode/shared memory without kernel config\"})\n", + " if label == \"base-multicore+persist-conf+system-conf\":\n", + " graphdata.append({\"execs_per_sec\": execs_per_sec, \"parallel_fuzzers\": parallel_fuzzers, \"afl_persistent_config\": afl_persistent_config, \"afl_system_config\": afl_system_config, \"label\": \"Multicore: Non-persistent mode + kernel config\"})\n", + " if label == \"shmem-singlecore+persist-conf+system-conf\":\n", + " for i in range(1, max_fuzzers + 1):\n", + " graphdata.append({\"execs_per_sec\": execs_per_sec, \"parallel_fuzzers\": float(i), \"afl_persistent_config\": afl_persistent_config, \"afl_system_config\": afl_system_config, \"label\": \"Singlecore: Persistent mode/shared memory + kernel config\"})\n", + " if label == \"base-singlecore+persist-conf+system-conf\":\n", + " for i in range(1, max_fuzzers + 1):\n", + " graphdata.append({\"execs_per_sec\": execs_per_sec, \"parallel_fuzzers\": float(i), \"afl_persistent_config\": afl_persistent_config, \"afl_system_config\": afl_system_config, \"label\": \"Singlecore: Non-persistent mode + kernel config\"})\n", + " return pd.DataFrame.from_records(graphdata).sort_values(\"label\", ascending=False)\n", + "\n", + "graphdf = build_graphdf_from_query(i7)" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "<svg class=\"main-svg\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"1200\" height=\"400\" style=\"\" viewBox=\"0 0 1200 400\"><rect x=\"0\" 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y=\"333\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(726.4,0)\">33</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"333\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(746.6,0)\">34</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"333\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(766.8,0)\">35</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"333\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(787,0)\">36</text></g></g><g class=\"yaxislayer-above\"><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" transform=\"translate(0,309)\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\">1x</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(0,269.4)\">25x</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(0,229.8)\">48x</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(0,190.2)\">72x</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(0,150.6)\">95x</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"79\" y=\"4.199999999999999\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(0,111)\">119x</text></g></g><g class=\"overaxes-above\"/></g></g><g class=\"polarlayer\"/><g class=\"smithlayer\"/><g class=\"ternarylayer\"/><g class=\"geolayer\"/><g class=\"funnelarealayer\"/><g class=\"pielayer\"/><g class=\"iciclelayer\"/><g class=\"treemaplayer\"/><g class=\"sunburstlayer\"/><g class=\"glimages\"/><defs id=\"topdefs-9d6f0e\"><g class=\"clips\"/><clipPath id=\"legend9d6f0e\"><rect width=\"387\" height=\"124\" x=\"0\" y=\"0\"/></clipPath></defs><g class=\"layer-above\"><g class=\"imagelayer\"/><g class=\"shapelayer\"/></g><g class=\"infolayer\"><g class=\"legend\" pointer-events=\"all\" transform=\"translate(801.14,100)\"><rect class=\"bg\" shape-rendering=\"crispEdges\" width=\"387\" height=\"124\" x=\"0\" y=\"0\" style=\"stroke: rgb(68, 68, 68); stroke-opacity: 1; fill: rgb(255, 255, 255); fill-opacity: 1; stroke-width: 0px;\"/><g class=\"scrollbox\" transform=\"\" clip-path=\"url(#legend9d6f0e)\"><text class=\"legendtitletext\" text-anchor=\"start\" x=\"2\" y=\"18.2\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Configuration</text><g class=\"groups\" transform=\"\"><g class=\"traces\" transform=\"translate(0,32.7)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Multicore: Non-persistent mode + kernel config</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(99, 110, 250); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"/></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"381.484375\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g></g><g class=\"groups\" transform=\"\"><g class=\"traces\" transform=\"translate(0,51.7)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Multicore: Persistent mode/shared memory + kernel config</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(239, 85, 59); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"/></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"381.484375\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g></g><g class=\"groups\" transform=\"\"><g class=\"traces\" transform=\"translate(0,70.7)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Multicore: Persistent mode/shared memory without kernel config</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(0, 204, 150); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"/></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"381.484375\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g></g><g class=\"groups\" transform=\"\"><g class=\"traces\" transform=\"translate(0,89.7)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Singlecore: Non-persistent mode + kernel config</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(171, 99, 250); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"/></g></g><rect class=\"legendtoggle\" x=\"0\" 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x=\"0\" y=\"0\" style=\"fill: rgb(128, 139, 164); fill-opacity: 1;\"/></g><g class=\"g-gtitle\"><text class=\"gtitle\" x=\"60\" y=\"50\" text-anchor=\"start\" dy=\"0em\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 17px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Fuzzer performance</text></g><g class=\"g-xtitle\"><text class=\"xtitle\" x=\"433.5\" y=\"360.3\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Number of parallel fuzzers</text></g><g class=\"g-ytitle\"><text class=\"ytitle\" transform=\"rotate(-90,29.559375000000003,210)\" x=\"29.559375000000003\" y=\"210\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Fuzz target executions per second</text></g></g></svg>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "pd.options.plotting.backend = \"plotly\"\n", + "\n", + "# Right now our table has absolute values of execs per sec, but it's more useful\n", + "# to show relative perf (vs 1.0x baseline)\n", + "pivotdf = graphdf.pivot(index=\"parallel_fuzzers\", columns=\"label\", values=\"execs_per_sec\")\n", + "fig = pivotdf.plot(\n", + " title=\"Fuzzer performance\",\n", + " labels={\n", + " \"label\": \"Configuration\",\n", + " \"parallel_fuzzers\": \"Number of parallel fuzzers\",\n", + " \"value\": \"Fuzz target executions per second\"\n", + " }\n", + ")\n", + "\n", + "# Compute tick values and their labels for the primary Y-axis\n", + "tickvals = np.linspace(graphdf['execs_per_sec'].min(), graphdf['execs_per_sec'].max(), 6)\n", + "ticktext = [f\"{val:.0f}x\" for val in tickvals / graphdf['execs_per_sec'].min()]\n", + "# Update the primary Y-axis with custom tick labels\n", + "fig.update_yaxes(tickvals=tickvals, ticktext=ticktext)\n", + "fig.update_xaxes(tickvals=list(range(1,36+1)))\n", + "fig.update_layout(width=1200, height=400)\n", + "fig.show(\"svg\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's what the table that produced this graph looks like:" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [ + { + "data": { + "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>label</th>\n", + " <th>Multicore: Non-persistent mode + kernel config</th>\n", + " <th>Multicore: Persistent mode/shared memory + kernel config</th>\n", + " <th>Multicore: Persistent mode/shared memory without kernel config</th>\n", + " <th>Singlecore: Non-persistent mode + kernel config</th>\n", + " <th>Singlecore: Persistent mode/shared memory + kernel config</th>\n", + " </tr>\n", + " <tr>\n", + " <th>parallel_fuzzers</th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>1.0</th>\n", + " <td>10714.79</td>\n", + " <td>120293.77</td>\n", + " <td>90641.62</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2.0</th>\n", + " <td>20493.07</td>\n", + " <td>231429.96</td>\n", + " <td>178184.19</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3.0</th>\n", + " <td>29660.06</td>\n", + " <td>346759.33</td>\n", + " <td>262652.86</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4.0</th>\n", + " <td>37875.57</td>\n", + " <td>455340.06</td>\n", + " <td>339119.32</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5.0</th>\n", + " <td>46326.75</td>\n", + " <td>568405.15</td>\n", + " <td>420239.94</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6.0</th>\n", + " <td>54595.48</td>\n", + " <td>678030.96</td>\n", + " <td>498062.02</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7.0</th>\n", + " <td>62720.98</td>\n", + " <td>782585.04</td>\n", + " <td>578495.44</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8.0</th>\n", + " <td>70777.99</td>\n", + " <td>893618.35</td>\n", + " <td>661836.22</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9.0</th>\n", + " <td>74236.02</td>\n", + " <td>956026.15</td>\n", + " <td>684808.49</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10.0</th>\n", + " <td>78134.94</td>\n", + " <td>984942.13</td>\n", + " <td>707094.65</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>11.0</th>\n", + " <td>81886.33</td>\n", + " <td>1016758.62</td>\n", + " <td>732106.17</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>12.0</th>\n", + " <td>85923.44</td>\n", + " <td>1053087.90</td>\n", + " <td>752910.17</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>13.0</th>\n", + " <td>89696.95</td>\n", + " <td>1085797.87</td>\n", + " <td>776179.85</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>14.0</th>\n", + " <td>93540.52</td>\n", + " <td>1110640.20</td>\n", + " <td>797520.58</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>15.0</th>\n", + " <td>97641.51</td>\n", + " <td>1138984.22</td>\n", + " <td>822235.41</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>16.0</th>\n", + " <td>101692.65</td>\n", + " <td>1168943.19</td>\n", + " <td>843897.51</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>17.0</th>\n", + " <td>101236.75</td>\n", + " <td>1135093.91</td>\n", + " <td>843177.15</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>18.0</th>\n", + " <td>101006.28</td>\n", + " <td>1160430.45</td>\n", + " <td>844779.09</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>19.0</th>\n", + " <td>99952.26</td>\n", + " <td>1155769.97</td>\n", + " <td>846060.74</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>20.0</th>\n", + " <td>99798.64</td>\n", + " <td>1150156.26</td>\n", + " <td>847556.23</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>21.0</th>\n", + " <td>99018.86</td>\n", + " <td>1136873.58</td>\n", + " <td>844022.97</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>22.0</th>\n", + " <td>98600.87</td>\n", + " <td>1112404.25</td>\n", + " <td>845818.70</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>23.0</th>\n", + " <td>98634.02</td>\n", + " <td>1143131.72</td>\n", + " <td>844118.27</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>24.0</th>\n", + " <td>98352.90</td>\n", + " <td>1143931.38</td>\n", + " <td>837189.02</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>25.0</th>\n", + " <td>98118.63</td>\n", + " <td>1102090.61</td>\n", + " <td>834712.31</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>26.0</th>\n", + " <td>97752.45</td>\n", + " <td>1116518.70</td>\n", + " <td>836344.12</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>27.0</th>\n", + " <td>97864.07</td>\n", + " <td>1099224.19</td>\n", + " <td>827784.91</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>28.0</th>\n", + " <td>97821.80</td>\n", + " <td>1114945.37</td>\n", + " <td>828641.27</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>29.0</th>\n", + " <td>97564.87</td>\n", + " <td>1110889.91</td>\n", + " <td>826123.67</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>30.0</th>\n", + " <td>98508.10</td>\n", + " <td>1058548.28</td>\n", + " <td>817765.77</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>31.0</th>\n", + " <td>98238.96</td>\n", + " <td>1119804.85</td>\n", + " <td>816556.66</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>32.0</th>\n", + " <td>98363.93</td>\n", + " <td>1118828.99</td>\n", + " <td>812661.77</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>33.0</th>\n", + " <td>96758.69</td>\n", + " <td>1093426.61</td>\n", + " <td>805352.16</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>34.0</th>\n", + " <td>96327.00</td>\n", + " <td>1108123.59</td>\n", + " <td>815888.26</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>35.0</th>\n", + " <td>95913.98</td>\n", + " <td>1041486.52</td>\n", + " <td>812348.56</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " <tr>\n", + " <th>36.0</th>\n", + " <td>95871.39</td>\n", + " <td>1092395.61</td>\n", + " <td>817278.03</td>\n", + " <td>9845.64</td>\n", + " <td>125682.73</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + "label Multicore: Non-persistent mode + kernel config \\\n", + "parallel_fuzzers \n", + "1.0 10714.79 \n", + "2.0 20493.07 \n", + "3.0 29660.06 \n", + "4.0 37875.57 \n", + "5.0 46326.75 \n", + "6.0 54595.48 \n", + "7.0 62720.98 \n", + "8.0 70777.99 \n", + "9.0 74236.02 \n", + "10.0 78134.94 \n", + "11.0 81886.33 \n", + "12.0 85923.44 \n", + "13.0 89696.95 \n", + "14.0 93540.52 \n", + "15.0 97641.51 \n", + "16.0 101692.65 \n", + "17.0 101236.75 \n", + "18.0 101006.28 \n", + "19.0 99952.26 \n", + "20.0 99798.64 \n", + "21.0 99018.86 \n", + "22.0 98600.87 \n", + "23.0 98634.02 \n", + "24.0 98352.90 \n", + "25.0 98118.63 \n", + "26.0 97752.45 \n", + "27.0 97864.07 \n", + "28.0 97821.80 \n", + "29.0 97564.87 \n", + "30.0 98508.10 \n", + "31.0 98238.96 \n", + "32.0 98363.93 \n", + "33.0 96758.69 \n", + "34.0 96327.00 \n", + "35.0 95913.98 \n", + "36.0 95871.39 \n", + "\n", + "label Multicore: Persistent mode/shared memory + kernel config \\\n", + "parallel_fuzzers \n", + "1.0 120293.77 \n", + "2.0 231429.96 \n", + "3.0 346759.33 \n", + "4.0 455340.06 \n", + "5.0 568405.15 \n", + "6.0 678030.96 \n", + "7.0 782585.04 \n", + "8.0 893618.35 \n", + "9.0 956026.15 \n", + "10.0 984942.13 \n", + "11.0 1016758.62 \n", + "12.0 1053087.90 \n", + "13.0 1085797.87 \n", + "14.0 1110640.20 \n", + "15.0 1138984.22 \n", + "16.0 1168943.19 \n", + "17.0 1135093.91 \n", + "18.0 1160430.45 \n", + "19.0 1155769.97 \n", + "20.0 1150156.26 \n", + "21.0 1136873.58 \n", + "22.0 1112404.25 \n", + "23.0 1143131.72 \n", + "24.0 1143931.38 \n", + "25.0 1102090.61 \n", + "26.0 1116518.70 \n", + "27.0 1099224.19 \n", + "28.0 1114945.37 \n", + "29.0 1110889.91 \n", + "30.0 1058548.28 \n", + "31.0 1119804.85 \n", + "32.0 1118828.99 \n", + "33.0 1093426.61 \n", + "34.0 1108123.59 \n", + "35.0 1041486.52 \n", + "36.0 1092395.61 \n", + "\n", + "label Multicore: Persistent mode/shared memory without kernel config \\\n", + "parallel_fuzzers \n", + "1.0 90641.62 \n", + "2.0 178184.19 \n", + "3.0 262652.86 \n", + "4.0 339119.32 \n", + "5.0 420239.94 \n", + "6.0 498062.02 \n", + "7.0 578495.44 \n", + "8.0 661836.22 \n", + "9.0 684808.49 \n", + "10.0 707094.65 \n", + "11.0 732106.17 \n", + "12.0 752910.17 \n", + "13.0 776179.85 \n", + "14.0 797520.58 \n", + "15.0 822235.41 \n", + "16.0 843897.51 \n", + "17.0 843177.15 \n", + "18.0 844779.09 \n", + "19.0 846060.74 \n", + "20.0 847556.23 \n", + "21.0 844022.97 \n", + "22.0 845818.70 \n", + "23.0 844118.27 \n", + "24.0 837189.02 \n", + "25.0 834712.31 \n", + "26.0 836344.12 \n", + "27.0 827784.91 \n", + "28.0 828641.27 \n", + "29.0 826123.67 \n", + "30.0 817765.77 \n", + "31.0 816556.66 \n", + "32.0 812661.77 \n", + "33.0 805352.16 \n", + "34.0 815888.26 \n", + "35.0 812348.56 \n", + "36.0 817278.03 \n", + "\n", + "label Singlecore: Non-persistent mode + kernel config \\\n", + "parallel_fuzzers \n", + "1.0 9845.64 \n", + "2.0 9845.64 \n", + "3.0 9845.64 \n", + "4.0 9845.64 \n", + "5.0 9845.64 \n", + "6.0 9845.64 \n", + "7.0 9845.64 \n", + "8.0 9845.64 \n", + "9.0 9845.64 \n", + "10.0 9845.64 \n", + "11.0 9845.64 \n", + "12.0 9845.64 \n", + "13.0 9845.64 \n", + "14.0 9845.64 \n", + "15.0 9845.64 \n", + "16.0 9845.64 \n", + "17.0 9845.64 \n", + "18.0 9845.64 \n", + "19.0 9845.64 \n", + "20.0 9845.64 \n", + "21.0 9845.64 \n", + "22.0 9845.64 \n", + "23.0 9845.64 \n", + "24.0 9845.64 \n", + "25.0 9845.64 \n", + "26.0 9845.64 \n", + "27.0 9845.64 \n", + "28.0 9845.64 \n", + "29.0 9845.64 \n", + "30.0 9845.64 \n", + "31.0 9845.64 \n", + "32.0 9845.64 \n", + "33.0 9845.64 \n", + "34.0 9845.64 \n", + "35.0 9845.64 \n", + "36.0 9845.64 \n", + "\n", + "label Singlecore: Persistent mode/shared memory + kernel config \n", + "parallel_fuzzers \n", + "1.0 125682.73 \n", + "2.0 125682.73 \n", + "3.0 125682.73 \n", + "4.0 125682.73 \n", + "5.0 125682.73 \n", + "6.0 125682.73 \n", + "7.0 125682.73 \n", + "8.0 125682.73 \n", + "9.0 125682.73 \n", + "10.0 125682.73 \n", + "11.0 125682.73 \n", + "12.0 125682.73 \n", + "13.0 125682.73 \n", + "14.0 125682.73 \n", + "15.0 125682.73 \n", + "16.0 125682.73 \n", + "17.0 125682.73 \n", + "18.0 125682.73 \n", + "19.0 125682.73 \n", + "20.0 125682.73 \n", + "21.0 125682.73 \n", + "22.0 125682.73 \n", + "23.0 125682.73 \n", + "24.0 125682.73 \n", + "25.0 125682.73 \n", + "26.0 125682.73 \n", + "27.0 125682.73 \n", + "28.0 125682.73 \n", + "29.0 125682.73 \n", + "30.0 125682.73 \n", + "31.0 125682.73 \n", + "32.0 125682.73 \n", + "33.0 125682.73 \n", + "34.0 125682.73 \n", + "35.0 125682.73 \n", + "36.0 125682.73 " + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pivotdf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can totally ignore the code cell directly below (unless you're curious). It's just preparing Markdown for the block below it to render. Jupyter Notebooks aren't able to use code variables inside Markdown blocks, so I have to do this instead." + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "\n", + "### Line graph analysis\n", + "Here are a few things that jump out from the graph above. Let's start at the bottom of the graph.\n", + "\n", + "#### test-instr vs. test-instr-persist-shmem\n", + "\n", + "This graph is scaled so that the single-core, non-persistent-mode performance (9845 execs per second) is\n", + "represented as **1.0x**. If you build and run a fuzzer without creating a persistent mode harness for it, and without running fuzzers in parallel, this is the performance\n", + "you get on this machine.\n", + "\n", + "#### Multicore test-instr\n", + "\n", + "By running as many parallel fuzzers are there are CPU threads, we can reach 101692 execs per second, which is **10.3x** that base speed.\n", + "\n", + "#### Persistent mode + shared memory\n", + "\n", + "##### Singlecore\n", + "\n", + "By modifying the harness to use persistent mode with shared memory as described [here](https://github.com/AFLplusplus/AFLplusplus/blob/stable/instrumentation/README.persistent_mode.md#4-persistent-mode),\n", + "we end up with **12.8x** base speed. So -- perhaps counter-intuively -- if you have a choice between switching to using multiple cores or rewriting\n", + "the harness to use persistent mode on a single core, it is better (at least on this machine) to use persistent mode on a single core, than to use non-persistent mode on all cores.\n", + "\n", + "##### Multicore\n", + "\n", + "By scaling up that persistent mode with shared memory harness across cores, and with kernel mitigations still turned on (see next section), we get to\n", + "**86.1x** base speed.\n", + "\n", + "#### Kernel config\n", + "\n", + "By \"kernel config\", I'm referring to booting the Linux kernel with `mitigations=off`, which is a meta-parameter for disabling *all* hardware vulnerability meltdowns (such as Spectre,\n", + "Meltdown, Retbleed, etc) introduced in Linux v5.2. Disabling these results in a `execs_per_sec` increase of 321386 execs -- the difference between\n", + "118.7x (mitigations off) and 86.1x (mitigations on) base speed. Turning on mitigations\n", + "reduced the overall performance by 27%!\n", + "\n", + "One way to think about this is that the mitigations turn this 16-thread CPU into a 7-thread CPU, since the number of execs reached with 16 threads and mitigations on is around the same\n", + "number of execs reached with 7 threads and mitigations off.\n", + "\n", + "Or if we want to think in terms of cores, then the average number of execs gained per core in the initial eight is 110474 execs per sec, but the loss due to\n", + "mitigations is 321386 execs per sec, which is the averaged performance of 2.9 cores.\n", + "\n", + "With kernel mitigations turned off, we reach our highest available execs_per_sec speed on this machine, which is **118.7x** higher\n", + "than where we started from.\n", + "\n", + "#### How many parallel fuzzers should we use on this machine?\n", + "\n", + "* Using >16 is worse than using 16. Makes sense.\n", + "* So, we should use the number of CPUs in /proc/cpuinfo (threads) to get the best performance. But if we did halve the number of\n", + " fuzzers, we would surprisingly only lose 23%\n", + " of performance. This could be a good tradeoff in terms of cost.\n" + ], + "text/plain": [ + "<IPython.core.display.Markdown object>" + ] + }, + "execution_count": 149, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# (You can ignore reading this code cell.)\n", + "from IPython.display import Markdown as md\n", + "singlecore_base_execs = pivotdf.iloc[0][\"Singlecore: Non-persistent mode + kernel config\"]\n", + "singlecore_persist_execs = pivotdf.iloc[0][\"Singlecore: Persistent mode/shared memory + kernel config\"]\n", + "multicore_fuzzers_max_execs = int(pivotdf[\"Multicore: Persistent mode/shared memory + kernel config\"].idxmax())\n", + "multicore_base_max_execs = pivotdf[\"Multicore: Non-persistent mode + kernel config\"].max()\n", + "factor_for_execs = lambda execs: round(execs / singlecore_base_execs, 1)\n", + "\n", + "multicore_persistent_without_mitigations_label = \"Multicore: Persistent mode/shared memory + kernel config\"\n", + "multicore_max_execs_mitigations_off = pivotdf[multicore_persistent_without_mitigations_label].max()\n", + "multicore_max_execs_mitigations_off_only_cores = pivotdf.loc[multicore_fuzzers_max_execs / 2][multicore_persistent_without_mitigations_label]\n", + "multicore_max_execs_mitigations_on = pivotdf[\"Multicore: Persistent mode/shared memory without kernel config\"].max()\n", + "multicore_avg_gain_per_core = pivotdf.loc[pivotdf.index <= 8][\"Multicore: Persistent mode/shared memory + kernel config\"].diff().dropna().mean()\n", + "mitigations_off_increase = int(multicore_max_execs_mitigations_off - multicore_max_execs_mitigations_on)\n", + "\n", + "md(f\"\"\"\n", + "### Line graph analysis\n", + "Here are a few things that jump out from the graph above. Let's start at the bottom of the graph.\n", + "\n", + "#### test-instr vs. test-instr-persist-shmem\n", + "\n", + "This graph is scaled so that the single-core, non-persistent-mode performance ({int(singlecore_base_execs)} execs per second) is\n", + "represented as **1.0x**. If you build and run a fuzzer without creating a persistent mode harness for it, and without running fuzzers in parallel, this is the performance\n", + "you get on this machine.\n", + "\n", + "#### Multicore test-instr\n", + "\n", + "By running as many parallel fuzzers are there are CPU threads, we can reach {int(multicore_base_max_execs)} execs per second, which is **{factor_for_execs(multicore_base_max_execs)}x** that base speed.\n", + "\n", + "#### Persistent mode + shared memory\n", + "\n", + "##### Singlecore\n", + "\n", + "By modifying the harness to use persistent mode with shared memory as described [here](https://github.com/AFLplusplus/AFLplusplus/blob/stable/instrumentation/README.persistent_mode.md#4-persistent-mode),\n", + "we end up with **{factor_for_execs(singlecore_persist_execs)}x** base speed. So -- perhaps counter-intuively -- if you have a choice between switching to using multiple cores or rewriting\n", + "the harness to use persistent mode on a single core, it is better (at least on this machine) to use persistent mode on a single core, than to use non-persistent mode on all cores.\n", + "\n", + "##### Multicore\n", + "\n", + "By scaling up that persistent mode with shared memory harness across cores, and with kernel mitigations still turned on (see next section), we get to\n", + "**{factor_for_execs(multicore_max_execs_mitigations_on)}x** base speed.\n", + "\n", + "#### Kernel config\n", + "\n", + "By \"kernel config\", I'm referring to booting the Linux kernel with `mitigations=off`, which is a meta-parameter for disabling *all* hardware vulnerability meltdowns (such as Spectre,\n", + "Meltdown, Retbleed, etc) introduced in Linux v5.2. Disabling these results in a `execs_per_sec` increase of {mitigations_off_increase} execs -- the difference between\n", + "{factor_for_execs(multicore_max_execs_mitigations_off)}x (mitigations off) and {factor_for_execs(multicore_max_execs_mitigations_on)}x (mitigations on) base speed. Turning on mitigations\n", + "reduced the overall performance by {abs(round(((multicore_max_execs_mitigations_on - multicore_max_execs_mitigations_off) / multicore_max_execs_mitigations_off) * 100))}%!\n", + "\n", + "One way to think about this is that the mitigations turn this 16-thread CPU into a 7-thread CPU, since the number of execs reached with 16 threads and mitigations on is around the same\n", + "number of execs reached with 7 threads and mitigations off.\n", + "\n", + "Or if we want to think in terms of cores, then the average number of execs gained per core in the initial eight is {int(multicore_avg_gain_per_core)} execs per sec, but the loss due to\n", + "mitigations is {mitigations_off_increase} execs per sec, which is the averaged performance of {round(mitigations_off_increase / multicore_avg_gain_per_core, 1)} cores.\n", + "\n", + "With kernel mitigations turned off, we reach our highest available execs_per_sec speed on this machine, which is **{factor_for_execs(multicore_max_execs_mitigations_off)}x** higher\n", + "than where we started from.\n", + "\n", + "#### How many parallel fuzzers should we use on this machine?\n", + "\n", + "* Using >16 is worse than using 16. Makes sense.\n", + "* So, we should use the number of CPUs in /proc/cpuinfo (threads) to get the best performance. But if we did halve the number of\n", + " fuzzers, we would surprisingly only lose {abs(int(((multicore_max_execs_mitigations_off_only_cores - multicore_max_execs_mitigations_off) / multicore_max_execs_mitigations_off) * 100))}%\n", + " of performance. This could be a good tradeoff in terms of cost.\n", + "\"\"\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example with more cores\n", + "\n", + "While there was some nuance here, the answer was pretty straightforward -- use the number of CPU threads you have access to. What if there were more threads? Here the experiment is repeated on an AWS EC2 \"r6a.48xlarge\" spot instance with 192 vCPUs:" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [ + { + "data": { + "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>config.afl_persistent_config</th>\n", + " <th>config.afl_system_config</th>\n", + " <th>config.afl_version</th>\n", + " <th>config.comment</th>\n", + " <th>config.compiler</th>\n", + " <th>config.target_arch</th>\n", + " <th>hardware.cpu_fastest_core_mhz</th>\n", + " <th>hardware.cpu_model</th>\n", + " <th>hardware.cpu_threads</th>\n", + " 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<td>AMD EPYC 7R13 Processor</td>\n", + " <td>192</td>\n", + " <td>237177.20</td>\n", + " <td>2372250.0</td>\n", + " <td>2.0</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " config.afl_persistent_config config.afl_system_config \\\n", + "223 True True \n", + "224 True True \n", + "\n", + " config.afl_version config.comment \\\n", + "223 ++4.09a AWS EC2 r6a.48xlarge spot instance \n", + "224 ++4.09a AWS EC2 r6a.48xlarge spot instance \n", + "\n", + " config.compiler \\\n", + "223 clang version 15.0.7 (Amazon Linux 15.0.7-3.am... \n", + "224 clang version 15.0.7 (Amazon Linux 15.0.7-3.am... \n", + "\n", + " config.target_arch hardware.cpu_fastest_core_mhz \\\n", + "223 x86_64-amazon-linux-gnu 3514.326 \n", + "224 x86_64-amazon-linux-gnu 3599.748 \n", + "\n", + " hardware.cpu_model hardware.cpu_threads \\\n", + "223 AMD EPYC 7R13 Processor 192 \n", + "224 AMD EPYC 7R13 Processor 192 \n", + "\n", + " targets.test-instr-persist-shmem.multicore.execs_per_sec \\\n", + "223 119469.35 \n", + "224 237177.20 \n", + "\n", + " targets.test-instr-persist-shmem.multicore.execs_total \\\n", + "223 1194813.0 \n", + "224 2372250.0 \n", + "\n", + " targets.test-instr-persist-shmem.multicore.fuzzers_used \n", + "223 1.0 \n", + "224 2.0 " + ] + }, + "execution_count": 150, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r6a = df.query(\"`config.comment` == 'AWS EC2 r6a.48xlarge spot instance'\")\n", + "r6a.head(2).dropna(axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "data": { + "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\" 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" + ] + }, + "execution_count": 151, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r6a_graphdf = build_graphdf_from_query(r6a)\n", + "r6a_graphdf.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "<svg class=\"main-svg\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"1200\" height=\"400\" style=\"\" viewBox=\"0 0 1200 400\"><rect x=\"0\" y=\"0\" width=\"1200\" height=\"400\" style=\"fill: rgb(255, 255, 255); fill-opacity: 1;\"/><defs id=\"defs-cbb66b\"><g class=\"clips\"><clipPath id=\"clipcbb66bxyplot\" class=\"plotclip\"><rect width=\"737\" height=\"220\"/></clipPath><clipPath class=\"axesclip\" id=\"clipcbb66bx\"><rect x=\"80\" y=\"0\" width=\"737\" height=\"400\"/></clipPath><clipPath class=\"axesclip\" id=\"clipcbb66by\"><rect x=\"0\" y=\"100\" width=\"1200\" height=\"220\"/></clipPath><clipPath class=\"axesclip\" id=\"clipcbb66bxy\"><rect x=\"80\" y=\"100\" width=\"737\" height=\"220\"/></clipPath></g><g class=\"gradients\"/><g class=\"patterns\"/></defs><g class=\"bglayer\"><rect class=\"bg\" x=\"80\" y=\"100\" width=\"737\" height=\"220\" style=\"fill: rgb(229, 236, 246); 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fill-opacity: 0;\"/></g></g></g><rect class=\"scrollbar\" rx=\"20\" ry=\"3\" width=\"0\" height=\"0\" x=\"0\" y=\"0\" style=\"fill: rgb(128, 139, 164); fill-opacity: 1;\"/></g><g class=\"g-gtitle\"><text class=\"gtitle\" x=\"60\" y=\"50\" text-anchor=\"start\" dy=\"0em\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 17px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Fuzzer performance</text></g><g class=\"g-xtitle\"><text class=\"xtitle\" x=\"448.5\" y=\"371.659375\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Number of parallel fuzzers</text></g><g class=\"g-ytitle\"><text class=\"ytitle\" transform=\"rotate(-90,28.668750000000003,210)\" x=\"28.668750000000003\" y=\"210\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Fuzz target executions per second</text></g></g></svg>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "r6a_pivotdf = r6a_graphdf.pivot(index=\"parallel_fuzzers\", columns=\"label\", values=\"execs_per_sec\")\n", + "r6a_fig = r6a_pivotdf.plot(\n", + " title=\"Fuzzer performance\",\n", + " labels={\n", + " \"label\": \"Configuration\",\n", + " \"parallel_fuzzers\": \"Number of parallel fuzzers\",\n", + " \"value\": \"Fuzz target executions per second\"\n", + " }\n", + ")\n", + "\n", + "# Compute tick values and their labels for the primary Y-axis\n", + "tickvals = np.linspace(r6a_graphdf['execs_per_sec'].min(), r6a_graphdf['execs_per_sec'].max(), 6)\n", + "ticktext = [f\"{val:.0f}x\" for val in tickvals / graphdf['execs_per_sec'].min()]\n", + "# Update the primary Y-axis with custom tick labels\n", + "r6a_fig.update_yaxes(tickvals=tickvals, ticktext=ticktext)\n", + "r6a_fig.update_xaxes(tickvals=list(range(0,200+1, 4)))\n", + "r6a_fig.update_layout(width=1200, height=400)\n", + "r6a_fig.show(\"svg\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Line graph analysis\n", + "\n", + "This is a shocking result for a 192 vCPU machine -- our optimal number of parallel fuzzers was 16! Using 32 parallel fuzzers gives less performance than using 8 fuzzers. Using 192 parallel fuzzers (the physical number of threads in this machine) gives the same performance as using 4 fuzzers.\n", + "\n", + "This is clearly a cautionary tale about measuring before simply using the number of hardware threads in your machine. But does this mean that AFL++ is a bad fuzzer, or that AWS tricked us and gave us a 16-thread machine instead of a 192-thread one?\n", + "\n", + "No, probably not -- the most likely cause here (other than a horrible bug) may be that we're already saturating the Linux kernel's ability to service system calls (although we're definitely hitting such a limit way earlier than I expected). A good way to test this theory would be to run more system-call-servicers (read: kernels!) at once on this machine; one way to do that is to use hardware virtualization with KVM. " + ] + } + ], + "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": "3.11.5" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/benchmark/benchmark.py b/benchmark/benchmark.py new file mode 100644 index 00000000..85dc7fd3 --- /dev/null +++ b/benchmark/benchmark.py @@ -0,0 +1,281 @@ +#!/usr/bin/env python3 +# Part of the aflplusplus project, requires Python 3.8+. +# Author: Chris Ball <chris@printf.net>, ported from Marc "van Hauser" Heuse's "benchmark.sh". +import argparse, asyncio, json, multiprocessing, os, platform, re, shutil, sys +from dataclasses import asdict, dataclass +from decimal import Decimal +from enum import Enum, auto +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +blue = lambda text: f"\033[1;94m{text}\033[0m"; gray = lambda text: f"\033[1;90m{text}\033[0m" +green = lambda text: f"\033[0;32m{text}\033[0m"; red = lambda text: f"\033[0;31m{text}\033[0m" +yellow = lambda text: f"\033[0;33m{text}\033[0m" + +class Mode(Enum): + multicore = auto() + singlecore = auto() + +@dataclass +class Target: + source: Path + binary: Path + +@dataclass +class Run: + execs_per_sec: float + execs_total: float + fuzzers_used: int + +@dataclass +class Config: + afl_persistent_config: bool + afl_system_config: bool + afl_version: Optional[str] + comment: str + compiler: str + target_arch: str + +@dataclass +class Hardware: + cpu_fastest_core_mhz: float + cpu_model: str + cpu_threads: int + +@dataclass +class Results: + config: Optional[Config] + hardware: Optional[Hardware] + targets: Dict[str, Dict[str, Optional[Run]]] + +all_modes = [Mode.singlecore, Mode.multicore] +all_targets = [ + Target(source=Path("../utils/persistent_mode/test-instr.c").resolve(), binary=Path("test-instr-persist-shmem")), + Target(source=Path("../test-instr.c").resolve(), binary=Path("test-instr")) +] +modes = [mode.name for mode in all_modes] +targets = [str(target.binary) for target in all_targets] +cpu_count = multiprocessing.cpu_count() +env_vars = { + "AFL_DISABLE_TRIM": "1", "AFL_I_DONT_CARE_ABOUT_MISSING_CRASHES": "1", "AFL_FAST_CAL": "1", + "AFL_NO_UI": "1", "AFL_TRY_AFFINITY": "1", "PATH": f'{str(Path("../").resolve())}:{os.environ["PATH"]}', +} + +parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) +parser.add_argument("-b", "--basedir", help="directory to use for temp files", type=str, default="/tmp/aflpp-benchmark") +parser.add_argument("-d", "--debug", help="show verbose debugging output", action="store_true") +parser.add_argument("-r", "--runs", help="how many runs to average results over", type=int, default=2) +parser.add_argument("-f", "--fuzzers", help="how many afl-fuzz workers to use", type=int, default=cpu_count) +parser.add_argument("-m", "--mode", help="pick modes", action="append", default=modes, choices=modes) +parser.add_argument("-c", "--comment", help="add a comment about your setup", type=str, default="") +parser.add_argument("--cpu", help="override the detected CPU model name", type=str, default="") +parser.add_argument("--mhz", help="override the detected CPU MHz", type=str, default="") +parser.add_argument( + "-t", "--target", help="pick targets", action="append", default=["test-instr-persist-shmem"], choices=targets +) +args = parser.parse_args() +# Really unsatisfying argparse behavior: we want a default and to allow multiple choices, but if there's a manual choice +# it should override the default. Seems like we have to remove the default to get that and have correct help text? +if len(args.target) > 1: + args.target = args.target[1:] +if len(args.mode) > 2: + args.mode = args.mode[2:] + +chosen_modes = [mode for mode in all_modes if mode.name in args.mode] +chosen_targets = [target for target in all_targets if str(target.binary) in args.target] +results = Results(config=None, hardware=None, targets={ + str(t.binary): {m.name: None for m in chosen_modes} for t in chosen_targets} +) +debug = lambda text: args.debug and print(blue(text)) + +async def clean_up_tempfiles() -> None: + shutil.rmtree(f"{args.basedir}/in") + for target in chosen_targets: + target.binary.unlink() + for mode in chosen_modes: + shutil.rmtree(f"{args.basedir}/out-{mode.name}-{str(target.binary)}") + +async def check_afl_persistent() -> bool: + with open("/proc/cmdline", "r") as cmdline: + return "mitigations=off" in cmdline.read().strip().split(" ") + +async def check_afl_system() -> bool: + sysctl = next((s for s in ["sysctl", "/sbin/sysctl"] if shutil.which(s)), None) + if sysctl: + (returncode, stdout, _) = await run_command([sysctl, "kernel.randomize_va_space"]) + return returncode == 0 and stdout.decode().rstrip().split(" = ")[1] == "0" + return False + +async def prep_env() -> None: + Path(f"{args.basedir}/in").mkdir(exist_ok=True, parents=True) + with open(f"{args.basedir}/in/in.txt", "wb") as seed: + seed.write(b"\x00" * 10240) + +async def compile_target(source: Path, binary: Path) -> None: + print(f" [*] Compiling the {binary} fuzzing harness for the benchmark to use.") + (returncode, stdout, stderr) = await run_command( + [str(Path("../afl-clang-lto").resolve()), "-o", str(Path(binary.resolve())), str(Path(source).resolve())] + ) + if returncode == 0: + return + print(yellow(f" [*] afl-clang-lto was unable to compile; falling back to afl-cc.")) + (returncode, stdout, stderr) = await run_command( + [str(Path("../afl-cc").resolve()), "-o", str(Path(binary.resolve())), str(Path(source).resolve())] + ) + if returncode != 0: + sys.exit(red(f" [*] Error: afl-cc is unable to compile: {stderr.decode()} {stdout.decode()}")) + +async def run_command(cmd: List[str]) -> Tuple[Optional[int], bytes, bytes]: + debug(f"Launching command: {cmd} with env {env_vars}") + p = await asyncio.create_subprocess_exec( + *cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE, env=env_vars + ) + stdout, stderr = await p.communicate() + debug(f"Output: {stdout.decode()} {stderr.decode()}") + return (p.returncode, stdout, stderr) + +async def check_deps() -> None: + if not (plat := platform.system()) == "Linux": sys.exit(red(f" [*] {plat} is not supported by this script yet.")) + if not os.access(Path("../afl-fuzz").resolve(), os.X_OK) and os.access(Path("../afl-cc").resolve(), os.X_OK) and ( + os.path.exists(Path("../SanitizerCoveragePCGUARD.so").resolve())): + sys.exit(red(" [*] Compile AFL++: we need afl-fuzz, afl-clang-fast and SanitizerCoveragePCGUARD.so built.")) + + (returncode, stdout, stderr) = await run_command([str(Path("../afl-cc").resolve()), "-v"]) + if returncode != 0: + sys.exit(red(f" [*] Error: afl-cc -v returned: {stderr.decode()} {stdout.decode()}")) + compiler = "" + target_arch = "" + for line in stderr.decode().split("\n"): + if "clang version" in line: + compiler = line + elif m := re.match(r"^Target: (.*)", line): + target_arch = m.group(1) + + # Pick some sample settings from afl-{persistent,system}-config to try to see whether they were run. + afl_pc = await check_afl_persistent() + afl_sc = await check_afl_system() + if not afl_pc: + print(yellow(f" [*] afl-persistent-config did not run; run it to improve performance (and decrease security).")) + if not afl_sc: + print(yellow(f" [*] afl-system-config did not run; run it to improve performance (and decrease security).")) + results.config = Config(afl_persistent_config=afl_pc, afl_system_config=afl_sc, afl_version="", + comment=args.comment, compiler=compiler, target_arch=target_arch) + +async def colon_values(filename: str, searchKey: str) -> List[str]: + """Return a colon-separated value given a key in a file, e.g. 'cpu MHz : 4976.109')""" + with open(filename, "r") as fh: + kv_pairs = (line.split(": ", 1) for line in fh if ": " in line) + v_list = [v.rstrip() for k, v in kv_pairs if k.rstrip() == searchKey] + return v_list + +async def describe_afl_config() -> str: + if results.config is None: + return "unknown" + elif results.config.afl_persistent_config and results.config.afl_system_config: + return "both" + elif results.config.afl_persistent_config: + return "persistent" + elif results.config.afl_system_config: + return "system" + else: + return "none" + +async def save_benchmark_results() -> None: + """Append a single row to the benchmark results in JSON Lines format (which is simple to write and diff).""" + with open("benchmark-results.jsonl", "a") as jsonfile: + json.dump(asdict(results), jsonfile, sort_keys=True) + jsonfile.write("\n") + print(blue(f" [*] Results have been written to the {jsonfile.name} file.")) + with open("COMPARISON", "r+") as comparisonfile: + described_config = await describe_afl_config() + aflconfig = described_config.ljust(12) + if results.hardware is None: + return + cpu_model = results.hardware.cpu_model.ljust(51) + if cpu_model in comparisonfile.read(): + print(blue(f" [*] Results have not been written to the COMPARISON file; this CPU is already present.")) + return + cpu_mhz = str(round(results.hardware.cpu_fastest_core_mhz)).ljust(5) + if not "test-instr-persist-shmem" in results.targets or \ + not "multicore" in results.targets["test-instr-persist-shmem"] or \ + not "singlecore" in results.targets["test-instr-persist-shmem"] or \ + results.targets["test-instr-persist-shmem"]["singlecore"] is None or \ + results.targets["test-instr-persist-shmem"]["multicore"] is None: + return + single = str(round(results.targets["test-instr-persist-shmem"]["singlecore"].execs_per_sec)).ljust(10) + multi = str(round(results.targets["test-instr-persist-shmem"]["multicore"].execs_per_sec)).ljust(9) + cores = str(args.fuzzers).ljust(7) + comparisonfile.write(f"{cpu_model} | {cpu_mhz} | {cores} | {single} | {multi} | {aflconfig} |\n") + print(blue(f" [*] Results have been written to the COMPARISON file.")) + with open("COMPARISON", "r") as comparisonfile: + print(comparisonfile.read()) + + +async def main() -> None: + try: + await clean_up_tempfiles() + except FileNotFoundError: + pass + await check_deps() + if args.mhz: + cpu_mhz = float(args.mhz) + else: + cpu_mhz_str = await colon_values("/proc/cpuinfo", "cpu MHz") + if len(cpu_mhz_str) == 0: + cpu_mhz_str.append("0") + cpu_mhz = max([float(c) for c in cpu_mhz_str]) # use the fastest CPU MHz for now + if args.cpu: + cpu_model = [args.cpu] + else: + cpu_model = await colon_values("/proc/cpuinfo", "model name") or [""] + results.hardware = Hardware(cpu_fastest_core_mhz=cpu_mhz, cpu_model=cpu_model[0], cpu_threads=cpu_count) + await prep_env() + print(f" [*] Ready, starting benchmark...") + for target in chosen_targets: + await compile_target(target.source, target.binary) + binary = str(target.binary) + for mode in chosen_modes: + if mode == Mode.multicore: + print(blue(f" [*] Using {args.fuzzers} fuzzers for multicore fuzzing "), end="") + print(blue("(use --fuzzers to override)." if args.fuzzers == cpu_count else f"(the default is {cpu_count})")) + execs_per_sec, execs_total = ([] for _ in range(2)) + for run_idx in range(0, args.runs): + print(gray(f" [*] {mode.name} {binary} run {run_idx+1} of {args.runs}, execs/s: "), end="", flush=True) + fuzzers = range(0, args.fuzzers if mode == Mode.multicore else 1) + outdir = f"{args.basedir}/out-{mode.name}-{binary}" + cmds = [] + for fuzzer_idx, afl in enumerate(fuzzers): + name = ["-o", outdir, "-M" if fuzzer_idx == 0 else "-S", str(afl)] + cmds.append(["afl-fuzz", "-i", f"{args.basedir}/in"] + name + ["-s", "123", "-V10", "-D", f"./{binary}"]) + # Prepare the afl-fuzz tasks, and then block while waiting for them to finish. + fuzztasks = [run_command(cmds[cpu]) for cpu in fuzzers] + await asyncio.gather(*fuzztasks) + afl_versions = await colon_values(f"{outdir}/0/fuzzer_stats", "afl_version") + if results.config: + results.config.afl_version = afl_versions[0] + # Our score is the sum of all execs_per_sec entries in fuzzer_stats files for the run. + sectasks = [colon_values(f"{outdir}/{afl}/fuzzer_stats", "execs_per_sec") for afl in fuzzers] + all_execs_per_sec = await asyncio.gather(*sectasks) + execs = sum([Decimal(count[0]) for count in all_execs_per_sec]) + print(green(execs)) + execs_per_sec.append(execs) + # Also gather execs_total and total_run_time for this run. + exectasks = [colon_values(f"{outdir}/{afl}/fuzzer_stats", "execs_done") for afl in fuzzers] + all_execs_total = await asyncio.gather(*exectasks) + execs_total.append(sum([Decimal(count[0]) for count in all_execs_total])) + + # (Using float() because Decimal() is not JSON-serializable.) + avg_afl_execs_per_sec = round(Decimal(sum(execs_per_sec) / len(execs_per_sec)), 2) + afl_execs_total = int(sum([Decimal(execs) for execs in execs_total])) + run = Run(execs_per_sec=float(avg_afl_execs_per_sec), execs_total=afl_execs_total, fuzzers_used=len(fuzzers)) + results.targets[binary][mode.name] = run + print(f" [*] Average execs/sec for this test across all runs was: {green(avg_afl_execs_per_sec)}") + if (((max(execs_per_sec) - min(execs_per_sec)) / avg_afl_execs_per_sec) * 100) > 15: + print(yellow(" [*] The difference between your slowest and fastest runs was >15%, maybe try again?")) + + await clean_up_tempfiles() + await save_benchmark_results() + +if __name__ == "__main__": + asyncio.run(main()) + |