From 49a1d81191aea5c7d068ad0051f39fc579ebfa63 Mon Sep 17 00:00:00 2001 From: Chris Ball Date: Mon, 2 Oct 2023 03:11:51 -0700 Subject: benchmark: cleanup, add results, add a data exploration notebook --- benchmark/benchmark.ipynb | 4490 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 4490 insertions(+) create mode 100644 benchmark/benchmark.ipynb (limited to 'benchmark/benchmark.ipynb') diff --git a/benchmark/benchmark.ipynb b/benchmark/benchmark.ipynb new file mode 100644 index 00000000..b1ef6b0f --- /dev/null +++ b/benchmark/benchmark.ipynb @@ -0,0 +1,4490 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 502, + "metadata": {}, + "outputs": [], + "source": [ + "# benchmark.ipynb\n", + "# Part of the aflplusplus project, requires an ipynb (Jupyter) editor or viewer.\n", + "# Author: Chris Ball \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": 503, + "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 15.0.7\",\n", + " \"target_arch\": \"x86_64-pc-linux-gnu\"\n", + " },\n", + " \"hardware\": {\n", + " \"cpu_fastest_core_mhz\": 4999.879,\n", + " \"cpu_model\": \"Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz\",\n", + " \"cpu_threads\": 16\n", + " },\n", + " \"targets\": {\n", + " \"test-instr\": {\n", + " \"multicore\": {\n", + " \"afl_execs_per_sec\": 11025.88,\n", + " \"afl_execs_total\": 519670,\n", + " \"fuzzers_used\": 1,\n", + " \"run_end\": \"2023-09-24 01:18:19.516294\",\n", + " \"run_start\": \"2023-09-24 01:17:55.982600\",\n", + " \"total_execs_per_sec\": 11019.3,\n", + " \"total_run_time\": 47.16\n", + " }\n", + " },\n", + " \"test-instr-persist-shmem\": {\n", + " \"multicore\": {\n", + " \"afl_execs_per_sec\": 134423.5,\n", + " \"afl_execs_total\": 519670,\n", + " \"fuzzers_used\": 1,\n", + " \"run_end\": \"2023-09-24 01:17:32.262373\",\n", + " \"run_start\": \"2023-09-24 01:17:30.328037\",\n", + " \"total_execs_per_sec\": 133591.26,\n", + " \"total_run_time\": 3.89\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": 504, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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config.afl_persistent_configconfig.afl_system_configconfig.afl_versionconfig.commentconfig.compilerconfig.target_archhardware.cpu_fastest_core_mhzhardware.cpu_modelhardware.cpu_threadstargets.test-instr.multicore.afl_execs_per_sec...targets.test-instr.singlecore.run_starttargets.test-instr.singlecore.total_execs_per_sectargets.test-instr.singlecore.total_run_timetargets.test-instr-persist-shmem.singlecore.afl_execs_per_sectargets.test-instr-persist-shmem.singlecore.afl_execs_totaltargets.test-instr-persist-shmem.singlecore.fuzzers_usedtargets.test-instr-persist-shmem.singlecore.run_endtargets.test-instr-persist-shmem.singlecore.run_starttargets.test-instr-persist-shmem.singlecore.total_execs_per_sectargets.test-instr-persist-shmem.singlecore.total_run_time
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execs_per_secparallel_fuzzersafl_persistent_configafl_system_configlabel
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" + ], + "text/plain": [ + " execs_per_sec parallel_fuzzers afl_persistent_config \\\n", + "108 135613.26 1.0 True \n", + "117 135613.26 10.0 True \n", + "\n", + " afl_system_config label \n", + "108 True Singlecore: Persistent mode/shared memory + ke... \n", + "117 True Singlecore: Persistent mode/shared memory + ke... " + ] + }, + "execution_count": 506, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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", + " if row[\"targets.test-instr-persist-shmem.multicore.total_execs_per_sec\"] > 0:\n", + " execs_per_sec = row[\"targets.test-instr-persist-shmem.multicore.total_execs_per_sec\"]\n", + " afl_execs_per_sec = row[\"targets.test-instr-persist-shmem.multicore.afl_execs_per_sec\"]\n", + " parallel_fuzzers = row[\"targets.test-instr-persist-shmem.multicore.fuzzers_used\"]\n", + " afl_persistent_config = row[\"config.afl_persistent_config\"]\n", + " afl_system_config = row[\"config.afl_system_config\"]\n", + " label = \"shmem-multicore\"\n", + " if afl_persistent_config:\n", + " label += \"+persist-conf\"\n", + " if afl_system_config:\n", + " label += \"+system-conf\"\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", + " graphdata.append({\"execs_per_sec\": afl_execs_per_sec, \"parallel_fuzzers\": parallel_fuzzers, \"afl_persistent_config\": afl_persistent_config, \"afl_system_config\": afl_system_config, \"label\": \"Multicore: afl_execs: 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 row[\"targets.test-instr.multicore.total_execs_per_sec\"] > 0:\n", + " execs_per_sec = row[\"targets.test-instr.multicore.total_execs_per_sec\"]\n", + " parallel_fuzzers = row[\"targets.test-instr.multicore.fuzzers_used\"]\n", + " afl_persistent_config = row[\"config.afl_persistent_config\"]\n", + " afl_system_config = row[\"config.afl_system_config\"]\n", + " label = \"base-multicore\"\n", + " if afl_persistent_config:\n", + " label += \"+persist-conf\"\n", + " if afl_system_config:\n", + " label += \"+system-conf\"\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 row[\"targets.test-instr-persist-shmem.singlecore.total_execs_per_sec\"] > 0:\n", + " execs_per_sec = row[\"targets.test-instr-persist-shmem.singlecore.total_execs_per_sec\"]\n", + " parallel_fuzzers = row[\"targets.test-instr-persist-shmem.singlecore.fuzzers_used\"]\n", + " afl_persistent_config = row[\"config.afl_persistent_config\"]\n", + " afl_system_config = row[\"config.afl_system_config\"]\n", + " label = \"shmem-singlecore\"\n", + " if afl_persistent_config:\n", + " label += \"+persist-conf\"\n", + " if afl_system_config:\n", + " label += \"+system-conf\"\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 row[\"targets.test-instr.singlecore.total_execs_per_sec\"] > 0:\n", + " execs_per_sec = row[\"targets.test-instr.singlecore.total_execs_per_sec\"]\n", + " parallel_fuzzers = row[\"targets.test-instr.singlecore.fuzzers_used\"]\n", + " afl_persistent_config = row[\"config.afl_persistent_config\"]\n", + " afl_system_config = row[\"config.afl_system_config\"]\n", + " label = \"base-singlecore\"\n", + " if afl_persistent_config:\n", + " label += \"+persist-conf\"\n", + " if afl_system_config:\n", + " label += \"+system-conf\"\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", + "\n", + " return pd.DataFrame.from_records(graphdata).sort_values(\"label\", ascending=False)\n", + "\n", + "graphdf = build_graphdf_from_query(i7)\n", + "graphdf.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 507, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "Configuration=Multicore: Non-persistent mode + kernel config
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labelMulticore: Non-persistent mode + kernel configMulticore: Persistent mode/shared memory + kernel configMulticore: Persistent mode/shared memory without kernel configMulticore: afl_execs: Persistent mode/shared memory + kernel configSinglecore: Non-persistent mode + kernel configSinglecore: Persistent mode/shared memory + kernel config
parallel_fuzzers
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" + ], + "text/plain": [ + "label Multicore: Non-persistent mode + kernel config \\\n", + "parallel_fuzzers \n", + "1.0 11019.30 \n", + "2.0 21111.92 \n", + "3.0 30568.82 \n", + "4.0 38963.07 \n", + "5.0 47693.65 \n", + "\n", + "label Multicore: Persistent mode/shared memory + kernel config \\\n", + "parallel_fuzzers \n", + "1.0 133591.26 \n", + "2.0 255995.07 \n", + "3.0 380246.34 \n", + "4.0 490254.72 \n", + "5.0 598698.16 \n", + "\n", + "label Multicore: Persistent mode/shared memory without kernel config \\\n", + "parallel_fuzzers \n", + "1.0 90851.40 \n", + "2.0 176159.32 \n", + "3.0 260268.78 \n", + "4.0 336355.99 \n", + "5.0 413750.00 \n", + "\n", + "label Multicore: afl_execs: Persistent mode/shared memory + kernel config \\\n", + "parallel_fuzzers \n", + "1.0 134423.50 \n", + "2.0 258490.04 \n", + "3.0 383777.45 \n", + "4.0 496249.48 \n", + "5.0 613089.31 \n", + "\n", + "label Singlecore: Non-persistent mode + kernel config \\\n", + "parallel_fuzzers \n", + "1.0 11038.96 \n", + "2.0 11038.96 \n", + "3.0 11038.96 \n", + "4.0 11038.96 \n", + "5.0 11038.96 \n", + "\n", + "label Singlecore: Persistent mode/shared memory + kernel config \n", + "parallel_fuzzers \n", + "1.0 135613.26 \n", + "2.0 135613.26 \n", + "3.0 135613.26 \n", + "4.0 135613.26 \n", + "5.0 135613.26 " + ] + }, + "execution_count": 508, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pivotdf.head()" + ] + }, + { + "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": 509, + "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 (11038 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 103765 execs per second, which is 9.4x that base speed.\n", + "\n", + "#### Persistent mode + shared memory\n", + "\n", + "By modifying the harness to use persistent mode as described [here](https://github.com/AFLplusplus/AFLplusplus/blob/stable/instrumentation/README.persistent_mode.md#4-persistent-mode),\n", + "we end up with 12.3x 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, it is better (at least on this machine) to use persistent mode on a single core, than to use non-persistent mode on every core.\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 `total_execs_per_sec` increase of 368476 execs -- the difference between\n", + "109.0x (mitigations off) and 75.6x (mitigations on) base speed. Turning on mitigations\n", + "reduced the overall performance by 31%!\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 115588 execs per sec, but the loss due to\n", + "mitigations is 368476 execs per sec, which is the averaged performance of 3.2 cores.\n", + "\n", + "#### afl_execs_per_sec vs. total_execs_per_sec\n", + "\n", + "* The purple line at the top is measuring `afl_execs_per_sec`. This is afl's own measurement of the speed of each fuzzer process, from the `out/fuzzer/fuzzer_stats` file.\n", + " * It peaks at 23 fuzzers running in parallel, on this 8-core (16-thread) CPU.\n", + " * In contrast, `total_execs_per_sec` shows large drops in performance as we pass 8 (cores) and 16 (threads) fuzzers.\n", + " * I'm inclined to trust `total_execs_per_sec` `(total_execs / (end time - start time))` more, so we'll use that from now on.\n", + "\n", + "#### How many parallel fuzzers should we use on this machine?\n", + "\n", + "* The drops in performance after 8/16 fuzzers are profound.\n", + " * Using 9-12 fuzzers is *worse* than using 8 fuzzers on this 8C/16T system, but using 13-16 is better than 8.\n", + " * And using >16 is worse than using 16. Makes sense.\n", + " * 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 21%\n", + " of performance. This could be a good tradeoff in terms of cost.\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 509, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# (Ignore 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_with_afl_max_execs = int(pivotdf[\"Multicore: afl_execs: Persistent mode/shared memory + kernel config\"].idxmax())\n", + "multicore_fuzzers_with_total_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_with_total_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", + "By modifying the harness to use persistent mode 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, it is better (at least on this machine) to use persistent mode on a single core, than to use non-persistent mode on every core.\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 `total_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", + "#### afl_execs_per_sec vs. total_execs_per_sec\n", + "\n", + "* The purple line at the top is measuring `afl_execs_per_sec`. This is afl's own measurement of the speed of each fuzzer process, from the `out/fuzzer/fuzzer_stats` file.\n", + " * It peaks at {multicore_fuzzers_with_afl_max_execs} fuzzers running in parallel, on this 8-core (16-thread) CPU.\n", + " * In contrast, `total_execs_per_sec` shows large drops in performance as we pass 8 (cores) and 16 (threads) fuzzers.\n", + " * I'm inclined to trust `total_execs_per_sec` `(total_execs / (end time - start time))` more, so we'll use that from now on.\n", + "\n", + "#### How many parallel fuzzers should we use on this machine?\n", + "\n", + "* The drops in performance after 8/16 fuzzers are profound.\n", + " * Using 9-12 fuzzers is *worse* than using 8 fuzzers on this 8C/16T system, but using 13-16 is better than 8.\n", + " * And using >16 is worse than using 16. Makes sense.\n", + " * 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, and the answer calls the conclusion we just made above into question:" + ] + }, + { + "cell_type": "code", + "execution_count": 521, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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149FalseTrue++4.09aAWS EC2 r6a.48xlarge spot instanceclang version 15.0.7 (Amazon Linux 15.0.7-3.am...x86_64-amazon-linux-gnu3599.425AMD EPYC 7R13 Processor192171655.961039340.02.02023-09-30 07:42:06.8534362023-09-30 07:42:03.776562168998.376.15
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execs_per_secparallel_fuzzersafl_persistent_configafl_system_configlabel
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" + ] + }, + "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, 5)))\n", + "r6a_fig.update_layout(width=1200, height=400)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Line graph analysis\n", + "\n", + "This is a shocking result for a 192 vCPU machine -- whether you count `afl_execs` or `total_execs`, our optimal number of parallel fuzzers was 16!\n", + "\n", + "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 -- the most likely cause here (based on a tip from @eqv) is that we're actually saturating the Linux kernel's ability to service system calls. We could look to reduce these, but there's another option available to us, which is to try running more system-call-servicers (read: kernels) at once, on this machine. One way to do that is to use hardware virtualization with KVM,\n", + "and if it is true that this particular fuzzer setup bottlenecks around 16 fuzzers, then we might expect an optimal number of KVM\n", + "kernels running on this machine to be around 16/192 == 8, each with 16 fuzzers in parallel, and perhaps a shared (in-memory?)\n", + "filesystem for the fuzzing queue." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Measuring system call saturation and experimenting with extra KVM hosts\n", + "\n", + "Coming soon!" + ] + } + ], + "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": "3.11.3" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} -- cgit 1.4.1 From b9db6b1254c9bf3a47c171bb96468628e9bd00f2 Mon Sep 17 00:00:00 2001 From: Chris Ball Date: Mon, 2 Oct 2023 03:23:09 -0700 Subject: benchmark: add a README, lower default runs from 5 to 3 --- benchmark/README.md | 46 + benchmark/benchmark.ipynb | 3502 +-------------------------------------------- benchmark/benchmark.py | 2 +- 3 files changed, 110 insertions(+), 3440 deletions(-) create mode 100644 benchmark/README.md (limited to 'benchmark/benchmark.ipynb') diff --git a/benchmark/README.md b/benchmark/README.md new file mode 100644 index 00000000..66f7f59e --- /dev/null +++ b/benchmark/README.md @@ -0,0 +1,46 @@ +# 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: + +``` +cd aflplusplus/benchmark +python3 benchmark.py + [*] Using 16 fuzzers for multicore fuzzing (use --fuzzers to override) + [*] Ready, starting benchmark... + [*] Compiling the test-instr-persist-shmem fuzzing harness for the benchmark to use. + [*] multicore test-instr-persist-shmem run 1 of 3, execs/s: 846065.81 + [*] multicore test-instr-persist-shmem run 2 of 3, execs/s: 849694.03 + [*] multicore test-instr-persist-shmem run 3 of 3, execs/s: 850757.52 + [*] Average AFL execs/sec for this test across all runs was: 848839.12 + [*] Average total execs/sec for this test across all runs was: 833138.28 + [*] Results have been written to benchmark-results.jsonl +``` + +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`). + +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.ipynb b/benchmark/benchmark.ipynb index b1ef6b0f..7ff90fcd 100644 --- a/benchmark/benchmark.ipynb +++ b/benchmark/benchmark.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 502, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -20,14 +20,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Translate the JSON Lines entries into a single pandas DataFrame.\n", + "### 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": 503, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -89,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 504, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -371,7 +371,7 @@ "[5 rows x 37 columns]" ] }, - "execution_count": 504, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -404,7 +404,7 @@ }, { "cell_type": "code", - "execution_count": 505, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -418,1647 +418,65 @@ }, { "cell_type": "code", - "execution_count": 506, + "execution_count": 16, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ] - }, - "execution_count": 506, - "metadata": {}, - "output_type": "execute_result" - } - ], + "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", - " if row[\"targets.test-instr-persist-shmem.multicore.total_execs_per_sec\"] > 0:\n", - " execs_per_sec = row[\"targets.test-instr-persist-shmem.multicore.total_execs_per_sec\"]\n", - " afl_execs_per_sec = row[\"targets.test-instr-persist-shmem.multicore.afl_execs_per_sec\"]\n", - " parallel_fuzzers = row[\"targets.test-instr-persist-shmem.multicore.fuzzers_used\"]\n", - " afl_persistent_config = row[\"config.afl_persistent_config\"]\n", - " afl_system_config = row[\"config.afl_system_config\"]\n", - " label = \"shmem-multicore\"\n", - " if afl_persistent_config:\n", - " label += \"+persist-conf\"\n", - " if afl_system_config:\n", - " label += \"+system-conf\"\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", - " graphdata.append({\"execs_per_sec\": afl_execs_per_sec, \"parallel_fuzzers\": parallel_fuzzers, \"afl_persistent_config\": afl_persistent_config, \"afl_system_config\": afl_system_config, \"label\": \"Multicore: afl_execs: 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 row[\"targets.test-instr.multicore.total_execs_per_sec\"] > 0:\n", - " execs_per_sec = row[\"targets.test-instr.multicore.total_execs_per_sec\"]\n", - " parallel_fuzzers = row[\"targets.test-instr.multicore.fuzzers_used\"]\n", - " afl_persistent_config = row[\"config.afl_persistent_config\"]\n", - " afl_system_config = row[\"config.afl_system_config\"]\n", - " label = \"base-multicore\"\n", - " if afl_persistent_config:\n", - " label += \"+persist-conf\"\n", - " if afl_system_config:\n", - " label += \"+system-conf\"\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}.total_execs_per_sec\"] > 0:\n", + " continue\n", + " execs_per_sec = row[f\"targets.{target}.{mode}.total_execs_per_sec\"]\n", + " afl_execs_per_sec = row[f\"targets.{target}.{mode}.afl_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", + " graphdata.append({\"execs_per_sec\": afl_execs_per_sec, \"parallel_fuzzers\": parallel_fuzzers, \"afl_persistent_config\": afl_persistent_config, \"afl_system_config\": afl_system_config, \"label\": \"Multicore: afl_execs: 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 row[\"targets.test-instr-persist-shmem.singlecore.total_execs_per_sec\"] > 0:\n", - " execs_per_sec = row[\"targets.test-instr-persist-shmem.singlecore.total_execs_per_sec\"]\n", - " parallel_fuzzers = row[\"targets.test-instr-persist-shmem.singlecore.fuzzers_used\"]\n", - " afl_persistent_config = row[\"config.afl_persistent_config\"]\n", - " afl_system_config = row[\"config.afl_system_config\"]\n", - " label = \"shmem-singlecore\"\n", - " if afl_persistent_config:\n", - " label += \"+persist-conf\"\n", - " if afl_system_config:\n", - " label += \"+system-conf\"\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 row[\"targets.test-instr.singlecore.total_execs_per_sec\"] > 0:\n", - " execs_per_sec = row[\"targets.test-instr.singlecore.total_execs_per_sec\"]\n", - " parallel_fuzzers = row[\"targets.test-instr.singlecore.fuzzers_used\"]\n", - " afl_persistent_config = row[\"config.afl_persistent_config\"]\n", - " afl_system_config = row[\"config.afl_system_config\"]\n", - " label = \"base-singlecore\"\n", - " if afl_persistent_config:\n", - " label += \"+persist-conf\"\n", - " if afl_system_config:\n", - " label += \"+system-conf\"\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", - "\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", - 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"zerolinewidth": 2 - } - } - }, - "title": { - "text": "Fuzzer performance" - }, - "width": 1200, - "xaxis": { - "anchor": "y", - "domain": [ - 0, - 1 - ], - "tickvals": [ - 0, - 5, - 10, - 15, - 20, - 25, - 30, - 35, - 40, - 45, - 50, - 55, - 60, - 65, - 70, - 75, - 80, - 85, - 90, - 95, - 100, - 105, - 110, - 115, - 120, - 125, - 130, - 135, - 140, - 145, - 150, - 155, - 160, - 165, - 170, - 175, - 180, - 185, - 190, - 195, - 200 - ], - "title": { - "text": "Number of parallel fuzzers" - } - }, - "yaxis": { - "anchor": "x", - "domain": [ - 0, - 1 - ], - "ticktext": [ - "10x", - "36x", - "62x", - "89x", - "115x", - "141x" - ], - "tickvals": [ - 105839.1, - 395932.13, - 686025.1599999999, - 976118.1899999998, - 1266211.22, - 1556304.25 - ], - "title": { - "text": "Fuzz target executions per second" - } - } - } - }, - "text/html": [ - "
" + "image/svg+xml": [ + "510152025303540455055606570758085909510010511011512012513013514014515015516016517017518018519019520010x36x62x89x115x141xConfigurationMulticore: Persistent mode/shared memory + kernel configMulticore: afl_execs: Persistent mode/shared memory + kernel configFuzzer performanceNumber of parallel fuzzersFuzz target executions per second" ] }, "metadata": {}, @@ -4436,7 +1071,8 @@ "# 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, 5)))\n", - "r6a_fig.update_layout(width=1200, height=400)" + "r6a_fig.update_layout(width=1200, height=400)\n", + "r6a_fig.show(\"svg\")" ] }, { @@ -4449,19 +1085,7 @@ "\n", "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 -- the most likely cause here (based on a tip from @eqv) is that we're actually saturating the Linux kernel's ability to service system calls. We could look to reduce these, but there's another option available to us, which is to try running more system-call-servicers (read: kernels) at once, on this machine. One way to do that is to use hardware virtualization with KVM,\n", - "and if it is true that this particular fuzzer setup bottlenecks around 16 fuzzers, then we might expect an optimal number of KVM\n", - "kernels running on this machine to be around 16/192 == 8, each with 16 fuzzers in parallel, and perhaps a shared (in-memory?)\n", - "filesystem for the fuzzing queue." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Measuring system call saturation and experimenting with extra KVM hosts\n", - "\n", - "Coming soon!" + "No, probably not -- the most likely causes here are a problem with our Python harness, or potentially 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 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. " ] } ], diff --git a/benchmark/benchmark.py b/benchmark/benchmark.py index da32167a..e0dea299 100644 --- a/benchmark/benchmark.py +++ b/benchmark/benchmark.py @@ -64,7 +64,7 @@ cpu_count = multiprocessing.cpu_count() 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=5) +parser.add_argument("-r", "--runs", help="how many runs to average results over", type=int, default=3) 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=["multicore"], choices=mode_names) parser.add_argument("-c", "--comment", help="add a comment about your setup", type=str, default="") -- cgit 1.4.1 From 3bfd194d469c04da7c74a3964bf31ef40605a178 Mon Sep 17 00:00:00 2001 From: Chris Ball Date: Mon, 2 Oct 2023 04:33:16 -0700 Subject: benchmark: notebook wording tweaks --- benchmark/benchmark.ipynb | 87 ++++++++++++++++++++++++++--------------------- 1 file changed, 49 insertions(+), 38 deletions(-) (limited to 'benchmark/benchmark.ipynb') diff --git a/benchmark/benchmark.ipynb b/benchmark/benchmark.ipynb index 7ff90fcd..e4c29f2f 100644 --- a/benchmark/benchmark.ipynb +++ b/benchmark/benchmark.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 12, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -89,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -371,7 +371,7 @@ "[5 rows x 37 columns]" ] }, - "execution_count": 14, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -388,23 +388,14 @@ "source": [ "### Graph prep\n", "\n", - "We're looking for a line graph showing lines for the following cases:\n", - "\n", - "* For each mode:\n", - " * For each target:\n", - " * persistent off, system off\n", - " * persistent on, system off\n", - " * persistent off, system on\n", - " * persistent on, system on\n", - "\n", - "where the x-axis is number of cores, and the y-axis is either afl_execs_per_sec or total_execs_per_sec (I'm not yet sure which is a better metric to use).\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 either afl_execs_per_sec or total_execs_per_sec (I'm not yet sure which is a better metric to use).\n", "\n", "But first, a mini test harness by checking that the number of rows matched what we'd intuitively expect:" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -418,7 +409,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -470,13 +461,13 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ - "1234567891011121314151617181920212223242526272829303132333435361x26x51x75x100x125xConfigurationMulticore: Non-persistent mode + kernel configMulticore: Persistent mode/shared memory + kernel configMulticore: Persistent mode/shared memory without kernel configMulticore: afl_execs: Persistent mode/shared memory + kernel configSinglecore: Non-persistent mode + kernel configSinglecore: Persistent mode/shared memory + kernel configFuzzer performanceNumber of parallel fuzzersFuzz target executions per second" + "1234567891011121314151617181920212223242526272829303132333435361x26x51x75x100x125xConfigurationMulticore: Non-persistent mode + kernel configMulticore: Persistent mode/shared memory + kernel configMulticore: Persistent mode/shared memory without kernel configMulticore: afl_execs: Persistent mode/shared memory + kernel configSinglecore: Non-persistent mode + kernel configSinglecore: Persistent mode/shared memory + kernel configFuzzer performanceNumber of parallel fuzzersFuzz target executions per second" ] }, "metadata": {}, @@ -518,7 +509,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -659,7 +650,7 @@ "5.0 135613.26 " ] }, - "execution_count": 18, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -677,7 +668,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -690,18 +681,25 @@ "#### test-instr vs. test-instr-persist-shmem\n", "\n", "This graph is scaled so that the single-core, non-persistent-mode performance (11038 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", + "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 103765 execs per second, which is 9.4x that base speed.\n", + "By running as many parallel fuzzers are there are CPU threads, we can reach 103765 execs per second, which is **9.4x** that base speed.\n", "\n", "#### Persistent mode + shared memory\n", "\n", - "By modifying the harness to use persistent mode as described [here](https://github.com/AFLplusplus/AFLplusplus/blob/stable/instrumentation/README.persistent_mode.md#4-persistent-mode),\n", - "we end up with 12.3x 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, it is better (at least on this machine) to use persistent mode on a single core, than to use non-persistent mode on every core.\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 **12.3x** 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", + "**75.6x** base speed.\n", "\n", "#### Kernel config\n", "\n", @@ -716,6 +714,9 @@ "Or if we want to think in terms of cores, then the average number of execs gained per core in the initial eight is 115588 execs per sec, but the loss due to\n", "mitigations is 368476 execs per sec, which is the averaged performance of 3.2 cores.\n", "\n", + "With kernel mitigations turned off, we reach our highest available total_execs_per_sec speed on this machine, which is **109.0x** higher\n", + "than where we started from.\n", + "\n", "#### afl_execs_per_sec vs. total_execs_per_sec\n", "\n", "* The purple line at the top is measuring `afl_execs_per_sec`. This is afl's own measurement of the speed of each fuzzer process, from the `out/fuzzer/fuzzer_stats` file.\n", @@ -736,7 +737,7 @@ "" ] }, - "execution_count": 19, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -765,18 +766,25 @@ "#### 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", + "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", + "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", - "By modifying the harness to use persistent mode 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, it is better (at least on this machine) to use persistent mode on a single core, than to use non-persistent mode on every core.\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", @@ -791,6 +799,9 @@ "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 total_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", "#### afl_execs_per_sec vs. total_execs_per_sec\n", "\n", "* The purple line at the top is measuring `afl_execs_per_sec`. This is afl's own measurement of the speed of each fuzzer process, from the `out/fuzzer/fuzzer_stats` file.\n", @@ -820,7 +831,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -955,7 +966,7 @@ "149 6.15 " ] }, - "execution_count": 20, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -967,7 +978,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -1029,7 +1040,7 @@ "153 True Multicore: afl_execs: Persistent mode/shared m... " ] }, - "execution_count": 21, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -1041,13 +1052,13 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ - "510152025303540455055606570758085909510010511011512012513013514014515015516016517017518018519019520010x36x62x89x115x141xConfigurationMulticore: Persistent mode/shared memory + kernel configMulticore: afl_execs: Persistent mode/shared memory + kernel configFuzzer performanceNumber of parallel fuzzersFuzz target executions per second" + "510152025303540455055606570758085909510010511011512012513013514014515015516016517017518018519019520010x36x62x89x115x141xConfigurationMulticore: Persistent mode/shared memory + kernel configMulticore: afl_execs: Persistent mode/shared memory + kernel configFuzzer performanceNumber of parallel fuzzersFuzz target executions per second" ] }, "metadata": {}, -- cgit 1.4.1 From d34bed5dbf024788251f519674519937b1f9009a Mon Sep 17 00:00:00 2001 From: Chris Ball Date: Sun, 19 Nov 2023 14:58:43 -0800 Subject: benchmark: Update Jupyter notebook and results file. --- benchmark/benchmark-results.jsonl | 965 +++++++++++++++-------------------- benchmark/benchmark.ipynb | 1020 ++++++++++++++++++++++++------------- 2 files changed, 1085 insertions(+), 900 deletions(-) (limited to 'benchmark/benchmark.ipynb') diff --git a/benchmark/benchmark-results.jsonl b/benchmark/benchmark-results.jsonl index 1d52402d..f3dd60be 100644 --- a/benchmark/benchmark-results.jsonl +++ b/benchmark/benchmark-results.jsonl @@ -1,550 +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 15.0.7", "target_arch": "x86_64-pc-linux-gnu"}, "hardware": {"cpu_fastest_core_mhz": 4999.879, "cpu_model": "Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz", "cpu_threads": 16}, 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"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": 3598.57, "cpu_model": "AMD EPYC 7R13 Processor", "cpu_threads": 192}, "targets": {"test-instr-persist-shmem": {"multicore": {"execs_per_sec": 465528.62, "execs_total": 4658439, "fuzzers_used": 186}}}} +{"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": 3587.126, "cpu_model": "AMD EPYC 7R13 Processor", "cpu_threads": 192}, "targets": {"test-instr-persist-shmem": {"multicore": {"execs_per_sec": 476194.69, "execs_total": 4765385, "fuzzers_used": 187}}}} +{"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": 3423.033, "cpu_model": "AMD EPYC 7R13 Processor", "cpu_threads": 192}, "targets": {"test-instr-persist-shmem": {"multicore": {"execs_per_sec": 475886.86, "execs_total": 4762069, "fuzzers_used": 188}}}} +{"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": 3599.32, "cpu_model": "AMD EPYC 7R13 Processor", "cpu_threads": 192}, "targets": {"test-instr-persist-shmem": {"multicore": {"execs_per_sec": 473599.91, "execs_total": 4739128, "fuzzers_used": 189}}}} +{"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": 3597.599, "cpu_model": "AMD EPYC 7R13 Processor", "cpu_threads": 192}, "targets": {"test-instr-persist-shmem": {"multicore": {"execs_per_sec": 476949.52, "execs_total": 4772500, "fuzzers_used": 190}}}} +{"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": 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 index e4c29f2f..aea2e0f1 100644 --- a/benchmark/benchmark.ipynb +++ b/benchmark/benchmark.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 40, + "execution_count": 142, "metadata": {}, "outputs": [], "source": [ @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 143, "metadata": {}, "outputs": [ { @@ -40,35 +40,20 @@ " \"afl_system_config\": true,\n", " \"afl_version\": \"++4.09a\",\n", " \"comment\": \"i9-9900k, 16GB DDR4-3000, Arch Linux\",\n", - " \"compiler\": \"clang version 15.0.7\",\n", + " \"compiler\": \"clang version 16.0.6\",\n", " \"target_arch\": \"x86_64-pc-linux-gnu\"\n", " },\n", " \"hardware\": {\n", - " \"cpu_fastest_core_mhz\": 4999.879,\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", - " \"multicore\": {\n", - " \"afl_execs_per_sec\": 11025.88,\n", - " \"afl_execs_total\": 519670,\n", - " \"fuzzers_used\": 1,\n", - " \"run_end\": \"2023-09-24 01:18:19.516294\",\n", - " \"run_start\": \"2023-09-24 01:17:55.982600\",\n", - " \"total_execs_per_sec\": 11019.3,\n", - " \"total_run_time\": 47.16\n", - " }\n", - " },\n", - " \"test-instr-persist-shmem\": {\n", - " \"multicore\": {\n", - " \"afl_execs_per_sec\": 134423.5,\n", - " \"afl_execs_total\": 519670,\n", - " \"fuzzers_used\": 1,\n", - " \"run_end\": \"2023-09-24 01:17:32.262373\",\n", - " \"run_start\": \"2023-09-24 01:17:30.328037\",\n", - " \"total_execs_per_sec\": 133591.26,\n", - " \"total_run_time\": 3.89\n", + " \"singlecore\": {\n", + " \"execs_per_sec\": 9845.64,\n", + " \"execs_total\": 98545,\n", + " \"fuzzers_used\": 1\n", " }\n", " }\n", " }\n", @@ -89,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 144, "metadata": {}, "outputs": [ { @@ -122,18 +107,18 @@ " hardware.cpu_fastest_core_mhz\n", " hardware.cpu_model\n", " hardware.cpu_threads\n", - " targets.test-instr.multicore.afl_execs_per_sec\n", + " targets.test-instr.singlecore.execs_per_sec\n", " ...\n", - " targets.test-instr.singlecore.run_start\n", - " targets.test-instr.singlecore.total_execs_per_sec\n", - " targets.test-instr.singlecore.total_run_time\n", - " targets.test-instr-persist-shmem.singlecore.afl_execs_per_sec\n", - " targets.test-instr-persist-shmem.singlecore.afl_execs_total\n", + " targets.test-instr.singlecore.fuzzers_used\n", + " targets.test-instr-persist-shmem.singlecore.execs_per_sec\n", + " targets.test-instr-persist-shmem.singlecore.execs_total\n", " targets.test-instr-persist-shmem.singlecore.fuzzers_used\n", - " targets.test-instr-persist-shmem.singlecore.run_end\n", - " targets.test-instr-persist-shmem.singlecore.run_start\n", - " targets.test-instr-persist-shmem.singlecore.total_execs_per_sec\n", - " targets.test-instr-persist-shmem.singlecore.total_run_time\n", + " targets.test-instr-persist-shmem.multicore.execs_per_sec\n", + " targets.test-instr-persist-shmem.multicore.execs_total\n", + " targets.test-instr-persist-shmem.multicore.fuzzers_used\n", + " targets.test-instr.multicore.execs_per_sec\n", + " targets.test-instr.multicore.execs_total\n", + " targets.test-instr.multicore.fuzzers_used\n", " \n", " \n", " \n", @@ -143,14 +128,14 @@ " True\n", " ++4.09a\n", " i9-9900k, 16GB DDR4-3000, Arch Linux\n", - " clang version 15.0.7\n", + " clang version 16.0.6\n", " x86_64-pc-linux-gnu\n", - " 4999.879\n", + " 4788.770\n", " Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz\n", " 16\n", - " 11025.88\n", + " 9845.64\n", " ...\n", - " NaN\n", + " 1.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -167,17 +152,17 @@ " True\n", " ++4.09a\n", " i9-9900k, 16GB DDR4-3000, Arch Linux\n", - " clang version 15.0.7\n", + " clang version 16.0.6\n", " x86_64-pc-linux-gnu\n", - " 4998.794\n", + " 4989.281\n", " Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz\n", " 16\n", - " 21139.64\n", - " ...\n", - " NaN\n", - " NaN\n", " NaN\n", + " ...\n", " NaN\n", + " 125682.73\n", + " 1257330.0\n", + " 1.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -191,20 +176,20 @@ " True\n", " ++4.09a\n", " i9-9900k, 16GB DDR4-3000, Arch Linux\n", - " clang version 15.0.7\n", + " clang version 16.0.6\n", " x86_64-pc-linux-gnu\n", - " 4998.859\n", + " 4799.415\n", " Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz\n", " 16\n", - " 30618.28\n", - " ...\n", - " NaN\n", - " NaN\n", " NaN\n", + " ...\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 120293.77\n", + " 1203058.0\n", + " 1.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -215,20 +200,20 @@ " True\n", " ++4.09a\n", " i9-9900k, 16GB DDR4-3000, Arch Linux\n", - " clang version 15.0.7\n", + " clang version 16.0.6\n", " x86_64-pc-linux-gnu\n", - " 5000.078\n", + " 4703.293\n", " Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz\n", " 16\n", - " 39125.92\n", - " ...\n", - " NaN\n", - " NaN\n", " NaN\n", + " ...\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 231429.96\n", + " 2314531.0\n", + " 2.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -239,27 +224,27 @@ " True\n", " ++4.09a\n", " i9-9900k, 16GB DDR4-3000, Arch Linux\n", - " clang version 15.0.7\n", + " clang version 16.0.6\n", " x86_64-pc-linux-gnu\n", - " 4996.885\n", + " 4800.375\n", " Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz\n", " 16\n", - " 47861.04\n", - " ...\n", - " NaN\n", - " NaN\n", " NaN\n", + " ...\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 346759.33\n", + " 3468290.0\n", + " 3.0\n", " NaN\n", " NaN\n", " NaN\n", " \n", " \n", "\n", - "

5 rows × 37 columns

\n", + "

5 rows × 21 columns

\n", "" ], "text/plain": [ @@ -271,18 +256,18 @@ "4 True True ++4.09a \n", "\n", " config.comment config.compiler \\\n", - "0 i9-9900k, 16GB DDR4-3000, Arch Linux clang version 15.0.7 \n", - "1 i9-9900k, 16GB DDR4-3000, Arch Linux clang version 15.0.7 \n", - "2 i9-9900k, 16GB DDR4-3000, Arch Linux clang version 15.0.7 \n", - "3 i9-9900k, 16GB DDR4-3000, Arch Linux clang version 15.0.7 \n", - "4 i9-9900k, 16GB DDR4-3000, Arch Linux clang version 15.0.7 \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 4999.879 \n", - "1 x86_64-pc-linux-gnu 4998.794 \n", - "2 x86_64-pc-linux-gnu 4998.859 \n", - "3 x86_64-pc-linux-gnu 5000.078 \n", - "4 x86_64-pc-linux-gnu 4996.885 \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", @@ -291,87 +276,87 @@ "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.multicore.afl_execs_per_sec ... \\\n", - "0 11025.88 ... \n", - "1 21139.64 ... \n", - "2 30618.28 ... \n", - "3 39125.92 ... \n", - "4 47861.04 ... \n", - "\n", - " targets.test-instr.singlecore.run_start \\\n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \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.total_execs_per_sec \\\n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \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.singlecore.total_run_time \\\n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \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.afl_execs_per_sec \\\n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \n", - "\n", - " targets.test-instr-persist-shmem.singlecore.afl_execs_total \\\n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \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 NaN \n", + "1 1.0 \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "\n", - " targets.test-instr-persist-shmem.singlecore.run_end \\\n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \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.singlecore.run_start \\\n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \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.singlecore.total_execs_per_sec \\\n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \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-persist-shmem.singlecore.total_run_time \n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \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", - "[5 rows x 37 columns]" + " 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": 42, + "execution_count": 144, "metadata": {}, "output_type": "execute_result" } @@ -388,28 +373,24 @@ "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 either afl_execs_per_sec or total_execs_per_sec (I'm not yet sure which is a better metric to use).\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", - "But first, a mini test harness by checking that the number of rows matched what we'd intuitively expect:" + "First, a quick check that the number of rows matched what we'd intuitively expect:" ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 145, "metadata": {}, "outputs": [], "source": [ "i7 = df.query(\"`config.comment` == 'i9-9900k, 16GB DDR4-3000, Arch Linux'\")\n", - "assert len(i7) == 148\n", - "multicore = i7.query(\"`targets.test-instr-persist-shmem.multicore.total_execs_per_sec` > 0 or `targets.test-instr.multicore.total_execs_per_sec` > 0\")\n", - "assert len(multicore) == 144 # 36 cores * 4 states * 1 run (containing two targets)\n", - "singlecore = i7.query(\"`targets.test-instr-persist-shmem.singlecore.total_execs_per_sec` > 0 or `targets.test-instr.singlecore.total_execs_per_sec` > 0\")\n", - "assert len(singlecore) == 4 # 1 core * 4 states * 1 run (containing two targets)" + "assert len(i7) == 185" ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 146, "metadata": {}, "outputs": [], "source": [ @@ -421,10 +402,9 @@ " 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}.total_execs_per_sec\"] > 0:\n", + " if not row[f\"targets.{target}.{mode}.execs_per_sec\"] > 0:\n", " continue\n", - " execs_per_sec = row[f\"targets.{target}.{mode}.total_execs_per_sec\"]\n", - " afl_execs_per_sec = row[f\"targets.{target}.{mode}.afl_execs_per_sec\"]\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", @@ -443,7 +423,6 @@ " \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", - " graphdata.append({\"execs_per_sec\": afl_execs_per_sec, \"parallel_fuzzers\": parallel_fuzzers, \"afl_persistent_config\": afl_persistent_config, \"afl_system_config\": afl_system_config, \"label\": \"Multicore: afl_execs: 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", @@ -461,13 +440,13 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 147, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ - "1234567891011121314151617181920212223242526272829303132333435361x26x51x75x100x125xConfigurationMulticore: Non-persistent mode + kernel configMulticore: Persistent mode/shared memory + kernel configMulticore: Persistent mode/shared memory without kernel configMulticore: afl_execs: Persistent mode/shared memory + kernel configSinglecore: Non-persistent mode + kernel configSinglecore: Persistent mode/shared memory + kernel configFuzzer performanceNumber of parallel fuzzersFuzz target executions per second" + "1234567891011121314151617181920212223242526272829303132333435361x25x48x72x95x119xConfigurationMulticore: Non-persistent mode + kernel configMulticore: Persistent mode/shared memory + kernel configMulticore: Persistent mode/shared memory without kernel configSinglecore: Non-persistent mode + kernel configSinglecore: Persistent mode/shared memory + kernel configFuzzer performanceNumber of parallel fuzzersFuzz target executions per second" ] }, "metadata": {}, @@ -509,7 +488,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 148, "metadata": {}, "outputs": [ { @@ -536,7 +515,6 @@ " Multicore: Non-persistent mode + kernel config\n", " Multicore: Persistent mode/shared memory + kernel config\n", " Multicore: Persistent mode/shared memory without kernel config\n", - " Multicore: afl_execs: Persistent mode/shared memory + kernel config\n", " Singlecore: Non-persistent mode + kernel config\n", " Singlecore: Persistent mode/shared memory + kernel config\n", " \n", @@ -547,54 +525,296 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", " 1.0\n", - " 11019.30\n", - " 133591.26\n", - " 90851.40\n", - " 134423.50\n", - " 11038.96\n", - " 135613.26\n", + " 10714.79\n", + " 120293.77\n", + " 90641.62\n", + " 9845.64\n", + " 125682.73\n", " \n", " \n", " 2.0\n", - " 21111.92\n", - " 255995.07\n", - " 176159.32\n", - " 258490.04\n", - " 11038.96\n", - " 135613.26\n", + " 20493.07\n", + " 231429.96\n", + " 178184.19\n", + " 9845.64\n", + " 125682.73\n", " \n", " \n", " 3.0\n", - " 30568.82\n", - " 380246.34\n", - " 260268.78\n", - " 383777.45\n", - " 11038.96\n", - " 135613.26\n", + " 29660.06\n", + " 346759.33\n", + " 262652.86\n", + " 9845.64\n", + " 125682.73\n", " \n", " \n", " 4.0\n", - " 38963.07\n", - " 490254.72\n", - " 336355.99\n", - " 496249.48\n", - " 11038.96\n", - " 135613.26\n", + " 37875.57\n", + " 455340.06\n", + " 339119.32\n", + " 9845.64\n", + " 125682.73\n", " \n", " \n", " 5.0\n", - " 47693.65\n", - " 598698.16\n", - " 413750.00\n", - " 613089.31\n", - " 11038.96\n", - " 135613.26\n", + " 46326.75\n", + " 568405.15\n", + " 420239.94\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 6.0\n", + " 54595.48\n", + " 678030.96\n", + " 498062.02\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 7.0\n", + " 62720.98\n", + " 782585.04\n", + " 578495.44\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 8.0\n", + " 70777.99\n", + " 893618.35\n", + " 661836.22\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 9.0\n", + " 74236.02\n", + " 956026.15\n", + " 684808.49\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 10.0\n", + " 78134.94\n", + " 984942.13\n", + " 707094.65\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 11.0\n", + " 81886.33\n", + " 1016758.62\n", + " 732106.17\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 12.0\n", + " 85923.44\n", + " 1053087.90\n", + " 752910.17\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 13.0\n", + " 89696.95\n", + " 1085797.87\n", + " 776179.85\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 14.0\n", + " 93540.52\n", + " 1110640.20\n", + " 797520.58\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 15.0\n", + " 97641.51\n", + " 1138984.22\n", + " 822235.41\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 16.0\n", + " 101692.65\n", + " 1168943.19\n", + " 843897.51\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 17.0\n", + " 101236.75\n", + " 1135093.91\n", + " 843177.15\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 18.0\n", + " 101006.28\n", + " 1160430.45\n", + " 844779.09\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 19.0\n", + " 99952.26\n", + " 1155769.97\n", + " 846060.74\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 20.0\n", + " 99798.64\n", + " 1150156.26\n", + " 847556.23\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 21.0\n", + " 99018.86\n", + " 1136873.58\n", + " 844022.97\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 22.0\n", + " 98600.87\n", + " 1112404.25\n", + " 845818.70\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 23.0\n", + " 98634.02\n", + " 1143131.72\n", + " 844118.27\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 24.0\n", + " 98352.90\n", + " 1143931.38\n", + " 837189.02\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 25.0\n", + " 98118.63\n", + " 1102090.61\n", + " 834712.31\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 26.0\n", + " 97752.45\n", + " 1116518.70\n", + " 836344.12\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 27.0\n", + " 97864.07\n", + " 1099224.19\n", + " 827784.91\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 28.0\n", + " 97821.80\n", + " 1114945.37\n", + " 828641.27\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 29.0\n", + " 97564.87\n", + " 1110889.91\n", + " 826123.67\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 30.0\n", + " 98508.10\n", + " 1058548.28\n", + " 817765.77\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 31.0\n", + " 98238.96\n", + " 1119804.85\n", + " 816556.66\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 32.0\n", + " 98363.93\n", + " 1118828.99\n", + " 812661.77\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 33.0\n", + " 96758.69\n", + " 1093426.61\n", + " 805352.16\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 34.0\n", + " 96327.00\n", + " 1108123.59\n", + " 815888.26\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 35.0\n", + " 95913.98\n", + " 1041486.52\n", + " 812348.56\n", + " 9845.64\n", + " 125682.73\n", + " \n", + " \n", + " 36.0\n", + " 95871.39\n", + " 1092395.61\n", + " 817278.03\n", + " 9845.64\n", + " 125682.73\n", " \n", " \n", "\n", @@ -603,60 +823,207 @@ "text/plain": [ "label Multicore: Non-persistent mode + kernel config \\\n", "parallel_fuzzers \n", - "1.0 11019.30 \n", - "2.0 21111.92 \n", - "3.0 30568.82 \n", - "4.0 38963.07 \n", - "5.0 47693.65 \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 133591.26 \n", - "2.0 255995.07 \n", - "3.0 380246.34 \n", - "4.0 490254.72 \n", - "5.0 598698.16 \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 90851.40 \n", - "2.0 176159.32 \n", - "3.0 260268.78 \n", - "4.0 336355.99 \n", - "5.0 413750.00 \n", - "\n", - "label Multicore: afl_execs: Persistent mode/shared memory + kernel config \\\n", - "parallel_fuzzers \n", - "1.0 134423.50 \n", - "2.0 258490.04 \n", - "3.0 383777.45 \n", - "4.0 496249.48 \n", - "5.0 613089.31 \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 11038.96 \n", - "2.0 11038.96 \n", - "3.0 11038.96 \n", - "4.0 11038.96 \n", - "5.0 11038.96 \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 135613.26 \n", - "2.0 135613.26 \n", - "3.0 135613.26 \n", - "4.0 135613.26 \n", - "5.0 135613.26 " + "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": 46, + "execution_count": 148, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pivotdf.head()" + "pivotdf" ] }, { @@ -668,7 +1035,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 149, "metadata": {}, "outputs": [ { @@ -680,81 +1047,71 @@ "\n", "#### test-instr vs. test-instr-persist-shmem\n", "\n", - "This graph is scaled so that the single-core, non-persistent-mode performance (11038 execs per second) is\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 103765 execs per second, which is **9.4x** that base speed.\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 **12.3x** base speed. So -- perhaps counter-intuively -- if you have a choice between switching to using multiple cores or rewriting\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", - "**75.6x** base speed.\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 `total_execs_per_sec` increase of 368476 execs -- the difference between\n", - "109.0x (mitigations off) and 75.6x (mitigations on) base speed. Turning on mitigations\n", - "reduced the overall performance by 31%!\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 115588 execs per sec, but the loss due to\n", - "mitigations is 368476 execs per sec, which is the averaged performance of 3.2 cores.\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 total_execs_per_sec speed on this machine, which is **109.0x** higher\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", - "#### afl_execs_per_sec vs. total_execs_per_sec\n", - "\n", - "* The purple line at the top is measuring `afl_execs_per_sec`. This is afl's own measurement of the speed of each fuzzer process, from the `out/fuzzer/fuzzer_stats` file.\n", - " * It peaks at 23 fuzzers running in parallel, on this 8-core (16-thread) CPU.\n", - " * In contrast, `total_execs_per_sec` shows large drops in performance as we pass 8 (cores) and 16 (threads) fuzzers.\n", - " * I'm inclined to trust `total_execs_per_sec` `(total_execs / (end time - start time))` more, so we'll use that from now on.\n", - "\n", "#### How many parallel fuzzers should we use on this machine?\n", "\n", - "* The drops in performance after 8/16 fuzzers are profound.\n", - " * Using 9-12 fuzzers is *worse* than using 8 fuzzers on this 8C/16T system, but using 13-16 is better than 8.\n", - " * And using >16 is worse than using 16. Makes sense.\n", - " * 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 21%\n", - " of performance. This could be a good tradeoff in terms of cost.\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": [ "" ] }, - "execution_count": 47, + "execution_count": 149, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# (Ignore this code cell.)\n", + "# (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_with_afl_max_execs = int(pivotdf[\"Multicore: afl_execs: Persistent mode/shared memory + kernel config\"].idxmax())\n", - "multicore_fuzzers_with_total_max_execs = int(pivotdf[\"Multicore: Persistent mode/shared memory + kernel config\"].idxmax())\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_with_total_max_execs / 2][multicore_persistent_without_mitigations_label]\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", @@ -789,7 +1146,7 @@ "#### 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 `total_execs_per_sec` increase of {mitigations_off_increase} execs -- the difference between\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", @@ -799,24 +1156,15 @@ "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 total_execs_per_sec speed on this machine, which is **{factor_for_execs(multicore_max_execs_mitigations_off)}x** higher\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", - "#### afl_execs_per_sec vs. total_execs_per_sec\n", - "\n", - "* The purple line at the top is measuring `afl_execs_per_sec`. This is afl's own measurement of the speed of each fuzzer process, from the `out/fuzzer/fuzzer_stats` file.\n", - " * It peaks at {multicore_fuzzers_with_afl_max_execs} fuzzers running in parallel, on this 8-core (16-thread) CPU.\n", - " * In contrast, `total_execs_per_sec` shows large drops in performance as we pass 8 (cores) and 16 (threads) fuzzers.\n", - " * I'm inclined to trust `total_execs_per_sec` `(total_execs / (end time - start time))` more, so we'll use that from now on.\n", - "\n", "#### How many parallel fuzzers should we use on this machine?\n", "\n", - "* The drops in performance after 8/16 fuzzers are profound.\n", - " * Using 9-12 fuzzers is *worse* than using 8 fuzzers on this 8C/16T system, but using 13-16 is better than 8.\n", - " * And using >16 is worse than using 16. Makes sense.\n", - " * 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", + "* 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" ] }, @@ -826,12 +1174,12 @@ "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, and the answer calls the conclusion we just made above into question:" + "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": 48, + "execution_count": 150, "metadata": {}, "outputs": [ { @@ -864,53 +1212,41 @@ " hardware.cpu_fastest_core_mhz\n", " hardware.cpu_model\n", " hardware.cpu_threads\n", - " targets.test-instr-persist-shmem.multicore.afl_execs_per_sec\n", - " targets.test-instr-persist-shmem.multicore.afl_execs_total\n", + " targets.test-instr-persist-shmem.multicore.execs_per_sec\n", + " targets.test-instr-persist-shmem.multicore.execs_total\n", " targets.test-instr-persist-shmem.multicore.fuzzers_used\n", - " targets.test-instr-persist-shmem.multicore.run_end\n", - " targets.test-instr-persist-shmem.multicore.run_start\n", - " targets.test-instr-persist-shmem.multicore.total_execs_per_sec\n", - " targets.test-instr-persist-shmem.multicore.total_run_time\n", " \n", " \n", " \n", " \n", - " 148\n", - " False\n", + " 223\n", + " True\n", " True\n", " ++4.09a\n", " AWS EC2 r6a.48xlarge spot instance\n", " clang version 15.0.7 (Amazon Linux 15.0.7-3.am...\n", " x86_64-amazon-linux-gnu\n", - " 3599.314\n", + " 3514.326\n", " AMD EPYC 7R13 Processor\n", " 192\n", - " 85586.47\n", - " 519670.0\n", + " 119469.35\n", + " 1194813.0\n", " 1.0\n", - " 2023-09-30 07:42:00.479418\n", - " 2023-09-30 07:41:57.396293\n", - " 84636.81\n", - " 6.14\n", " \n", " \n", - " 149\n", - " False\n", + " 224\n", + " True\n", " True\n", " ++4.09a\n", " AWS EC2 r6a.48xlarge spot instance\n", " clang version 15.0.7 (Amazon Linux 15.0.7-3.am...\n", " x86_64-amazon-linux-gnu\n", - " 3599.425\n", + " 3599.748\n", " AMD EPYC 7R13 Processor\n", " 192\n", - " 171655.96\n", - " 1039340.0\n", + " 237177.20\n", + " 2372250.0\n", " 2.0\n", - " 2023-09-30 07:42:06.853436\n", - " 2023-09-30 07:42:03.776562\n", - " 168998.37\n", - " 6.15\n", " \n", " \n", "\n", @@ -918,55 +1254,39 @@ ], "text/plain": [ " config.afl_persistent_config config.afl_system_config \\\n", - "148 False True \n", - "149 False True \n", + "223 True True \n", + "224 True True \n", "\n", " config.afl_version config.comment \\\n", - "148 ++4.09a AWS EC2 r6a.48xlarge spot instance \n", - "149 ++4.09a AWS EC2 r6a.48xlarge spot instance \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", - "148 clang version 15.0.7 (Amazon Linux 15.0.7-3.am... \n", - "149 clang version 15.0.7 (Amazon Linux 15.0.7-3.am... \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", - "148 x86_64-amazon-linux-gnu 3599.314 \n", - "149 x86_64-amazon-linux-gnu 3599.425 \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", - "148 AMD EPYC 7R13 Processor 192 \n", - "149 AMD EPYC 7R13 Processor 192 \n", - "\n", - " targets.test-instr-persist-shmem.multicore.afl_execs_per_sec \\\n", - "148 85586.47 \n", - "149 171655.96 \n", + "223 AMD EPYC 7R13 Processor 192 \n", + "224 AMD EPYC 7R13 Processor 192 \n", "\n", - " targets.test-instr-persist-shmem.multicore.afl_execs_total \\\n", - "148 519670.0 \n", - "149 1039340.0 \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.fuzzers_used \\\n", - "148 1.0 \n", - "149 2.0 \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.run_end \\\n", - "148 2023-09-30 07:42:00.479418 \n", - "149 2023-09-30 07:42:06.853436 \n", - "\n", - " targets.test-instr-persist-shmem.multicore.run_start \\\n", - "148 2023-09-30 07:41:57.396293 \n", - "149 2023-09-30 07:42:03.776562 \n", - "\n", - " targets.test-instr-persist-shmem.multicore.total_execs_per_sec \\\n", - "148 84636.81 \n", - "149 168998.37 \n", - "\n", - " targets.test-instr-persist-shmem.multicore.total_run_time \n", - "148 6.14 \n", - "149 6.15 " + " targets.test-instr-persist-shmem.multicore.fuzzers_used \n", + "223 1.0 \n", + "224 2.0 " ] }, - "execution_count": 48, + "execution_count": 150, "metadata": {}, "output_type": "execute_result" } @@ -978,7 +1298,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 151, "metadata": {}, "outputs": [ { @@ -1011,36 +1331,36 @@ " \n", " \n", " \n", - " 399\n", - " 331957.22\n", - " 200.0\n", + " 0\n", + " 119469.35\n", + " 1.0\n", " True\n", " True\n", - " Multicore: afl_execs: Persistent mode/shared m...\n", + " Multicore: Persistent mode/shared memory + ker...\n", " \n", " \n", - " 153\n", - " 1026766.44\n", - " 77.0\n", + " 1\n", + " 237177.20\n", + " 2.0\n", " True\n", " True\n", - " Multicore: afl_execs: Persistent mode/shared m...\n", + " Multicore: Persistent mode/shared memory + ker...\n", " \n", " \n", "\n", "" ], "text/plain": [ - " execs_per_sec parallel_fuzzers afl_persistent_config \\\n", - "399 331957.22 200.0 True \n", - "153 1026766.44 77.0 True \n", + " execs_per_sec parallel_fuzzers afl_persistent_config afl_system_config \\\n", + "0 119469.35 1.0 True True \n", + "1 237177.20 2.0 True True \n", "\n", - " afl_system_config label \n", - "399 True Multicore: afl_execs: Persistent mode/shared m... \n", - "153 True Multicore: afl_execs: Persistent mode/shared m... " + " label \n", + "0 Multicore: Persistent mode/shared memory + ker... \n", + "1 Multicore: Persistent mode/shared memory + ker... " ] }, - "execution_count": 49, + "execution_count": 151, "metadata": {}, "output_type": "execute_result" } @@ -1052,13 +1372,13 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 152, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ - "510152025303540455055606570758085909510010511011512012513013514014515015516016517017518018519019520010x36x62x89x115x141xConfigurationMulticore: Persistent mode/shared memory + kernel configMulticore: afl_execs: Persistent mode/shared memory + kernel configFuzzer performanceNumber of parallel fuzzersFuzz target executions per second" + "481216202428323640444852566064687276808488929610010410811211612012412813213614014414815215616016416817217618018418819212x43x74x104x135x166xConfigurationMulticore: Persistent mode/shared memory + kernel configFuzzer performanceNumber of parallel fuzzersFuzz target executions per second" ] }, "metadata": {}, @@ -1081,7 +1401,7 @@ "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, 5)))\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\")" ] @@ -1092,11 +1412,11 @@ "source": [ "### Line graph analysis\n", "\n", - "This is a shocking result for a 192 vCPU machine -- whether you count `afl_execs` or `total_execs`, our optimal number of parallel fuzzers was 16!\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", - "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", + "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 causes here are a problem with our Python harness, or potentially 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 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. " + "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. " ] } ], @@ -1116,7 +1436,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.3" + "version": "3.11.5" }, "orig_nbformat": 4 }, -- cgit 1.4.1