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+# 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).
+