https://github.com/etra0/sapeaob-bench

https://github.com/etra0/sapeaob-bench

Last synced: about 1 month ago
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• Host: GitHub
• URL: https://github.com/etra0/sapeaob-bench
• Owner: etra0
• Created: 2021-07-16T23:22:25.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2021-08-05T23:26:10.000Z (over 3 years ago)
• Last Synced: 2024-12-25T11:43:57.613Z (about 2 months ago)
• Language: C++
• Size: 76.2 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Benchmark of sapeAOB.

![](https://github.com/etra0/sapeaob-bench/blob/master/output.png)

The benchmarking process is rather a naive one. We generate a 10MB array with a signature placed anywhere, then run the `sapeaob-benchmark` with that data.

This benchmark include other two libraries:

* otis.h: [Frans Bouma](github.com/FransBouma/)'s implementation which he happily provided to me to do this benchmark, Thanks a lot Frans!

* [ModUtils](https://github.com/CookiePLMonster/ModUtils): Silent's implementation to pattern searching. 

# Doing the experiment yourself.

### 0. Cloning the repo

Make sure you're cloning with the recursive option or update the submodules after. You can do this by using `git clone --recursive `.

### 1. Generate the data

First, you need to generate the random data with the signature in a random place, to do this, you can do

```bash

cd data

python generate_data.py

```

This requires Python 3.9+.

### 2. Building

This project uses Cmake. You can either use Visual Studio or do it in the terminal by:

```bash

mkdir build && cd build

cmake .. -GNinja

ninja

```

### 3. Generating the plot

You can also generate the plot. This requires `pandas`, `matplotlib` and `seaborn` to be installed in your current python environment.

Then, you can do

```bash

python tools/plot.py 

# For example

python tools/plot.py builds/src/sapeaob-benchmark.exe

```