This blogpost is a short tutorial on how to efficiently generate vanity Bitcoin addresses on AWS' GPU instance and the resulting performance.
The next CTF, namely the ASIS Cyber Security Contest, requires you to provide a Bitcoin address during the registration if you want to claim a prize.
Custom Bitcoin addresses
During our team's preparation meetup we thought that it would be a cool idea to have our team's name
legofan in it. Luckily there's a tool called vanitygen which generates Bitcoin addresses until a specific pattern is found.
For example, this will generate an address with the prefix
$> vanitygen 1Hi Difficulty: 1330 Pattern: 1Hi Address: 1His8MJwroVeW4Hsg9u2kdtVKijEaJV1vo Privkey: 5JDiU6JxwbBgPtW2RhrmPAwSMT7Z1qKz1ZHAvfKmTrLEd3t3rxb
However, the longer the prefix is, the longer (exponential!) it takes for the process to finish.
We first started out with our laptops, then some dedicated servers or big VMs and finally tried AWS' g2.8xlarge GPU instance. Skip to the performace section for some numbers.
The g2.8xlarge comes with 32 CPUs, 60GB of RAM and 4 Nvidia GRID K520 cards.
You probably want to rent a so called
Spot instance, because these are usually up to 90% cheaper.
Manson's blog gives a detailed description on how to request a spot instance. However, make sure to choose a custom AMI with the ID
ami-c79b7eac in step 4, because it comes with all Nvidia/cuda drivers preconfigured and a
g2.8xlarge instance in step 5.
You can follow his steps until point 11, but everything else will be already installed.
After logging in using SSH (username is
ubuntu), we need to install some dependencies:
$> sudo apt-get install vim git libcurl3-dev libssl-dev libpcre-dev opencl-dev screen
Now we can compile the
$> git clone https://github.com/samr7/vanitygen.git $> cd vanitygen $> make all
Running the tool without any parameters except for the pattern should show you all available GPUs:
$> ./oclvanitygen 1Test Difficulty: 264104224 Available OpenCL platforms: 0: [NVIDIA Corporation] NVIDIA CUDA 0: [NVIDIA Corporation] GRID K520 1: [NVIDIA Corporation] GRID K520 2: [NVIDIA Corporation] GRID K520 3: [NVIDIA Corporation] GRID K520
The tool can't handle all four GPUs concurrently, so for each GPU 0,1,2,3 you run:
$> screen -S gpu$id $> ./oclvanitygen -p 0 -d $id -i $pat
$idis 0, 1, 2, or 3
$patis the desired pattern
You can exit a screen by hitting
Ctrl+A+D and enter it again by using
screen -r gpu$id. In other words: You run the tool four times - Once per GPU.
Vanitygen can either run as a multicore CPU program (
vanitygen) or use the power of GPUs using opencl (
Here are some numbers:
- 2 Cores @ Core i5-2430M @ 2.4Ghz: ~660 KH/s
- 20 Cores @ Xeon E5-2650L v3 @ 1.80GHz: ~3 MH/s
- 32 Cores @ Xeon E5-2670 @ 2.60GHz: ~ 5 MH/s
- 1 GPU @ Nvidia GRID K520: ~ 23 MH/s
The pattern was four combinations of a 8 characters long, case-insensitive prefix:
$> cat pat 1legofan 1l3gofan 1legof4n 1l3gof4n $> ./oclvanitygen -p 0 -d 0 -i -f pat
I let the GPU instance running for about 55 minutes and it has found 13 matching addresses and that costed me around $2.