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signum-vanity-opencl's Introduction

Signum vanity opencl

Use GPU or CPU to find a Signum address that matches your desire.

When finding a new account address, you must activate it on first time that you send a transaction. It can be done using extended address S-____-____-____-_____-____________________________________ or using the Signum account activator. After this process, it will be allowed to use the short address S-____-____-____-_____ for transactions.

Status

Hashing at 2M tries per second on RX470. This is 33 times faster than using cpu, or 180x faster than usign previous java solutions (on my setup)! Optimized code in C works at 60k tries per second, 5.5 times faster than java vanity. Check speeds at wiki and add your record there.

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Register your nice adddress at Hall of RS-Addresses dApp, or drop me a donation at S-DKVF-VE8K-KUXB-DELET.

Known issues

Unfortunatelly the program is not running on newer cards shipped with opencl 3.0. Any questions please ask at my discord channel.

Binary package usage

Linux

  • Extract package contents
  • Test if it is running on cpu: ./vanity --cpu A?A
  • Play with command line options to maximize speed

Windows

  • Extract package contents
  • Open a command prompt
  • Go to the folder
  • Test it running on cpu: vanity --cpu A?A
  • Play with command line options to maximize speed

Mac OS

I do not have a hardware to compile the binary, but the compilation process is straight forward. Check instructions on Compilation details.

Help

Passphrase generator for vanity addresses on Signum cryptocurrency.

Usage: vanity [OPTION]... MASK
Example: vanity --cpu --pass-length 32 SGN@

Options:
  --help             Show this help statement
  --suffix           Match given mask from the end of address. Default is to match from the beginning
  --pass-length N    Passphrase length. Max 142 chars. Default: 64
  --cpu              Set to use CPU and disable using GPU
  --gpu              Set to use GPU. Already is default
  --gpu-platform N   Select GPU from platorm N. Default: 0
  --gpu-device N     Select GPU device N. Default: 0
  --gpu-threads N    Send a batch of N threads. Default: 16384
  --gpu-work-size N  Select N concurrent works. Default: Max available
  --endless          Never stop finding passphrases
  --use-charset ABC  Generate passphrase only containing the ABC chars
  --use-bip39        Generate passphrase with 12 words from BIP-39 list
  --dict [EN|PT|ES]  Dictionary language for bip-39. Default is english
  --add-salt ABC     Add your salt to the bip39 word list
  --append-db        Append (or create) database.csv with found results

Mask:
  Specify the rules for the desired address.
  It must be at least one char long.
  No 0, O, I or 1 are allowed.
  Following wildcars can be used:
    ?: Matches any char
    @: Matches only letters [A-Z]
    #: Matches only numbers [2-9]
    c: Matches consonants [BCDFGHJKLMNPQRSTVWXZ]
    v: Matches vowels [AEUY]
    p: Matches previous char
    -: Use to organize the mask, does not affect result
  • Define a custom charset, custom length and your salt for paranoid levels of security
  • Use gpu-work-size as multiple of you graphics card compute units, and gpu-threads to multiple of gpu-work-size for best speed.
  • Try to rename the .cl file and swap with the second implementation. It can be 5% faster.

Compilation details

Linux

  • Dependencies: install packates build-essential, libssl-dev (OpenSSL) and ocl-icd-opencl-dev (OpenCL).
  • Clone repository
  • Compile make
  • Package will be avaliable at dist folder.

Mac OS

  • At the Releases page, download the source code and unzip to a folder
  • Open a terminal and change to the respective folder.
  • Run the command make
  • Package will be avaliable at dist folder.

Windows

Resource files

  • Remember to include passphraseToId.cl, passphraseToId2.cl, bip39*.txt and README.md with the binary file if redistributing a compiled version.

Inspiration

signum-vanity-opencl's People

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signum-vanity-opencl's Issues

Nvidia GTX 1070 - CL_OUT_OF_RESOURCES

Seems like an issue specifically with a the 1070. I have an old 760 that works fine. Tried reducing work group size and threads and neither seems to resolve it. Any ideas?

GPU platform 0 details:
Platform name...........: NVIDIA CUDA
Details for device 0:
Device..................: NVIDIA GeForce GTX 1070
Hardware version........: OpenCL 3.0 CUDA
Software version........: 470.129.06
OpenCL C version........: OpenCL C 1.2
Parallel compute units..: 15
Max Work Group Size.....: 1024
Compiling OpenCL kernel: Done!
Using mask REKT-????-????-?????
2414434 tries for 90% chance finding a match. Ctrl + C to cancel.
OpenCL error -5 at debug position 53:
CL_OUT_OF_RESOURCES if there is a failure to allocate resources required by the OpenCL implementation on the device.

Not working on AMD HD 7970

User reported that the program does incorrect calculations on his GPU when using the card Sapphire HD 7970 OC Boost, when the monitor is plugged on the card. If the monitor is plugged on the second graphics card, then it works.

I do not own this card to make further investigation. If you own this card, please give the feedback.

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