vs-kangaroo-multi

Forked from https://github.com/Telariust/vs-kangaroo from https://github.com/JeanLucPons/Kangaroo

This experimental project.

Usage

./vs-kangaroo-multi -v 1 -gpu -d 14 -bits 65 0230210c23b1a047bc9bdbb13448e67deddc108946de6de639bcc75d47c0216b1b

./vs-kangaroo-multi -v 1 -gpu -d 14 -bits 75 03726b574f193e374686d8e12bc6e4142adeb06770e0a2856f5e4ad89f66044755

Check Bits: 110 Compressed Address: 1FZgkq1dqCfgxaCFVkoP9sZuFFja1FTwMP Address hash160: 9fc042129e3c1c5649beee5f575babfc85eba9db Secret wif: KwDiBf89QgGbjEhKnhXJuH7LrctqV9Hyh5UagDz41NsKpRbQHnAx Secret hex: 0x3878efc360a7e843c1517c415f07L pk: 0352c323c1fe80131546a81141657cbd7f84a00a869cdb31c3b09310048bdc2077

./vs-kangaroo-multi -v 1 -d 14 -gpu -bits 3878efc360a6e843c1517c415f07:3878efc360a8e843c1517c415f07 0352c323c1fe80131546a81141657cbd7f84a00a869cdb31c3b09310048bdc2077

Check Bits: 256 Compressed Address: 1LrqA7saB8tP7NN42yDui4Y2VxQkQKGnTL Address hash160: d9d6f8bd48f1056c0413d21979774348573f8981 Secret wif: L2iAxC8cAhWHk435CpmQ6CAxLWYXpryCXctYVXEJ5NikpfaCucCf Secret hex: 0xa3d4235179bd55c0ba6595fbef8cd2a45efa5fd31ce25150eb9b72b8ff810cb3L pk: 02be96c2c11b385b20e5783ded59293a6e7e714d99e259d99cd1e6071332834324

./vs-kangaroo-multi -v 1 -d 14 -gpu -bits a3d4235179bd55c0ba6595fbef8cd2a45efa5fd31ce25150eb9a72b8ff810cb3:a3d4235179bd55c0ba6595fbef8cd2a45efa5fd31ce25150eb9c72b8ff810cb3 02be96c2c11b385b20e5783ded59293a6e7e714d99e259d99cd1e6071332834324

Compilation

Windows

Intall CUDA SDK and open vs-kangaroo.sln in Visual C++ 2017.
You may need to reset your Windows SDK version in project properties.
In Build->Configuration Manager, select the Release configuration.
Build and enjoy.

Note: The current relase has been compiled with CUDA SDK 10.2, if you have a different release of the CUDA SDK, you may need to update CUDA SDK paths in vs-kangaroo.vcxproj using a text editor. The current nvcc option are set up to architecture starting at 3.0 capability, for older hardware, add the desired compute capabilities to the list in GPUEngine.cu properties, CUDA C/C++, Device, Code Generation.

Linux

Intall CUDA SDK.
Depenging on the CUDA SDK version and on your Linux distribution you may need to install an older g++ (just for the CUDA SDK).
Edit the makefile and set up the good CUDA SDK path and appropriate compiler for nvcc.

CUDA       = /usr/local/cuda-8.0
CXXCUDA    = /usr/bin/g++-4.8

You can enter a list of architectrure (refer to nvcc documentation) if you have several GPU with different architecture. Compute capability 2.0 (Fermi) is deprecated for recent CUDA SDK. vs-kangaroo-multi need to be compiled and linked with a recent gcc (>=7). The current release has been compiled with gcc 7.3.0.
Go to the vs-kangaroo-multi directory. ccap is the desired compute capability https://ru.wikipedia.org/wiki/CUDA

$ g++ -v
gcc version 7.3.0 (Ubuntu 7.3.0-27ubuntu1~18.04)
$ make all (for build without CUDA support)
or
$ make gpu=1 ccap=20 all

License

vs-kangaroo-multi is licensed under GPLv3.

qalander / vs-kangaroo-multi Goto Github PK