Arionum GPU Miner, Cryptogone Edition

GPU miner for Arionum

This is a fork of Guli's GPU miner, adding windows support and many performance & stability improvements.

Please make sure you install latest Nvidia drivers if you use the CUDA miner !

Miner takes 1% fees (everytime you find a share or block, there is 1 chance on 100 it will be taken as fees).

Prebuilt Binaries

If you do not want to compile the project by yourself, you can find up to date ready to go binaries at this address :

Simple Ubuntu install config & run

Here is a typical list of shell commands needed to install on Ubuntu (parts starting with a # are comments, do not type them !)

I assume this is for a Nvidia card, for AMD card just replace with and skip nvidia drivers installation

I also assume this is for Ubuntu 16, if you want to install on another Ubuntu version (17 or 18), then use another binary url for the wget command, all binaries are here

# update list of known packages
sudo apt-get update

# install nvidia drivers (only needed if you have a NVidia GPU)
sudo ubuntu-drivers autoinstall

# install required packages
sudo apt-get -y install wget libcpprest libboost-all-dev ocl-icd-opencl-dev

# create a folder to put the miner in, and go inside it
mkdir arionum-gpu-miner
cd arionum-gpu-miner

# download latest binary archive, make sure that you use the correct url for your Ubuntu version (16, 17, 18 ...)
wget -O arionum-gpu-miner.tar.gz ""

# extract archive 
tar xzvf arionum-gpu-miner.tar.gz

# edit the run script to fit your needs:
# (set wallet address, worker name, number of batches & threads, eventually change -u to -d if you want to use specific devices)
# when done, press CTRL+X to exit nano text editor, press Y to save changes

# finally you can the miner

How to use

  1. Either compile yourself or get prebuilt binaries (see above)
  2. Uncompress the archive somewhere on your hard drive
  3. If you have a Nvidia card then it is better to use the CUDA version otherwise use OpenCL version
  4. Edit or to set miner id, wallet address and thread / batch count.
  5. Launch or and enjoy mining !

See Tuning section below for more advanced usage and choosing thread & batch count.

Also see the FAQ for more info

Commandline Options

-l, --list-devices                list all available devices and exit
-d, --device(s)=DEVICES           GPU devices to use, examples: -d 0 / -d 1 / -d 0,1,3,5 [default: 0]
-u, --use-all-devices             use all available GPU devices (overrides -d)
-a, --address=ADDRESS             public arionum address [default: 419qwxjJLBRdAVttxcJVT84vdmdq3GP6ghXdQdxN6sqbdr5SBXvPU8bvfVzUXWrjrNrJbAJCvW9JYDWvxenus1pK]
-p, --pool=POOL_URL               pool URL [default:]
-t, --threads-per-device=THREADS  number of threads to use per device, ex: -t 1 / -t 6,3 [default: 1]
-b, --batch-per-device=BATCHES    number of batches to use per device, ex: -b 6 / -b 6,3 [default: 48]
-i, --workerId=WORKER_ID          worker id [default: autogenerated]
    --test-mode                   test CPU/GPU blocks hashrate
    --skip-cpu-blocks             do not mine cpu blocks
    --skip-gpu-blocks             do not mine gpu blocks
    --legacy-5s-hashrate          show 5s avg hashrate instead of last set of batches hashrate
-?, --help                        show this help and exit

Tuning performance

First check the list of compute devices on your system:

  • For this, launch listDevices_CUDA.bat/.sh or listDevices_OpenCL.bat/.sh
  • If no devices listed, means that CUDA / OpenCL drivers are not properly installed or you have no GPU.

Now decide which GPU device(s) you want to use for mining

  • Usually you want all devices, for that use -u parameter
  • If you want to only use specific devices, list them with -d parameter (ex: -d 0,3 mines only on devices 0 and 3)
  • On laptops combinining a gaming GPU with an Integrated GPU, only mine on the gaming GPU (usually -d 0)

On Windows, make sure your virtual memory is bigger than total RAM of all your GPUs

Choosing -t / nThreads and -b / nBatches values:

  • Miner will create nThreads GPU tasks per device, each task computing nBatches hashes
  • Memory used per GPU is nThreads * nBatches * 0.0167 GB
  • Each GPU also needs an extra nThreads * 0.05 GB for CPU/GPU communication & precomputed tables
  • In a perfect world, you would just use -t 1 and set -b as high as possible in order to use all available GPU mem
  • However GPUs have limitations on the maximum size for a single allocated chunk of memory (usualy around 4GB)
  • So if your GPU has more than 4GB you may need to use more threads
  • Also Windows / Linux already uses some of your GPU memory so not all of it is avalaible (use GPUZ or nvidia-smi to see that)
  • For example on a Vega64, with 7.5 GB available over 8, ideally you would use -t 1 -b 448 (7.5 / 0.0167 ~= 448)
  • Sadly, doing this will cause miner to crash at launch with an out of memory error (limitation described above)
  • So instead use -t 2 -b 224, this will allow using the full 2*224*0.0167 ~= 7.5 GB of memory
  • But, again, reality is more complex than that ... GPUs are complex beasts ;-)
  • Some people have reported better hashrates by increasing nThreads and adapting nBatchesaccordingly
  • For the Vega64 example above it would mean : -t 3 -b 148 or -t 4 -b 112 (actually produces lower hashrate for my Vega64)
  • Other people have reported better hashrates by decreasing nBatches while keeping the same nThreads
  • For the Vega64 example above it would mean for example : -t 2 -b 200 (actually produces lower hashrate for my Vega64)
  • This depends on a lot of factors (GPU model, GPU drivers, OS, overclocking settings etc.)
  • So you will have to fiddle a bit, trying various values until you find a good compromise for your mining rig

Test Mode:

  • Make sure to use the --test-mode parameter to run the miner in test mode when you are tuning -b / -t
  • Miner will alternate every 20s between CPU and GPU blocks allowing you to quickly see hashrates
  • Miner will not submit any share in test mode (so you do not need to specify a pool or wallet address)
  • In this mode, the value you want to optimize is the average hashrate (more important than the instant one)
  • In test mode you will see that the hashrate drops a bit every time a block change, this is normal;
  • this happens because GPU need to finish current block tasks before launching tasks for the new block
  • (with CUDA / OpenCL you cannot easily interrupt a task, you need to wait for it to finish)
  • Hashrate loss on block change also explains why in normal mining mode, average hashrate is often lower than instant hashrate

For rigs with many GPUs:

  • If you have a rig with more than 2 GPUs, consider running multiple instances of the miner
  • for example on a 8 gpu rig you could launch 2 instances of 4 GPU: one with -d 0,1,2,3, the other with -d 4,5,6,7
  • people reported 10 to 20% increase in hashrate by using multiple instances using each 2, 3 or 4 GPUs

Example -b / -t values

Here are some battle tested values for various GB GPUs, should be similar for other GPUs with same memory:

AMD Vega64,           8GB, Win10, OpenCL => -t 2 -b 224
NVIDIA 1080ti,       11GB, Linux, CUDA   => -t 3 -b 228
NVIDIA GTX960,        4GB, Linux, CUDA   => -t 1 -b 224
NVIDIA Quadro M500M,  2GB, Win10, CUDA   => -t 1 -b 104

Building (Linux)

First, install CUDA SDK ( Important the CUDA SDK version depends on your GPU drivers (use nvidia-smi to view current driver version) :

driver < 387.26 ==> compatible with CUDA <= 9.0 driver < 396.14 ==> compatible with CUDA <= 9.1 driver >= 396.14 ==> compatible with CUDA <= 9.2

Then use the following commands:

sudo apt-get install git
git clone
cd arionum-gpu-miner
./ linux
./ linux

Once done, binaries are put in the rel/folder.

Note that may ask you for your password as it needs to install some packages with apt-get

Building (Windows)

First install the following:

Then open a git console and launch the following commands (if needed, replace vs2015 by vs2017)

git clone
cd arionum-gpu-miner
./ vs2015
./ vs2015

Once done, binaries are put in the rel/folder.

You can also skip the last step and instead open build/arionum-gpu-miner.sln with Visual Studio 2015 or 2017, then in the toolbar select Release / x64 then Build Menu -> Build Solution.

Versions release notes

Version 1.5.1

  • show cuda / opencl version used for building in welcome msg
  • reduced minimum Cmake version to 3.5 (for Ubuntu 16)
  • on Linux, if CUDA sdk not installed will only compile OpenCL miner
  • fixed various Linux compilation issues
  • improved README (improved linux compilation instructions, added Ubuntu easy setup instructions)

Version 1.5.0

  • reusing same buffers for CPU & GPU blocks (improves stability, less hashrate loss on block change)
  • --test-mode option (benchmarking: in this mode miner alternates GPU/CPU blocks every 20s)
  • fixed bug causing small hashrate loss when submitting shares
  • new "instant" hashrate computation (less variance), average hashrate not changed
  • (use --legacy-5s-hashrate to revert to previous way of computing instant hashrate: avg of last 5s)
  • OpenCL program cache (faster startup time when -t > 1)
  • linux launch scripts: added option to auto restart the miner if it crashes
  • updated FAQ & README

Version 1.5.0 beta

  • improved CPU block mining hashrate (now in line with Ariominer)
  • fixed random crashes at launch
  • --skip-cpu-blocks / --skip-gpu-blocks options
  • use pinned memory (OpenCL)

Version 1.5.0 alpha

  • fixed important memory leak
  • CPU blocks mining now working (but still way slower than Bogdan's)
  • more accurate hashrate computations (the displayed hashrate will probably be a bit lower than before, but more exact)
  • now correctly reports CPU/GPU hashrate too pools
  • can set worker name via -i parameter (you can also set it via the run_CUDA/run_OpenCL scripts)

Version 1.4.0 (08/23/2018)

  • block 80k fork support, only mines GPU blocks, idle during CPU ones
  • Linux support
  • see the FAQ for more info

Version 1.3.0 (07/11/2018)

  • fixed more OpenCL issues
  • switched to a single thread managing all GPUs (improves stability)
  • can now set batch size & thread count per device, for example:
  • -d 0 -t 1 -b 6
  • -d 0,1,2 -t 1 -b 6
  • -d 0,1,2 -t 1,2,1 -b 6,3,6
  • -k parameter removed
  • updated with information on how to get the most out of your GPU(s)

Version 1.2.0 (07/02/2018)

  • fix OpenCL miner (kernel file was missing in release zip)
  • -k parameter now also works for the OpenCL miner
  • show error message and stack trace whenever a CUDA or OpenCL exception occurs (will help fixing future issues)

Version 1.1.0 (06/27/2018)

  • fix CUDA multi gpu support (-d & -u parameters)
  • add -k parameter, allows to specify a list of gpu devices to use, ex: -k 0,3,7
  • show error message when out of memory during CUDA kernel creation