SILENTARMY

SILENTARMY is a Zcash miner for Linux written in OpenCL with multi-GPU support. The Stratum protocol is implemented for connecting to mining pools. It runs best on AMD GPUs but has also been reported to work on other OpenCL devices such as Xeon Phi, Intel GPUs, and through OpenCL CPU drivers with NVIDIA support.

After compiling SILENTARMY, list the available OpenCL devices:

$ silentarmy --list

Start mining with two GPUs (ID 2 and ID 5) on a pool:

$ silentarmy --use 2,5 -c stratum+tcp://us1-zcash.flypool.org:3333 -u t1cVviFvgJinQ4w3C2m2CfRxgP5DnHYaoFC

When run without options, SILENTARMY mines with the first OpenCL device, using my donation address, on flypool:

$ silentarmy
Connecting to us1-zcash.flypool.org:3333
Stratum server sent us the first job
Mining on 1 device
Total 0.0 sol/s [dev0 0.0] 0 shares
Total 43.9 sol/s [dev0 43.9] 0 shares
Total 46.9 sol/s [dev0 46.9] 0 shares
Total 44.9 sol/s [dev0 44.9] 1 share
[...]

Usage:

$ silentarmy --help
Usage: silentarmy [options]

Options:
  -h, --help            show this help message and exit
  -v, --verbose         verbose mode (may be repeated for more verbosity)
  --debug               enable debug mode (for developers only)
  --list                list available OpenCL devices by ID (GPUs...)
  --use=LIST            use specified GPU device IDs to mine, for example to
                        use the first three: 0,1,2 (default: 0)
  --instances=N         run N instances of Equihash per GPU (default: 2)
  -c POOL, --connect=POOL
                        connect to POOL, for example:
                        stratum+tcp://example.com:1234
  -u USER, --user=USER  username for connecting to the pool
  -p PWD, --pwd=PWD     password for connecting to the pool

Equihash solver

SILENTARMY also provides a command line Equihash solver (sa-solver) implementing the CLI API described in the Zcash open source miner challenge. To solve a specific block header and print the encoded solution on stdout, run the following command (this header is from mainnet block #3400 and should result in 1 Equihash solution):

$ sa-solver -i 04000000e54c27544050668f272ec3b460e1cde745c6b21239a81dae637fde4704000000844bc0c55696ef9920eeda11c1eb41b0c2e7324b46cc2e7aa0c2aa7736448d7a000000000000000000000000000000000000000000000000000000000000000068241a587e7e061d250e000000000000010000000000000000000000000000000000000000000000

If the option -i is not specified, sa-solver solves a 140-byte header of all zero bytes. The option --nonces <nr> instructs the program to try multiple nonces, each time incrementing the nonce by 1. So a convenient way to run a quick test/benchmark is simply:

$ sa-solver --nonces 100

Note: due to BLAKE2b optimizations in my implementation, if the header is specified it must be 140 bytes and its last 12 bytes must be zero. For convenience, -i can also specify a 108-byte nonceless header to which sa-solver adds an implicit nonce of 32 zero bytes.

Use the verbose (-v) and very verbose (-v -v) options to show the solutions and statistics in progressively more and more details.

Performance

• 47.5 Sol/s with one R9 Nano
• 45.0 Sol/s with one R9 290X
• 41.0 Sol/s with one RX 480 8GB

Note: the silentarmy miner automatically achieves this performance level, however the sa-solver command-line solver by design runs only 1 instance of the Equihash proof-of-work algorithm causing it to underperform. One must manually run 2 instances of sa-solver (eg. in 2 terminal consoles) to achieve the same performance level as the silentarmy miner.

Troubleshooting performance issues:

• By default SILENTARMY mines with only one device/GPU; make sure to specify all the GPUs in the --use option, for example silentarmy --use 0,1,2 if the host has three devices with IDs 0, 1, and 2.
• If some GPUs have less than ~2.4 GB of GPU memory, run silentarmy --instances 1 (2 instances use ~2.4 GB of GPU memory, 1 instance uses ~1.2 GB of GPU memory.)
• If you are using an AMD GPU with the Radeon Software Crimson Edition driver, as opposed to the AMDGPU-PRO driver, then edit param.h and set OPTIM_FOR_FGLRX to 1. This will improve performance by +5% and reduce GPU memory usage from 1.2 GB per instance to 805 MB per instance. But do not set it if you are using the AMDGPU-PRO driver or else it will degrade performance by -15% or more.
• If 1 instance still requires too much memory, edit param.h and set NR_ROWS_LOG to 19 (this reduces the per-instance memory usage to ~670 MB) and run with --instances 1.

Dependencies

SILENTARMY has primarily been tested with AMD GPUs on 64-bit Linux with the AMDGPU-PRO driver (amdgpu.ko, for newer GPUs) and the Radeon Software Crimson Edition driver (fglrx.ko, for older GPUs). Its only build dependency is an OpenCL implementation.

Installation of the drivers and SDK can be error-prone, so below are step-by-step instructions for the AMD OpenCL implementation (AMD APP SDK), for Ubuntu 16.04 as well as Ubuntu 14.04 (beware: the silentarmy miner makes use of Python's ensure_future() which requires Python 3.4.4, however Ubuntu 14.04 ships 3.4.3, therefore only the sa-solver tool is usable on Ubuntu 14.04.)

Ubuntu 16.04

1. Download the AMDGPU-PRO Driver (as of 30 Oct 2016, the latest version is 16.40)

2. Extract it: $ tar xf amdgpu-pro-16.40-348864.tar.xz

3. Install (non-root, will use sudo access automatically): $ ./amdgpu-pro-install

4. Add yourself to the video group if not already a member: $ sudo gpasswd -a $(whoami) video

5. Reboot

6. Download the AMD APP SDK (as of 27 Oct 2016, the latest version is 3.0)

7. Extract it: $ tar xf AMD-APP-SDKInstaller-v3.0.130.136-GA-linux64.tar.bz2

8. Install system-wide by running as root (accept all the default options): $ sudo ./AMD-APP-SDK-v3.0.130.136-GA-linux64.sh

9. Install compiler dependencies which you will need to compile SILENTARMY: $ sudo apt-get install build-essential

Ubuntu 14.04

1. Install the official Ubuntu package: $ sudo apt-get install fglrx (as of 30 Oct 2016, the latest version is 2:15.201-0ubuntu0.14.04.1)

2. Follow steps 5-9 above.

Arch Linux

1. Install the silentarmy AUR package.

Compilation and installation

Compiling SILENTARMY is easy:

$ make

You may need to specify the paths to the locations of your OpenCL C headers and libOpenCL.so if the Makefile does not find them:

$ make OPENCL_HEADERS=/path/here LIBOPENCL=/path/there

Self-testing the command-line solver (solves 100 all-zero 140-byte blocks with their nonces varying from 0 to 99):

$ make test

For more testing run sa-solver --nonces 10000. It should finds 18681 solutions which is less than 1% off the theoretical expected average number of solutions of 1.88 per Equihash run at (n,k)=(200,9).

For installing, just copy silentarmy and sa-solver to the same directory.

Implementation details

The silentarmy Python script is actually mostly a lighteight Stratum implementation and job dispatcher that sends Equihash work items to 1 or more instances of sa-solver --mining which initializes the solver in a special "mining mode" so it can be controled via stdin/stdout. By default 2 instances of sa-solver are launched for each GPU (this can be changed with the silentarmy --instances N option.) 2 instances per GPU usually results in the best performance.

The sa-solver binary invokes the OpenCL kernel which contains the core of the Equihash algorithm. My implementation uses two hash tables to avoid having to sort the (Xi,i) pairs:

• Round 0 (BLAKE2b) fills up table #0
• Round 1 reads table #0, identifies collisions, XORs the Xi's, stores the results in table #1
• Round 2 reads table #1 and fills up table #0 (reusing it)
• Round 3 reads table #0 and fills up table #1 (also reusing it)
• ...
• Round 8 (last round) reads table #1 and fills up table #0.

Only the non-zero parts of Xi are stored in the hash table, so fewer and fewer bytes are needed to store Xi as we progress toward round 8. For a description of the layout of the hash table, see the comment at the top of input.cl.

Also the code implements the notion of "encoded reference to inputs" which I--apparently like most authors of Equihash solvers--independently discovered as a neat trick to save having to read/write so much data. Instead of saving lists of inputs that double in size every round, SILENTARMY re-uses the fact they were stored in the previous hash table, and saves a reference to the two previous inputs, encoded as a (row,slot0,slot1) where (row,slot0) and (row,slot1) themselves are each a reference to 2 previous inputs, and so on, until round 0 where the inputs are just the 21-bit values.

A BLAKE2b optimization implemented by SILENTARMY requires the last 12 bytes of the nonce/header to be zero. When set to a fixed value like zero, not only the code does not need to implement the "sigma" permutations, but many 64-bit additions in the BLAKE2b mix() function can be pre-computed automatically by the OpenCL compiler.

Managing invalid solutions (duplicate inputs) is done in multiple places:

• Any time a XOR results in an all-zero value, this work item is discarded as it is statistically very unlikely that the XOR of 256 or fewer inputs is zero. This check is implemented at the end of xor_and_store()
• When the final hash table produced at round 8 has many elements that collide in the same row (because bits 160-179 are identical, and almost certainly bits 180-199), this is also discarded as a likely invalid solution because this is statistically guaranteed to be all inputs repeated at least once. This check is implemented in kernel_sols() (see likely_invalids.)
• Finally when the GPU returns potential solutions, the CPU also checks for invalid solutions with duplicate inputs. This check is implemented in verify_sol().

Finally, SILENTARMY makes many optimization assumptions and currently only supports Equihash parameters 200,9.

Author

Marc Bevand -- http://zorinaq.com

Donations welcome: t1cVviFvgJinQ4w3C2m2CfRxgP5DnHYaoFC

Thanks

I would like to thank these persons for their contributions to SILENTARMY, in alphabetical order:

• nerdralph

License

The MIT License (MIT) Copyright (c) 2016 Marc Bevand

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.