SILENTARMY is an OpenCL GPU Zcash Equihash solver. It runs best on AMD GPUs and implements 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 testnet block #2680 and should result in 3 solutions):

$ silentarmy -i 0400000052a6a17bb3cf95c62ec140d22f4fe96cfbc192ff288251282174481312040000b9711b4850b4b89598e16103148a8a368f74e472fa919ac7d0dbb57b1090f6c80000000000000000000000000000000000000000000000000000000000000000667211581e1b071e4302000000000000020000000000000000000000000000000000000000000000

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

$ silentarmy --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 SILENTARMY 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.

• 45.7 Sol/s with one R9 Nano
• 39.6 Sol/s with one RX 480 8GB

Note: run 2 instances of SILENTARMY in parallel (eg. in 2 terminal consoles) on the same GPU to reach these performance numbers. The code is currently very poorly optimized; it makes zero attempts to keep the queue of OpenCL commands full, therefore 2 instances are needed to keep the GPU fully utilized.

SILENTARMY has primarily been tested with AMD GPUs on 64-bit Linux with the AMDGPU-PRO driver (amdgpu.ko) or the Radeon Software Crimson Edition driver (fglrx.ko). Its only build dependency is the OpenCL C headers from the AMD APP SDK.

Installation of the drivers and SDK can be error-prone, so below are step-by-step instructions for Ubuntu 16.04 as well as Ubuntu 14.04.

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 a compiler tools which you will need to compile SILENTARMY: $ sudo apt-get install build-essential

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.

Compiling SILENTARMY is easy:

$ make

You may need to edit the Makefile and change the path /opt/AMDAPPSDK-3.0/include if AMD APP SDK was installed in a non-default location. Also if you are not using the AMDGPU-PRO Driver you may need to edit the Makefile and change the path to libOpenCL.so.

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

$ make test

For more testing run silentarmy --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 wherever.

SILENTARMY 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.

Marc Bevand -- http://zorinaq.com

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

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