DON is a free, powerful UCI chess engine derived from Glaurung 2.1. DON is not a complete chess program and requires a UCI-compatible graphical user interface (GUI) (e.g. XBoard with PolyGlot, Scid, Cute Chess, eboard, Arena, Sigma Chess, Shredder, Chess Partner or Fritz) in order to be used comfortably. Read the documentation for your GUI of choice for information about how to use DON with it.
The DON engine features two evaluation functions for chess, the classical evaluation based on handcrafted terms, and the NNUE evaluation based on efficiently updateable neural networks. The classical evaluation runs efficiently on almost all CPU architectures, while the NNUE evaluation benefits from the vector intrinsics available on most CPUs (sse2, avx2, neon, or similar).
DON uses bitboard representations, and is an alfa-beta searcher.
DON supports up to 512 cores. The engine defaults to one search thread, so it is therefore recommended to inspect the value of the 'Threads' UCI parameter, to make sure it equals the # of CPU cores on your computer.
DON has support for 32/64-bit CPUs, the hardware USE_POPCNT/USE_BMI2 instruction, big-endian machines such as Power PC, and other platforms.
DON has support for Polyglot book. For information about how to create such books, consult the Polyglot documentation. The book file can be selected by setting the Book File UCI parameter.
DON has support for Syzygybases.
This distribution of DON consists of the following files:
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Readme.md, the file you are currently reading.
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Copying.txt, a text file containing the GNU General Public License version 3.
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src, a subdirectory containing the full source code, including a Makefile that can be used to compile DON on Unix-like systems.
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a file with the .nnue extension, storing the neural network for the NNUE evaluation. Binary distributions will have this file embedded.
Note: to use the NNUE evaluation, the additional data file with neural network parameters
needs to be available. Normally, this file is already embedded in the binary or it can be downloaded.
The filename for the default (recommended) net can be found as the default
value of the Eval File UCI option, with the format nn-[SHA256 first 12 digits].nnue
(for instance, nn-c157e0a5755b.nnue). This file can be downloaded from
https://tests.stockfishchess.org/api/nn/[filename]
replacing [filename] as needed.
Currently, DON has the following UCI options:
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The size of the hash table in MB. It is recommended to set Hash after setting Threads.
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Clear the hash table.
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Retain the hash table.
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Hash file name.
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The number of CPU threads used for searching a position. For best performance, set this equal to the number of CPU cores available.
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Lower the Skill Level in order to make DON play weaker (see also UCI_LimitStrength). Internally, MultiPV is enabled, and with a certain probability depending on the Skill Level a weaker move will be played.
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Output the N best lines (principal variations, PVs) when searching. Leave at 1 for best performance.
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A positive value for contempt favors middle game positions and avoids draws.
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By default, contempt is set to prefer the side to move. Set this option to "White" or "Black" to analyse with contempt for that side, or "Off" to disable contempt.
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Toggle between the NNUE and classical evaluation functions. If set to "true", the network parameters must be available to load from file (see also EvalFile), if they are not embedded in the binary.
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The name of the file of the NNUE evaluation parameters. Depending on the GUI the filename might have to include the full path to the folder/directory that contains the file. Other locations, such as the directory that contains the binary and the working directory, are also searched.
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Assume a time delay of x ms due to network and GUI overheads. This is useful to avoid losses on time in those cases.
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Lower values will make DON take less time in games, higher values will make it think longer.
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Let DON ponder its next move while the opponent is thinking.
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Tells the engine to use nodes searched instead of wall time to account for elapsed time. Useful for engine testing.
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Write all communication to and from the engine into a text file.
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Path to the folders/directories storing the Syzygy tablebase files. Multiple directories are to be separated by ";" on Windows and by ":" on Unix-based operating systems. Do not use spaces around the ";" or ":".
Example:
C:\tablebases\wdl345;C:\tablebases\wdl6;D:\tablebases\dtz345;D:\tablebases\dtz6It is recommended to store .rtbw files on an SSD. There is no loss in storing the .rtbz files on a regular HD. It is recommended to verify all md5 checksums of the downloaded tablebase files (
md5sum -c checksum.md5) as corruption will lead to engine crashes. -
Minimum remaining search depth for which a position is probed. Set this option to a higher value to probe less agressively if you experience too much slowdown (in terms of nps) due to TB probing.
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Count drawn by the 50-move rule as win or loss / draw 'true' -> draw 'false' -> win or lose. This is useful for ICCF correspondence games.
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Limit Syzygy tablebase probing to positions with at most this many pieces left (including kings and pawns).
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An option handled by your GUI. If true, DON will play Chess960.
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If enabled, show approximate WDL statistics as part of the engine output. These WDL numbers model expected game outcomes for a given evaluation and game ply for engine self-play at fishtest LTC conditions (60+0.6s per game).
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Enable weaker play aiming for an Elo rating as set by UCI_Elo. This option overrides Skill Level.
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If enabled by UCI_LimitStrength, aim for an engine strength of the given Elo. This Elo rating has been calibrated at a time control of 60s+0.6s and anchored to CCRL 40/4.
Both approaches assign a value to a position that is used in alpha-beta (PVS) search to find the best move. The classical evaluation computes this value as a function of various chess concepts, handcrafted by experts, tested and tuned using fishtest. The NNUE evaluation computes this value with a neural network based on basic inputs (e.g. piece positions only). The network is optimized and trained on the evaluations of millions of positions at moderate search depth.
The NNUE evaluation was first introduced in shogi, and ported to DON afterward. It can be evaluated efficiently on CPUs, and exploits the fact that only parts of the neural network need to be updated after a typical chess move. The nodchip repository provides additional tools to train and develop the NNUE networks.
On CPUs supporting modern vector instructions (avx2 and similar), the NNUE evaluation results in stronger playing strength, even if the nodes per second computed by the engine is somewhat lower (roughly 60% of nps is typical).
Note that the NNUE evaluation depends on the DON binary and the network parameter
file (see Eval File). Not every parameter file is compatible with a given DON binary.
The default value of the Eval File UCI option is the name of a network that is guaranteed
to be compatible with that binary.
If the engine is searching a position that is not in the tablebases (e.g. a position with 8 pieces), it will access the tablebases during the search. If the engine reports a very large score (typically 153.xx), this means that it has found a winning line into a tablebase position.
If the engine is given a position to search that is in the tablebases, it will use the tablebases at the beginning of the search to preselect all good moves, i.e. all moves that preserve the win or preserve the draw while taking into account the 50-move rule. It will then perform a search only on those moves. The engine will not move immediately, unless there is only a single good move. The engine likely will not report a mate score even if the position is known to be won.
It is therefore clear that this behaviour is not identical to what one might be used to with Nalimov tablebases. There are technical reasons for this difference, the main technical reason being that Nalimov tablebases use the DTM metric (distance-to-mate), while Syzygybases use a variation of the DTZ metric (distance-to-zero, zero meaning any move that resets the 50-move counter). This special metric is one of the reasons that Syzygybases are more compact than Nalimov tablebases, while still storing all information needed for optimal play and in addition being able to take into account the 50-move rule.
DON supports large pages on Linux and Windows. Large pages make the hash access more efficient, improving the engine speed, especially on large hash sizes. Typical increases are 5..10% in terms of nodes per second, but speed increases up to 30% have been measured. The support is automatic. DON attempts to use large pages when available and will fall back to regular memory allocation when this is not the case.
Large page support on Linux is obtained by the Linux kernel transparent huge pages functionality. Typically, transparent huge pages are already enabled and no configuration is needed.
The use of large pages requires "Lock Pages in Memory" privilege. See Enable the Lock Pages in Memory Option (Windows) on how to enable this privilege, then run RAMMap to double-check that large pages are used. We suggest that you reboot your computer after you have enabled large pages, because long Windows sessions suffer from memory fragmentation which may prevent DON from getting large pages: a fresh session is better in this regard.
DON has support for 32 or 64-bit CPUs, certain hardware instructions, big-endian machines such as Power PC, and other platforms.
On Unix-like systems, it should be easy to compile DON
directly from the source code with the included Makefile in the folder src.
In general it is recommended to run make help to see a list of make
targets with corresponding descriptions.
cd src
make help
make build ARCH=x86-64-modern
When not using the Makefile to compile (for instance with Microsoft MSVC) you need to manually set/unset some switches in the compiler command line; see file type.h for a quick reference.
When reporting an issue or a bug, please tell us which version and compiler you used to create your executable. These informations can be found by typing the following commands in a console:
./DON compiler
DON's improvement over the last couple of years has been a great community effort. There are a few ways to help contribute to its growth.
Improving DON requires a massive amount of testing. You can donate your hardware resources by installing the Fishtest Worker and view the current tests on Fishtest.
If you want to help improve the code, there are several valuable resources:
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In this wiki, many techniques used in DON are explained with a lot of background information.
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The section on DON describes many features and techniques used by DON. However, it is generic rather than being focused on DON's precise implementation. Nevertheless, a helpful resource.
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The latest source can always be found on GitHub. Discussions about DON take place in the FishCooking group and engine testing is done on Fishtest. If you want to help improve DON, please read this guideline first, where the basics of DON development are explained.
DON is free, and distributed under the GNU General Public License version 3 (GPL v3). Essentially, this means that you are free to do almost exactly what you want with the program, including distributing it among your friends, making it available for download from your web site, selling it (either by itself or as part of some bigger software package), or using it as the starting point for a software project of your own.
The only real limitation is that whenever you distribute DON in some way, you must always include the full source code, or a pointer to where the source code can be found. If you make any changes to the source code, these changes must also be made available under the GPL.
For full details, read the copy of the GPL v3 found in the file named Copying.txt.