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This enables different Stockfish processes that use the same weights to use the same memory. The approach establishes equivalence by memory content, and is compatible with NUMA replication. The benefit of sharing is reduced memory usage and a speedup thanks to improved (inter-process) caching of the network in the CPUs cache, and thus reduced bandwidth usage to main memory. Even though this change doesn't benefit a user running a single process, this helps on fishtest or e.g. for Lichess, when multiple games run concurrently, or multiple positions are analyzed in parallel. This concept was probably first introduced in the Monty engine (https://github.com/official-monty/Monty/pull/62), after a discussion in https://github.com/official-stockfish/fishtest/issues/2077 on the issue of memory pressure. Measurements based on Torch (https://github.com/user-attachments/files/21386224/verbatim.pdf) further suggested that large gains were possible. Multiple other engines have adopted this 'verbatim' format as well. The implementation here adds the flexibility needed for SF, for example, retains the ability to bundle compressed networks with the binary, to load nets by uci option, and to distribute the shared nets to the proper NUMA region. This flexibility comes with a fair amount of complexity in the implementation, such as OS specific code, and fallback code. For most users this should be transparent. However, for example, those running docker containers should ensure the `--ipc` flag is set correctly, and `--shm-size` is sufficiently large. The benefits of this patch significantly depend on hardware, with systems with many cores and a large (O(150MB), the net size) L3 cache benefitting typically most. On such systems SF speedups (as measured via nps playing games with large concurrency but just 1 thread) can be 38%, which results in master vs. patch Elo which gains about 25 Elo. ``` # PLAYER : RATING ERROR POINTS PLAYED (%) 1 shared_memoryPR : 24.8 1.9 39432.0 73728 53 2 master : 0.0 ---- 34296.0 73728 47 ``` In a multithreaded setup, where weights are already shared, that benefit is smaller, for example on the same HW as above, but with 8t for each side. ``` # PLAYER : RATING ERROR POINTS PLAYED (%) 1 shared_memoryPR : 5.2 3.5 9351.0 18432 51 2 master : 0.0 ---- 9081.0 18432 49 ``` On fishtest with a typical hardware mix of our contributors, the following was measured: STC, 60k games https://tests.stockfishchess.org/tests/view/69074a49ea4b268f1fac236c Elo: 4.69 ± 1.4 (95%) LOS: 100.0% Total: 60000 W: 16085 L: 15275 D: 28640 Ptnml(0-2): 154, 6440, 16053, 7148, 205 nElo: 9.38 ± 2.8 (95%) PairsRatio: 1.12 To verify correctness with a single process on a NUMA architecture, speedtest was used, confirming near equivalence: ``` master: Average (over 10): 296236186 shared_memory: Average (over 10): 295769332 ``` Currently, using large pages for the shared network weights is not always possible, which can lead to a small slowdown (1-2%), in case a single process is run. closes https://github.com/official-stockfish/Stockfish/pull/6173 No functional change Co-authored-by: disservin <disservin.social@gmail.com> Co-authored-by: Joost VandeVondele <Joost.VandeVondele@gmail.com>
Stockfish
A free and strong UCI chess engine.
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Overview
Stockfish is a free and strong UCI chess engine derived from Glaurung 2.1 that analyzes chess positions and computes the optimal moves.
Stockfish does not include a graphical user interface (GUI) that is required to display a chessboard and to make it easy to input moves. These GUIs are developed independently from Stockfish and are available online. Read the documentation for your GUI of choice for information about how to use Stockfish with it.
See also the Stockfish documentation for further usage help.
Files
This distribution of Stockfish 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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AUTHORS, a text file with the list of authors for the project.
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src, a subdirectory containing the full source code, including a Makefile that can be used to compile Stockfish 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.
Contributing
See Contributing Guide.
Donating hardware
Improving Stockfish requires a massive amount of testing. You can donate your hardware resources by installing the Fishtest Worker and viewing the current tests on Fishtest.
Improving the code
In the chessprogramming wiki, many techniques used in Stockfish are explained with a lot of background information. The section on Stockfish describes many features and techniques used by Stockfish. However, it is generic rather than focused on Stockfish's precise implementation.
The engine testing is done on Fishtest. If you want to help improve Stockfish, please read this guideline first, where the basics of Stockfish development are explained.
Discussions about Stockfish take place these days mainly in the Stockfish Discord server. This is also the best place to ask questions about the codebase and how to improve it.
Compiling Stockfish
Stockfish 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 Stockfish 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. An example suitable for most Intel and AMD chips:
cd src
make -j profile-build
Detailed compilation instructions for all platforms can be found in our documentation. Our wiki also has information about the UCI commands supported by Stockfish.
Terms of use
Stockfish is free and distributed under the GNU General Public License version 3 (GPL v3). Essentially, this means 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 website, 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 Stockfish in some way, you MUST always include the license and the full source code (or a pointer to where the source code can be found) to generate the exact binary you are distributing. If you make any changes to the source code, these changes must also be made available under GPL v3.
Acknowledgements
Stockfish uses neural networks trained on data provided by the Leela Chess Zero project, which is made available under the Open Database License (ODbL).