anematodeandJoost VandeVondele
d5bbc6b67d
Performance on Spacemit K3, thanks @edolnx for testing master: Total time (ms) : 65200 Nodes searched : 3493826 Nodes/second : 53586 riscv-scalable-port: Total time (ms) : 15834 Nodes searched : 3493826 Nodes/second : 220653 Also thanks to @camel-cdr for guidance on RVV programming, and https://cloud-v.co for supplying an RVV instance to test with passed STC: LLR: 2.81 (-2.94,2.94) <0.00,2.00> Total: 1152 W: 527 L: 108 D: 517 Ptnml(0-2): 0, 17, 167, 348, 44 https://tests.stockfishchess.org/tests/view/6a39895b3036e45021aeb368 ## Summary We've had a `riscv64` target for a while, but haven't really optimized for it, in particular the vector extension (RVV). RVV, like SVE, is based on a scalable vector system where the vector length ranges from 128 to 65536. In practice implementations are between 128 and 2048, and 256 bits is quite common (e.g. the Spacemit K3 system above). Unfortunately this doesn't fit well into the rest of our code which assumes a fixed vector length, so what I've done is bypass the `VECTOR` ifdef (which now basically means "FIXED_LENGTH_VECTOR") and just have RVV-specific paths. The ability to explicitly control `vl` makes the code quite readable, in my opinion. We use LMUL>1 in most places to take advantage of multi-vector instructions. Generally the LMULs were chosen to best support a 256-bit vlen, which is very common, but by virtue of how the vlen control works, the code works with any vlen. In a couple places, i.e., `get_changed_pieces` and `AffineTransformSparseInput::propagate`, we have separate implementations depending on the vlen, because the optimal LMUL varies a lot between implementations. One little wrinkle is that `load_as` is compiled to a sequence of byte loads, because although unaligned loads are legal in RVA23, the spec says that they *may* be extremely slow (even though they usually aren't, in actual hw), so compilers are conservative. Thus I aligned the relevant buffers and made the semantics of `load_as` that the operand is aligned, by adding a runtime assertion. ### Universal binary Adding a universal binary is pretty easy and we can just cross-compile. There are two targets: baseline rv64gc and riscv64-rva23, which is actually a smaller subset of RVA23 that also works on some older processors that don't support the full thing. We use clang because GCC, until recently, has a nasty bug with LTO and RVV. Like the universal ARM and x86 builds, we check all the builds in CI. In this case we run bench with multiple vlens, 128 through 1024. In the meantime I deleted the existing broken and unused riscv64 tests. ### Follow-ups - Optimizations - zvdot4a8i path closes https://github.com/official-stockfish/Stockfish/pull/6920 No functional change
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).