Starting with Windows Build 20348 the behavior of the numa API has been changed:
https://docs.microsoft.com/en-us/windows/win32/procthread/numa-support
Old code only worked because there was probably a limit on how many
cores/threads can reside within one NUMA node, and the OS creates extra NUMA
nodes when necessary, however the actual mechanism of core binding is
done by "Processor Groups"(https://docs.microsoft.com/en-us/windows/win32/procthread/processor-groups). With a newer OS, one NUMA node can have many
such "Processor Groups" and we should just consistently use the number
of groups to bind the threads instead of deriving the topology from
the number of NUMA nodes.
This change is required to spread threads on all cores on Windows 11 with
a 3990X CPU. It has only 1 NUMA node with 2 groups of 64 threads each.
closes https://github.com/official-stockfish/Stockfish/pull/3787
No functional change.
Official release version of Stockfish 14.1
Bench: 6334068
---
Today, we have the pleasure to announce Stockfish 14.1.
As usual, downloads will be freely available at stockfishchess.org/download [1].
With Stockfish 14.1 our users get access to the strongest chess engine
available today. In the period leading up to this release, Stockfish
convincingly won several chess engine tournaments, including the TCEC 21
superfinal, the TCEC Cup 9, and the Computer Chess Championship for
Fischer Random Chess (Chess960). In the latter tournament, Stockfish
was undefeated in 599 out of 600 games played.
Compared to Stockfish 14, this release introduces a more advanced NNUE
architecture and various search improvements. In self play testing, using
a book of balanced openings, Stockfish 14.1 wins three times more game
pairs than it loses [2]. At this high level, draws are very common, so the
Elo difference to Stockfish 14 is about 17 Elo. The NNUE evaluation method,
introduced to top level chess with Stockfish 12 about one year ago [3],
has now been adopted by several other strong CPU based chess engines.
The Stockfish project builds on a thriving community of enthusiasts
(thanks everybody!) that contribute their expertise, time, and resources
to build a free and open-source chess engine that is robust,
widely available, and very strong. We invite our chess fans to join the
fishtest testing framework and programmers to contribute to the project [4].
Stay safe and enjoy chess!
The Stockfish team
[1] https://stockfishchess.org/download/
[2] https://tests.stockfishchess.org/tests/view/6175c320af70c2be1788fa2b
[3] https://github.com/official-stockfish/Stockfish/discussions/3628
[4] https://stockfishchess.org/get-involved/
Official release version of Stockfish 14
Bench: 4770936
---
Today, we have the pleasure to announce Stockfish 14.
As usual, downloads will be freely available at https://stockfishchess.org
The engine is now significantly stronger than just a few months ago,
and wins four times more game pairs than it loses against the previous
release version [0]. Stockfish 14 is now at least 400 Elo ahead of
Stockfish 7, a top engine in 2016 [1]. During the last five years,
Stockfish has thus gained about 80 Elo per year.
Stockfish 14 evaluates positions more accurately than Stockfish 13 as
a result of two major steps forward in defining and training the
efficiently updatable neural network (NNUE) that provides the evaluation
for positions.
First, the collaboration with the Leela Chess Zero team - announced
previously [2] - has come to fruition. The LCZero team has provided a
collection of billions of positions evaluated by Leela that we have
combined with billions of positions evaluated by Stockfish to train the
NNUE net that powers Stockfish 14. The fact that we could use and combine
these datasets freely was essential for the progress made and demonstrates
the power of open source and open data [3].
Second, the architecture of the NNUE network was significantly updated:
the new network is not only larger, but more importantly, it deals better
with large material imbalances and can specialize for multiple phases of
the game [4]. A new project, kick-started by Gary Linscott and
Tomasz Sobczyk, led to a GPU accelerated net trainer written in
pytorch.[5] This tool allows for training high-quality nets in a couple
of hours.
Finally, this release features some search refinements, minor bug
fixes and additional improvements. For example, Stockfish is now about
90 Elo stronger for chess960 (Fischer random chess) at short time control.
The Stockfish project builds on a thriving community of enthusiasts
(thanks everybody!) that contribute their expertise, time, and resources
to build a free and open-source chess engine that is robust, widely
available, and very strong. We invite our chess fans to join the fishtest
testing framework and programmers to contribute to the project on
github [6].
Stay safe and enjoy chess!
The Stockfish team
[0] https://tests.stockfishchess.org/tests/view/60dae5363beab81350aca077
[1] https://nextchessmove.com/dev-builds
[2] https://stockfishchess.org/blog/2021/stockfish-13/
[3] https://lczero.org/blog/2021/06/the-importance-of-open-data/
[4] https://github.com/official-stockfish/Stockfish/commit/e8d64af1
[5] https://github.com/glinscott/nnue-pytorch/
[6] https://stockfishchess.org/get-involved/
e2k (Elbrus 2000) - this is a VLIW/EPIC architecture,
the like Intel Itanium (IA-64) architecture.
The architecture has half native / half software support
for most Intel/AMD SIMD (e.g. MMX/SSE/SSE2/SSE3/SSSE3/SSE4.1/SSE4.2/AES/AVX/AVX2 & 3DNow!/SSE4a/XOP/FMA4) via intrinsics.
https://en.wikipedia.org/wiki/Elbrus_2000
closes https://github.com/official-stockfish/Stockfish/pull/3425
No functional change
The codebase contains multiple functions returning by const-value.
This patch is a small cleanup making those function returns
by value instead, removing the const specifier.
closes https://github.com/official-stockfish/Stockfish/pull/3328
No functional change
Official release version of Stockfish 13
Bench: 3766422
-----
It is our pleasure to release Stockfish 13 to chess fans worldwide.
As usual, downloads are freely available at
https://stockfishchess.org
The Stockfish project builds on a thriving community of enthusiasts
who contribute their expertise, time, and resources to build a free
and open-source chess engine that is robust, widely available, and
very strong. We would like to thank them all!
The good news first: from now on, our users can expect more frequent
high-quality releases of Stockfish! Sadly, this decision has been
triggered by the start of sales of the Fat Fritz 2 engine by ChessBase,
which is a copy of a very recent development version of Stockfish
with minor modifications. We refer to our statement on Fat Fritz 2[1]
and a community blog[2] for further information.
This version of Stockfish is significantly stronger than any of its
predecessors. Stockfish 13 outperforms Stockfish 12 by at least
35 Elo[3]. When playing against a one-year-old Stockfish, it wins 60
times more game pairs than it loses[4]. This release features an NNUE
network retrained on billions of positions, much faster network
evaluation code, and significantly improved search heuristics, as
well as additional evaluation tweaks. In the course of its development,
this version has won the superfinals of the TCEC Season 19 and
TCEC Season 20.
Going forward, the Leela Chess Zero and Stockfish teams will join
forces to demonstrate our commitment to open source chess engines and
training tools, and open data. We are convinced that our free and
open-source chess engines serve the chess community very well.
Stay safe and enjoy chess!
The Stockfish team
[1] https://blog.stockfishchess.org/post/643239805544792064/statement-on-fat-fritz-2
[2] https://lichess.org/blog/YCvy7xMAACIA8007/fat-fritz-2-is-a-rip-off
[3] https://tests.stockfishchess.org/tests/view/602bcccf7f517a561bc49b11
[4] https://tests.stockfishchess.org/tests/view/600fbb9c735dd7f0f0352d59
Use TT memory functions to allocate memory for the NNUE weights. This
should provide a small speed-up on systems where large pages are not
automatically used, including Windows and some Linux distributions.
Further, since we now have a wrapper for std::aligned_alloc(), we can
simplify the TT memory management a bit:
- We no longer need to store separate pointers to the hash table and
its underlying memory allocation.
- We also get to merge the Linux-specific and default implementations
of aligned_ttmem_alloc().
Finally, we'll enable the VirtualAlloc code path with large page
support also for Win32.
STC: https://tests.stockfishchess.org/tests/view/5f66595823a84a47b9036fba
LLR: 2.94 (-2.94,2.94) {-0.25,1.25}
Total: 14896 W: 1854 L: 1686 D: 11356
Ptnml(0-2): 65, 1224, 4742, 1312, 105
closes https://github.com/official-stockfish/Stockfish/pull/3081
No functional change.
Official release version of Stockfish 12
Bench: 3624569
-----------------------
It is our pleasure to release Stockfish 12 to users world-wide
Downloads will be freely available at
https://stockfishchess.org/download/
This version 12 of Stockfish plays significantly stronger than
any of its predecessors. In a match against Stockfish 11,
Stockfish 12 will typically win at least ten times more game pairs
than it loses.
This jump in strength, visible in regular progression tests during
development[1], results from the introduction of an efficiently
updatable neural network (NNUE) for the evaluation in Stockfish[2],
and associated tuning of the engine as a whole. The concept of the
NNUE evaluation was first introduced in shogi, and ported to
Stockfish afterward. Stockfish remains a CPU-only engine, since the
NNUE networks can be very efficiently evaluated on CPUs. The
recommended parameters of the NNUE network are embedded in
distributed binaries, and Stockfish will use NNUE by default.
Both the NNUE and the classical evaluations are available, and
can be used to assign values to positions that are later 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. The network is optimized and trained on the
evaluations of millions of positions.
The Stockfish project builds on a thriving community of enthusiasts
that contribute their expertise, time, and resources to build a free
and open source chess engine that is robust, widely available, and
very strong. We invite chess fans to join the fishtest testing
framework and programmers to contribute on github[3].
Stay safe and enjoy chess!
The Stockfish team
[1] https://github.com/glinscott/fishtest/wiki/Regression-Tests
[2] 84f3e86790
[3] https://stockfishchess.org/get-involved/
covers the most important cases from the user perspective:
It embeds the default net in the binary, so a download of that binary will result
in a working engine with the default net. The engine will be functional in the default mode
without any additional user action.
It allows non-default nets to be used, which will be looked for in up to
three directories (working directory, location of the binary, and optionally a specific default directory).
This mechanism is also kept for those developers that use MSVC,
the one compiler that doesn't have an easy mechanism for embedding data.
It is possible to disable embedding, and instead specify a specific directory, e.g. linux distros might want to use
CXXFLAGS="-DNNUE_EMBEDDING_OFF -DDEFAULT_NNUE_DIRECTORY=/usr/share/games/stockfish/" make -j ARCH=x86-64 profile-build
passed STC non-regression:
https://tests.stockfishchess.org/tests/view/5f4a581c150f0aef5f8ae03a
LLR: 2.95 (-2.94,2.94) {-1.25,-0.25}
Total: 66928 W: 7202 L: 7147 D: 52579
Ptnml(0-2): 291, 5309, 22211, 5360, 293
closes https://github.com/official-stockfish/Stockfish/pull/3070
fixes https://github.com/official-stockfish/Stockfish/issues/3030
No functional change.
Adds support for Vector Neural Network Instructions (avx512), as available on Intel Cascade Lake
The _mm512_dpbusd_epi32() intrinsic (vpdpbusd instruction) is taylor made for NNUE.
on a cascade lake CPU (AWS C5.24x.large, gcc 10) NNUE eval is at roughly 78% nps of classical
(single core test)
bench 1024 1 24 default depth:
target classical NNUE ratio
vnni 2207232 1725987 78.20
avx512 2216789 1671734 75.41
avx2 2194006 1611263 73.44
modern 2185001 1352469 61.90
closes https://github.com/official-stockfish/Stockfish/pull/2987
No functional change
fails to build on that target, because of missing Intel Intrinsics.
macOS has posix_memalign() since ~2014 so we can simplify the code and just use that for all Apple platforms.
closes https://github.com/official-stockfish/Stockfish/pull/2985
No functional change.
This patch allows old x86 CPUs, from AMD K8 (which the x86-64 baseline
targets) all the way down to the Pentium MMX, to benefit from NNUE with
comparable performance hit versus hand-written eval as on more modern
processors.
NPS of the bench with NNUE enabled on a Pentium III 1.13 GHz (using the
MMX code):
master: 38951
this patch: 80586
NPS of the bench with NNUE enabled using baseline x86-64 arch, which is
how linux distros are likely to package stockfish, on a modern CPU
(using the SSE2 code):
master: 882584
this patch: 1203945
closes https://github.com/official-stockfish/Stockfish/pull/2956
No functional change.
Makefile targets x86-64-sse42, x86-sse3 are removed; x86-64-sse41
is renamed to x86-64-sse41-popcnt (it did enable popcnt).
Makefile variables sse3, sse42, their associated compilation flags
and code in misc.cpp are removed.
closes https://github.com/official-stockfish/Stockfish/pull/2922
No functional change
This patch ports the efficiently updatable neural network (NNUE) evaluation to Stockfish.
Both the NNUE and the classical evaluations are available, and can be used to
assign a value to a position that is later 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. The network is optimized
and trained on the evalutions of millions of positions at moderate search depth.
The NNUE evaluation was first introduced in shogi, and ported to Stockfish 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](https://github.com/nodchip/Stockfish) provides additional
tools to train and develop the NNUE networks.
This patch is the result of contributions of various authors, from various communities,
including: nodchip, ynasu87, yaneurao (initial port and NNUE authors), domschl, FireFather,
rqs, xXH4CKST3RXx, tttak, zz4032, joergoster, mstembera, nguyenpham, erbsenzaehler,
dorzechowski, and vondele.
This new evaluation needed various changes to fishtest and the corresponding infrastructure,
for which tomtor, ppigazzini, noobpwnftw, daylen, and vondele are gratefully acknowledged.
The first networks have been provided by gekkehenker and sergiovieri, with the latter
net (nn-97f742aaefcd.nnue) being the current default.
The evaluation function can be selected at run time with the `Use NNUE` (true/false) UCI option,
provided the `EvalFile` option points the the network file (depending on the GUI, with full path).
The performance of the NNUE evaluation relative to the classical evaluation depends somewhat on
the hardware, and is expected to improve quickly, but is currently on > 80 Elo on fishtest:
60000 @ 10+0.1 th 1
https://tests.stockfishchess.org/tests/view/5f28fe6ea5abc164f05e4c4c
ELO: 92.77 +-2.1 (95%) LOS: 100.0%
Total: 60000 W: 24193 L: 8543 D: 27264
Ptnml(0-2): 609, 3850, 9708, 10948, 4885
40000 @ 20+0.2 th 8
https://tests.stockfishchess.org/tests/view/5f290229a5abc164f05e4c58
ELO: 89.47 +-2.0 (95%) LOS: 100.0%
Total: 40000 W: 12756 L: 2677 D: 24567
Ptnml(0-2): 74, 1583, 8550, 7776, 2017
At the same time, the impact on the classical evaluation remains minimal, causing no significant
regression:
sprt @ 10+0.1 th 1
https://tests.stockfishchess.org/tests/view/5f2906a2a5abc164f05e4c5b
LLR: 2.94 (-2.94,2.94) {-6.00,-4.00}
Total: 34936 W: 6502 L: 6825 D: 21609
Ptnml(0-2): 571, 4082, 8434, 3861, 520
sprt @ 60+0.6 th 1
https://tests.stockfishchess.org/tests/view/5f2906cfa5abc164f05e4c5d
LLR: 2.93 (-2.94,2.94) {-6.00,-4.00}
Total: 10088 W: 1232 L: 1265 D: 7591
Ptnml(0-2): 49, 914, 3170, 843, 68
The needed networks can be found at https://tests.stockfishchess.org/nns
It is recommended to use the default one as indicated by the `EvalFile` UCI option.
Guidelines for testing new nets can be found at
https://github.com/glinscott/fishtest/wiki/Creating-my-first-test#nnue-net-tests
Integration has been discussed in various issues:
https://github.com/official-stockfish/Stockfish/issues/2823https://github.com/official-stockfish/Stockfish/issues/2728
The integration branch will be closed after the merge:
https://github.com/official-stockfish/Stockfish/pull/2825https://github.com/official-stockfish/Stockfish/tree/nnue-player-wip
closes https://github.com/official-stockfish/Stockfish/pull/2912
This will be an exciting time for computer chess, looking forward to seeing the evolution of
this approach.
Bench: 4746616
Do not send the following info string on the first call to
aligned_ttmem_alloc() on Windows:
info string Hash table allocation: Windows large pages [not] used.
The first call occurs before the 'uci' command has been received. This
confuses some GUIs, which expect the first engine-sent command to be
'id' as the response to the 'uci' command. (see https://github.com/official-stockfish/Stockfish/issues/2681)
closes https://github.com/official-stockfish/Stockfish/pull/2689
No functional change.
for users that set the needed privilige "Lock Pages in Memory"
large pages will be automatically enabled (see Readme.md).
This expert setting might improve speed, 5% - 30%, depending
on the hardware, the number of threads and hash size. More for
large hashes, large number of threads and NUMA. If the operating
system can not allocate large pages (easier after a reboot), default
allocation is used automatically. The engine log provides details.
closes https://github.com/official-stockfish/Stockfish/pull/2656
fixes https://github.com/official-stockfish/Stockfish/issues/2619
No functional change
Align the TT allocation by 2M to make it huge page friendly and advise the
kernel to use huge pages.
Benchmarks on my i7-8700K (6C/12T) box: (3 runs per bench per config)
vanilla (nps) hugepages (nps) avg
==================================================================================
bench | 3012490 3024364 3036331 3071052 3067544 3071052 +1.5%
bench 16 12 20 | 19237932 19050166 19085315 19266346 19207025 19548758 +1.1%
bench 16384 12 20 | 18182313 18371581 18336838 19381275 19738012 19620225 +7.0%
On my box, huge pages have a significant perf impact when using a big
hash size. They also speed up TT initialization big time:
vanilla (s) huge pages (s) speed-up
=======================================================================
time stockfish bench 16384 1 1 | 5.37 1.48 3.6x
In practice, huge pages with auto-defrag may always be enabled in the
system, in which case this patch has no effect. This
depends on the values in /sys/kernel/mm/transparent_hugepage/enabled
and /sys/kernel/mm/transparent_hugepage/defrag.
closes https://github.com/official-stockfish/Stockfish/pull/2463
No functional change
Official release version of Stockfish 11.
Bench: 5156767
-----------------------
It is our pleasure to release Stockfish 11 to our fans and supporters.
Downloads are freely available at http://stockfishchess.org/download/
This version 11 of Stockfish is 50 Elo stronger than the last version, and
150 Elo stronger than the version which famously lost a match to AlphaZero
two years ago. This makes Stockfish the strongest chess engine running on
your smartphone or normal desktop PC, and we estimate that on a modern four
cores CPU, Stockfish 11 could give 1:1000 time odds to the human chess champion
having classical time control, and be on par with him. More specific data,
including nice cumulative curves for the progression of Stockfish strength
over the last seven years, can be found on [our progression page][1], at
[Stefan Pohl site][2] or at [NextChessMove][3].
In October 2019 Stockfish has regained its crown in the TCEC competition,
beating in the superfinal of season 16 an evolution of the neural-network
engine Leela that had won the previous season. This clash of style between an
alpha-beta and an neural-network engine produced spectacular chess as always,
with Stockfish [emerging victorious this time][0].
Compared to Stockfish 10, we have made hundreds of improvements to the
[codebase][4], from the evaluation function (improvements in king attacks,
middlegame/endgame transitions, and many more) to the search algorithm (some
innovative coordination methods for the searching threads, better pruning of
unsound tactical lines, etc), and fixed a couple of bugs en passant.
Our testing framework [Fishtest][5] has also seen its share of improvements
to continue propelling Stockfish forward. Along with a lot of small enhancements,
Fishtest has switched to new SPRT bounds to increase the chance of catching Elo
gainers, along with a new testing book and the use of pentanomial statistics to
be more resource-efficient.
Overall the Stockfish project is an example of open-source at its best, as
its buzzing community of programmers sharing ideas and daily reviewing their
colleagues' patches proves to be an ideal form to develop innovative ideas for
chess programming, while the mathematical accuracy of the testing framework
allows us an unparalleled level of quality control for each patch we put in
the engine. If you wish, you too can help our ongoing efforts to keep improving
it, just [get involved][6] :-)
Stockfish is also special in that every chess fan, even if not a programmer,
[can easily help][7] the team to improve the engine by connecting their PC to
Fishtest and let it play some games in the background to test new patches.
Individual contributions vary from 1 to 32 cores, but this year Bojun Guo
made it a little bit special by plugging a whole data center during the whole
year: it was a vertiginous experience to see Fishtest spikes with 17466 cores
connected playing [25600 games/minute][8]. Thanks Guo!
The Stockfish team
[0]: <http://mytcecexperience.blogspot.com/2019/10/season-16-superfinal-games-91-100.html>
[1]: <https://github.com/glinscott/fishtest/wiki/Regression-Tests>
[2]: <https://www.sp-cc.de/index.htm>
[3]: <https://nextchessmove.com/dev-builds>
[4]: <https://github.com/official-stockfish/Stockfish>
[5]: <https://tests.stockfishchess.org/tests>
[6]: <https://stockfishchess.org/get-involved/>
[7]: <https://github.com/glinscott/fishtest/wiki>
[8]: <https://groups.google.com/forum/?fromgroups=#!topic/fishcooking/lebEmG5vgng%5B1-25%5D>
This patch shows a description of the compiler used to compile Stockfish,
when starting from the console.
Usage:
```
./stockfish
compiler
```
Example of output:
```
Stockfish 120120 64 POPCNT by T. Romstad, M. Costalba, J. Kiiski, G. Linscott
Compiled by clang++ 9.0.0 on Apple
__VERSION__ macro expands to: 4.2.1 Compatible Apple LLVM 9.0.0 (clang-900.0.38)
```
No functional change
Stockfish crashes immediately if users enter a wrong file name (or even an existing
folder name) for debug log file. It may be hard for users to find out since it prints
nothing. If they enter the string via a chess GUI, the chess GUI may remember and
auto-send to Stockfish next time, makes Stockfish crashes all the time. Bug report by
Nguyen Hong Pham in this issue: https://github.com/official-stockfish/Stockfish/issues/2365
This patch avoids the crash and instead prefers to exit gracefully with a error
message on std:cerr, like we do with the fenFile for instance.
Closes https://github.com/official-stockfish/Stockfish/pull/2366
No functional change.
Revert the previous patch now that the binary for the super-final
of TCEC season 16 has been sent.
Maybe the feature of showing the name of compiler will be added to the
master branch in the future. But we may use a cleaner way to code it, see
some ideas using the Makefile approach at the end of pull request #2327 :
https://github.com/official-stockfish/Stockfish/pull/2327
Bench: 3618154
This patch shows a description of the compiler used to compile Stockfish,
when starting from the console.
Usage:
```
./stockfish
compiler
```
Example of output:
```
Stockfish 240919 64 POPCNT by T. Romstad, M. Costalba, J. Kiiski, G. Linscott
Compiled by clang++ 9.0.0 on Apple
__VERSION__ macro expands to: 4.2.1 Compatible Apple LLVM 9.0.0 (clang-900.0.38)
```
No functional change
noobpwnftw