New engine releases & news H1 2022

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pohl4711
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Full name: Stefan Pohl

Re: New engine releases & news H1 2022

Post by pohl4711 »

Windows Avx2
And this is not the issue. The engine is running. And playing.
Most GUIs dont care about PV-lines and ponder-moves, they just print them out but do not verify the moves. But cutechess-cli checks the moves and prints a warning.
So, it is an engine bug. But in most GUIs, it will be not recognized.


I checked the sse-Windows Binary, too. The same bug.
Wolfgang
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Re: New engine releases & news H1 2022

Post by Wolfgang »

Angle wrote: Fri May 27, 2022 10:47 am
Guenther wrote: Fri May 27, 2022 10:40 am
Angle wrote: Fri May 27, 2022 7:24 am After a long break, Ivan Maklyakov released a new version of his engine Uralochka 3.35a The expected rating of the single-threaded version is 3200-3250 ELO (according to the CCRL scale)! You can download binaries for SSE, AVX2, and AVX512 (Windows & Linux) and read more information (in Russian) here:
http://sdchess.ru/news.htm
With or w/o source code?
I only know what is shared on the page of Sergey and Dmitry Kudryavtsev (sdchess.ru). Only binaries are posted there. I'll ask them if the author provides the source code.
Besides the source code it would be fine to know more about the embedded NN(UE).
"Own" net or based on another engines net and if yes on which?

Interesting engine btw., my first "inofficial" tests (non CEGT!!) with 2'+1" showed a performance of ~~3240 on our scale, which would mean close to 3400 (!!) CCRL. But 200 games only...

And no problems here and at my colleague Jörg Burwitz with Shredder Classic, Arena, Banksia and Cutechess (w/o "cli"). I also tried Banksia on Linux, no problems too 8-)
Best
Wolfgang
CEGT-Team
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connor_mcmonigle
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Full name: Connor McMonigle

Re: New engine releases & news H1 2022

Post by connor_mcmonigle »

I've not looked at it very closely, but on initial inspection, mostly looking at the function names, arguments and where it spends time, it doesn't seem an obvious clone. In general, it seems rather unoptimized (spending more time on move generation than accumulator updates/forward propagation!). Regardless, some explanation from the author regarding where it originated from and how its network was trained would probably be appreciated.

Code: Select all

  10.71%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] TranspositionTable::load                                                                                                                              
   6.47%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Moves::gen_quiet                                                                                                                                      
   6.45%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Neural::accum_piece_add                                                                                                                               
   5.82%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Neural::accum_piece_remove                                                                                                                            
   5.55%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Board::move_do                                                                                                                                        
   4.74%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Game::search                                                                                                                                         
   3.86%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Moves::gen_kills                                                                                                                                      
   3.75%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Bitboards::poplsb                                                                                                                                     
   3.49%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] TranspositionTable::prefetch                                                                                                                          
   3.44%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Neural::accum_predict                                                                                                                                 
   3.40%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Moves::get_next                                                                                                                                       
   3.35%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] __memmove_avx_unaligned_erms                                                                                                                          
   3.19%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Neural::accum_piece_add                                                                                                                               
   3.01%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] std::__introsort_loop<__gnu_cxx::__normal_iterator<Move*, std::vector<Move, std::allocator<Move> > >, long, __gnu_cxx::__ops::_Iter_comp_iter<Moves::g▒
   2.91%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Moves::attacks_all                                                                                                                                    
   2.79%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Neural::accum_piece_add                                                                                                                               
   2.45%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Moves::moves_pawn                                                                                                                                     
   2.40%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Magic::index                                                                                                                                          
   2.25%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Moves::see                                                                                                                                            
   1.99%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] TranspositionTable::save                                                                                                                              
   1.88%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] std::__insertion_sort<__gnu_cxx::__normal_iterator<Move*, std::vector<Move, std::allocator<Move> > >, __gnu_cxx::__ops::_Iter_comp_iter<Moves::get_nex▒
   1.84%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Moves::kills_pawn                                                                                                                                     
   1.30%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Board::check_draw                                                                                                                                     
   1.09%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Moves::init_generator                                                                                                                                 
   1.05%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Board::move_undo                                                                                                                                      
   1.05%  Uralochka3.35a-  [kernel.kallsyms]    [k] mutex_spin_on_owner                                                                                                                                   
   0.95%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Moves::add                                                                                                                                            
   0.95%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Game::quiescence                                                                                                                                      
   0.87%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Moves::SEE                                                                                                                                            
   0.72%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Moves::moves_king                                                                                                                                     
   0.53%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Moves::get_history                                                                                                                                    
   0.51%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Board::is_check                                                                                                                                       
   0.48%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Board::color                                                                                                                                          
   0.39%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Board::is_attacked                                                                                                                                    
   0.38%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Bitboards::bit_clear                                                                                                                                  
   0.37%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Moves::add                                                                                                                                            
   0.28%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Bitboards::bit_set                                                                                                                                    
   0.24%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] TranspositionTable::TranspositionTable                                                                                                                
   0.23%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] __memmove_avx_unaligned                                                                                                                               
   0.23%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] std::vector<TTCell, std::allocator<TTCell> >::_M_default_append                                                                                       
   0.21%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Neural::accum_all_pieces                                                                                                                              
   0.17%  Uralochka3.35a-  Uralochka3.35a-avx2  [.] Board::moves_init    
Frank Quisinsky
Posts: 6888
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Re: New engine releases & news H1 2022

Post by Frank Quisinsky »

Hi there,

from 0 to 100 ... viewpoint awareness level!
Again an interesting story!

Would like to have more information about the person and development.

Best
Frank
expositor
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Re: New engine releases & news H1 2022

Post by expositor »

Expositor 2WN29
https://github.com/expo-dev/expositor/r ... /tag/2WN29

This version is roughly 100 Elo stronger than 2WQ23 (estimating from Cute Chess results).
Guenther
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Re: New engine releases & news H1 2022

Post by Guenther »

Smallbrain (new) 1.0 - 1.2
author: Max Allendorf (DEU)
https://github.com/Disservin/Smallbrain

Expositor 2WN29
https://github.com/expo-dev/expositor
https://rwbc-chess.de

[Trolls n'existent pas...]
expositor
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Re: New engine releases & news H1 2022

Post by expositor »

Guenther found a linktime error with Windows builds that causes stack overflows, so I've re-released 2WN29: https://github.com/expo-dev/expositor/r ... /tag/2WN29

So sorry about that, everyone!
Guenther
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Re: New engine releases & news H1 2022

Post by Guenther »

https://rwbc-chess.de

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Guenther
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Re: New engine releases & news H1 2022

Post by Guenther »

https://rwbc-chess.de

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voffka
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Full name: Volodymyr Shcherbyna

Re: Igel 3.1.0

Post by voffka »

Image

Igel 3.1.0 at https://github.com/vshcherbyna/igel/releases/tag/3.1.0 (official executable binaries for Windows and IGN net).

This release brings adequate improvement in strength.

What's new:

- Scale eval at 1280
- More aggressive pruning of quiets when not improving
- Use 150 cp when razoring
- Avoid tt cutoff for rule50
- Improve statistics in SMP mode
- New network trained using 16B of d5-d8 data from Igel HCE/ign-1-139b702b with d16 validation using ign-1-139b702b

Regression run against Igel 3.0.5:

Long Time Control With Increment

Code: Select all

ELO   | 30.13 +- 2.64 (95%)
CONF  | 60.0+0.60s Threads=1 Hash=64MB
GAMES | N: 20000 W: 3906 L: 2176 D: 13918
P.S. In case of any questions please ask them in the main Igel thread at https://talkchess.com/forum3/viewtopic. ... 06#p927006