Zeta Chess

Zeta Dva v0405 + Zeta v099q Blueprint

I got everything I need on paper:

  • Board Representation: Quad-Bitboards
  • Move Generation: vectorized Kogge-Stone
  • Search Algorithm: RMO - parallel AlphaBeta
  • Selective Search: NNOM++*
  • Evaluation: SF 13 NNUE**
  • Parallel Layers: direction-wise, square-wise, NN-wise**, AB-worker-wise, PV-Splitting-device-wise

now to find some time to implement this blueprint, first on CPU+AVX2 then on GPU.

*NNOM++ - Move Ordering Neural Networks: use SF 13 NNUE for selective search in non-QS search.

** still need to figure how to use 64 gpu threads of a worker during NNUE inference.

Computer Timeline

...show me your computer timeline, and I tell who you are ;)

 1987 - Atari 800 XE with XC12 datasette (family home computer, shared with older
siblings), BASIC 1988 - used Atari VCS game console ~1989 - older brother with Copam PC-AT, AMD 286 8/16MHz, 640KB RAM, 20MB HDD, Hercules
Graphics Card (TL ET-1000?), IBM DOS 3.3 1990 - my own Amiga 500, A520 RF, later A501 512KB memory upgrade, AmigaOS 1.3 1996 - used PC, 486DX 33MHz, 32MB RAM, 120MB HDD, 14" CRT, 640x480px, Hercules
Dynamite 128 2MB video card, MS-DOS 6.2.x+Win95 1998 - AMD K5 100@133MHz, 15" CRT, 800x600px, Diamond Viper 330 (Riva 128) 4MB, Win98 ~1999 - upgrade to Pentium 233MHz, SuseLinux+Win98 2001 - AMD K6-2+ 550@600MHz, 512MB RAM, 19" CRT, 1600x1200px, SuseLinux+Win2000 2003 - Pentium 4 1.8@2.95GHz, 1GB RAM, 2x17" CRT, 1024x768px, Nvidia GeForce 4 MX440
64MB, SuseLinux+Win2000/WinXP 2008 - Lenovo Thinkpad T61 (type:7659), 2x2GHz, 4GB RAM, 14.1" 1440x900px, X3100
iGPU, UbuntuLinux 2008 - MaschinaI (first dedicated machine for GPU and chess programming purpose) AMD K8 X2 2x2.5GHz, 8GB RAM, 22" LCD, 1920x1080px, Nvidia 8800 GT 512MB,
UbuntuLinux+WinXP 2012 - MaschinaII, used AMD K10 X4 4x1.8GHz, 8GB RAM, Asus Crosshair Formula II MoBo
with 3xPCIe, Nvidia GTX 580, K20, ++, UbuntuLinux+WinXP ~2013 - ~2015 - GPU and chess programming hiatus 2015 - MaschinaIII, same as MaschinaII, with AMD Fury X 4GB GPU, ++, UbuntuLinux+Win7 ~2019 - Laptop + Google Cloud Computing with Nvidia V100 >2021 - MaschinaIV, used Core i5-6500 4x3.2GHz (Skylake 14nm with AVX2 from 2015),
32GB RAM, Intel HD530 iGPU, Nvidia GTX 750, AMD RX 550 GPU, UbuntuLinux+Win10

++ = a couple of used GPUs with different architecture to tinker with GPGPU.

I tried in 2019 to switch to Cloud Computing and stop collecting GPU architectures, but it did not work out, hence I did set up Maschina #IV with some entry-level hardware to tinker with computer chess.

I intended in 2008 to drop the desktop PC and use just a laptop for my studies, but then came the desire to tinker with GPGPU.

Retrospectively, the 90s were jumpy for me, went from 68k@7MHz to 586@233MHz.

...still run some oldies via emulators on my laptop.

The Next Big Thing in Computer Chess?

We are getting closer to the perfect chess oracle, a chess engine with perfect play and 100% draw rate.

The Centaurs reported already that their game is dead, Centaurs participate in tournaments and use all kind of computer assist to choose the best move, big hardware, multiple engines, huge opening books, end game tables, but meanwhile they get close to the 100% draw rate with common hardware, and therefore unbalanced opening books were introduced, where one side has an slight advantage, but again draws.

The #1 open source engine Stockfish lowered in the past years the effective branching factor of the search algorithm from ~2 to ~1.5 to now ~1.25, this indicates that the selective search heuristics and evaluation heuristics are getting closer to the optimum, where only one move per position has to be considered.

About a decade ago it was estimated that with about ~4000 Elo points we will have a 100% draw rate amongst engines on our computer rating lists, now the best engines are in the range of ~3750 Elo (CCRL), what translates estimated to ~3600 human FIDE Elo points (Magnus Carlsen is rated today 2852 Elo in Blitz). Larry Kaufman (grandmaster and computer chess legenda) mentioned that with the current techniques we might have still ~50 Elo to gain, and it seems everybody waits for the next bing thing in computer chess to happen.

We replaced the HCE, handcrafted evaluation function, of our computer chess engines with neural networks. We train now neural networks with billions of labeled chess positions, and they evaluate chess positions via pattern recognition better than what a human is able to encode by hand. The NNUE technique, neural networks used in AlphaBeta search engines, gave an boost of 100 to 200 Elo points.

What could be next thing, the next boost?

If we assume we still have 100 to 200 Elo points until perfect play (normal chess with standard opening and a draw), if we assume an effective branching factor ~1.25 with HCSH, hand crafted search heuristics, and that neural networks are superior in this regard, we could imagine to replace HCSH with neural networks too and lower the EBF further, closer to 1.

Such an technique was already proposed, NNOM++. Move Ordering Neural Networks, but until now it seems that the additional computation effort needed does not pay off.

What else?

We use neural networks in the classic way for pattern recognition in nowadays chess engines, but now the shift is to pattern creation, the so called generative AIs. They generate text, source code, images, audio, video and 3D models. I would say the race is now up for the next level, an AI which is able to code an chess engine and outperforms humans in this task.

An AI coding a chess engine has also a philosophical implication, such an event is what the Transhumanists call the takeoff of Technological Singularity, when the AI starts to feed its own development in an feedback loop and exceeds human understanding.

Moore's Law has still something in pipe, from currently 5nm to 3nm to maybe 2nm and 1+nm, so we can expect even larger and more performant neural networks for generative AIs in future. Maybe in ~6 years there will be a kind of peak or kind of silicon sweetspot (current transistor density/efficiency vs. needed financial investment in fab process/research), but currently there is so much money flowing into this domain that progress for the next couple of years seems assured.

Interesting times ahead.

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