Zeta Chess

Zeta v099l

Zeta v099k did not scale well on Nvidia Pascal and Turing gpus, so I wrote a
patch to fix this issue, and released Zeta v099l:

https://github.com/smatovic/Zeta/release

On Pascal it runs now 4 workers per Compute Unit and on Turing 2 workers per
Compute Unit during guessconfigx.

According to Nvidia papers, Turing should have 16 wide SIMD units, with four
units per Compute Unit, but according to my tests I can only speculate that the
integer units are 32 wide, not 16, with two of them per Compute Unit.

During benchmarks on other systems it was shown again that some Windows OS have
an OS gpu timeout, so you may want to apply this registry update on your Windows
machine:

https://zeta-chess.app26.de/downloads/SetWindowsGPUTimeoutTo20s.reg

Download, double-click and reboot OS to increase gpu timeout from 2 to 20 seconds.

If you want to run an SMP benchmark for your gpu, I suggest to increase the gpu
timeout to 400 seconds:

https://zeta-chess.app26.de/downloads/SetWindowsGPUTimeoutTo400s.reg

Neural Networks on GPU

Currently there is much going on with neural networks for chess. With GiraffeAlphaZero, and its open source adaptation LC0 (Leela Chess Zero), it was shown that, with enough horse power, artificial neural networks are competitive in computer chess.

Currently LC0 uses an MCTS, Monte-Carlo Tree Search, approach with GPU as neural network accelerator for position evaluation.

My own experiments showed that AlphaBeta search is superior to MCTS, but current GPU architectures suffer from host-device latency, so you have to couple tasks to batches to be executed in one run on the GPU, not that conform with the serial nature of AlphaBeta.

With upcoming GPGPU architectures (or ANN accelerators) with less latency there might be AlphaBeta ANN engines possible...

Bye bye 8800 GT

Hmm,
my GPU workstation went broken, so I decommissioned my Maschina, time to say bye bye to my workhorse from 2008, the Nvidia 8800 GT
 

Nvidia 8800 GT

 

and the Asus Crosshair Formula II

Asus Crosshair Formula II

 

and the 8 GB GeIL Black Dragon Memory

GeIL Black Dragon Memory

 

not to forget, the water-cooled 'Beast' from 2015, AMD Fury X with 4096 cores, 8 TFLOPs and 4 GB HBM

The Beast - AMD Fury X

...we had a lot of fun, may you find a new, worthy owner on eBay...
 

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