Most recent implementationa of Persistent Hash Table

[glav]

I would like to know which ones are the Stockfish derivatives that allow you to save the hash table to disk. Ideally, I am looking for the most reecnt stockfish nnue version clones.
Thanks in advance.

[whelanh]

Although it's base is Cfish, this clone (which uses its own net) allows a persistent transposition table: https://github.com/Sopel97/FatTitz This engine shows similar strength to Stockfish (see

See also: Brainlearn (https://github.com/amchess/BrainLearn)

[glav]

Thank you! BTW, nice projects.
Also, I was intrigued by the Persistent Learning Algoritm in Brain learn. Do you know how it differs from the classical hash table saving in FatTitz?
Thanks again for your help and assistance,

[Sopel]

Brainlearn's "persistent learning algorithm" is more intelligent in what it stores, has no size limit, and works all the time. FatTitz's is just a way to store the TT, so has finite size, a simple replace policy, and is reset between games. I'd say Brainlearn's approach is better if you want to analyze mostly opening positions in large quantities and have the engine "learn" seemlessly; FatTitz better if you want to preserve the hash table of the current search of a specific position and resume it later (though it doesn't preserve everything, for example histories or low ply TT entries)

[glav]

Brainlearn's "persistent learning algorithm" is more intelligent in what it stores, has no size limit, and works all the time. FatTitz's is just a way to store the TT, so has finite size, a simple replace policy, and is reset between games. I'd say Brainlearn's approach is better if you want to analyze mostly opening positions in large quantities and have the engine "learn" seemlessly; FatTitz better if you want to preserve the hash table of the current search of a specific position and resume it later (though it doesn't preserve everything, for example histories or low ply TT entries)

Thanks for your explanation. So, if I read you correctly, you can achieve with the BrainLearn Persistent learning everything you can do with the FatTitz hash saving capability. I mean in terms of a multi-session position analysis. Moreover, you don't need to have a separate hash table for each root position, making its use much less demanding in terms of disk-space.
Thanks again for eleborating on this important topic.