To port an classic chess engine approach with an parallel Alphabeta algorithm like YBWC to an GPU architecutre would take a significant bunch of time, if it is even possible to port all well known computer chess techniques straight forward. And it is questionable if an Elo gain, by more computed nodes per second, is eaten up again by an higher branchingfactor due to an simpler implementation.
Zeta 098 and 097 make use of an Randomized Best First MiniMax Search, but my implementation makes excessive use of Global Memory and scales poorly.
At the very beginning of the project it was clear, that an Monte Carlo Tree Search would fit best for gpus. But until now there is no known engine that could make MCTS work well for Chess.
What is left, except to try to port an classic approach?
I could improve the performance of the BestFist search significantly by switching from GlobalMemory to LocalMemory and i could remove the randomness...another alternative would be to switch to MCAB, Monte Carlo Alphabeta...