  In this study, we explore the efficiency of the Monte Carlo Tree Search
(MCTS), a prominent decision-making algorithm renowned for its effectiveness in
complex decision environments, contingent upon the volume of simulations
conducted. Notwithstanding its broad applicability, the algorithm's performance
can be adversely impacted in certain scenarios, particularly within the domain
of game strategy development. This research posits that the inherent branch
divergence within the Da Vinci Code board game significantly impedes
parallelism when executed on Graphics Processing Units (GPUs). To investigate
this hypothesis, we implemented and meticulously evaluated two variants of the
MCTS algorithm, specifically designed to assess the impact of branch divergence
on computational performance. Our comparative analysis reveals a linear
improvement in performance with the CPU-based implementation, in stark contrast
to the GPU implementation, which exhibits a non-linear enhancement pattern and
discernible performance troughs. These findings contribute to a deeper
understanding of the MCTS algorithm's behavior in divergent branch scenarios,
highlighting critical considerations for optimizing game strategy algorithms on
parallel computing architectures.
