In recent years there has been much interest in the Monte Carlo tree search algorithm, a new, adaptive, randomized optimization algorithm. In fields as diverse as Artificial Intelligence, Operations Research, and High Energy Physics, research has established that Monte Carlo tree search can find good solutions without domain dependent heuristics. However, practice shows that reaching high performance on large parallel machines is not so successful as expected. This paper proposes a new method for parallel Monte Carlo tree search based on the pipeline computation pattern.
 Monte-Carlo planning algorithms, such as UCT, select actions at each decision epoch by intelligently expanding a single search tree given the available time and then selecting the best root action. Recent work has provided evidence that it can be advantageous to instead construct an ensemble of search trees and to make a decision according to a weighted vote. However, these prior investigations have only considered the application domains of Go and Solitaire and were limited in the scope of ensemble configurations considered. In this paper, we conduct a more exhaustive empirical study of ensemble Monte-Carlo planning using the UCT algorithm in a set of six additional domains. In particular, we evaluate the advantages of a broad set of ensemble configurations in terms of space and time efficiency in both parallel and single-core models. Our results demonstrate that ensembles are an effective way to improve performance per unit time given a parallel time model and performance per unit space in a single-core model. However, contrary to prior isolated observations, we did not find significant evidence that ensembles improve performance per unit time in a single-core model. Parallelizing Monte Carlo tree search (MCTS) has been considered to be a way to improve the strength of Computer Go programs. In this paper, we analyze the performance of two root parallelization methods: the standard strategy based on average selection and our new strategy based on majority voting. As a starting code base, we used Fuego, which is one of the best programs available. Our experimental results with 64 central processing unit (CPU) cores show that majority voting outperforms average selection. Additionally, we show through an extensive analysis that root parallelization has limitations.