Monte-Carlo Tree Search (MCTS) is a new best-first search method that started a revolution in the field of Computer Go. Parallelizing MCTS is an important way to increase the strength of any Go program. In this article, we discuss three parallelization methods for MCTS: leaf parallelization, root parallelization,and tree parallelization. To be effective tree parallelization requires two techniques: adequately handling of (1) local mutexes and (2) virtual loss. Experiments in 1313 Go reveal that in the program Mango root parallelization may lead to the best results for a specific time setting and specific program parame- ters. However, as soon as the selection mechanism is able to handle more adequately the balance of exploitation and exploration, tree paralleliza- tion should have attention too and could become a second choice for parallelizing MCTS. Preliminary experiments on the smaller 99 board provide promising prospects for tree parallelization.