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.
We present a state of the art implementation of the Monte Carlo Tree Search algorithm for the game of Go. Our Pachi software is currently one of the strongest open source Go programs, competing at the top level with other programs and playing evenly against advanced human players. We describe our implementation and choice of published algorithms as well as three notable original improvements: (1) an adaptive time control algorithm, (2) dynamic komi, and (3) the usage of the criticality statistic. We also present new methods to achieve efficient scaling both in terms of multiple threads and multiple machines in a cluster.  With the recent success of Monte-Carlo tree search algorithms in Go and other games, and the increasing number of cores in standard CPUs, the efficient parallelization of the search has become an important issue. We present a new lock-free parallel algorithm for Monte-Carlo tree search which takes advantage of the memory model of the IA-32 and Intel-64 CPU architectures and intentionally ignores rare faulty updates of node values. We show that this algorithm significantly improves the scalability of the Fuego Go program. FUEGO is both an open-source software framework and a state-of-the-art program that plays the game of Go. The framework supports developing game engines for full-information two-player board games, and is used successfully in a substantial number of projects. The FUEGO Go program became the first program to win a game against a top professional player in 9 × 9 Go. It has won a number of strong tournaments against other programs, and is competitive for 19 × 19 as well. This paper gives an overview of the development and current state of the FUEGO project. It describes the reusable components of the software framework and specific algorithms used in the Go engine.