A Mapping Heuristic for Minimizing Message Latency in Massively Distributed MCTS

Abstract

We propose a topology-aware heuristic that significantly reduces the message latency for search trees of tree parallel Monte-Carlo Tree Search. There exist many communication-aware and topology-aware mappings. However, those mappings are not applicable to the hash driven parallel search techniques. This is because in hash driven parallel search each graph/tree node is randomly distributed based on a hash function and each edge is also randomly connected, so each computation cluster only knows about the tasks that are being executed on themselves, so it is not possible to do dynamic load balancing according to the current status of the network. To cope with that, we devise an heuristic based on the depth of each search tree node and the betweenness centrality of each computational cluster of the network topology. Our experimental results show that we can reduce the average message latency by 15% to 35%.