Parallel Retrograde Analysis on a Distributed System

Abstract

Retrograde Analysis (RA) is an AI search technique used to compute endgame databases, which contain optimal solutions for part of the search space of a game. RA has been applied successfully to several games, but its usefulness is restricted by the huge amount of CPU time and internal memory it requires. We present a parallel distributed algorithm for RA that addresses these problems. RA is hard to parallelize efficiently, because the communication overhead potentially is enormous. We show that the overhead can be reduced drastically using message combining. We implemented the algorithm on an Ethernet-based distributed system. For one example game (awari), we have computed a large database in 50 minutes on 64 processors, whereas one machine took 40 hours (a speedup of 48). An even larger database (computed in 20 hours) would have required over 600 MByte of internal memory on a uniprocessor and would compute for many weeks.