Parallel retrograde analysis on different architecture

Abstract

Retrograde analysis is an efficient exhaustive search method. It is a powerful tool that can be used in solving problems where end states have known values but starting states do not. It has been widely used to solve mathematically-precise games such as chess endgames, and is potentially usable in energy-minimization problems. With increasing computing power, both in speed and storage capacity, retrograde analysis will become more and more useful. This paper looks at successful applications to games, the challenges ahead and the modifications that are required to utilize distributed hardware. The power and the usefulness of retrograde analysis are still limited by the computing resources one has access to. Today, the best sequential retrograde algorithms are capable of solving problems with about 10/sup 9/ states in a few hours on a standard personal computer. Bigger problems need more powerful computers, or take much longer to solve, or are simply out of the reach of today's technologies. Introducing parallelism to retrograde analysis is a natural way to attack the bigger problems. There are today three main architectures available for doing parallel retrograde analysis, namely symmetric multiprocessor (SMP) systems, high-speed network-based distributed systems and Internet-based distributed systems. In this paper, we discuss some of the key issues in doing parallel retrograde analysis on these different architectures. Technical challenges are addressed in detail, as well as some examples and proposals. These examples and proposals are drawn from various board games, but the ideas can be applied to other problem domains.