No abstract

Large classes of game theoretic problems seem to defy attempts of finding polynomial-time algorithms while analyzing large amounts of data. This premise leads naturally to the possibility of using efficient parallel computing implementations when seeking exact solutions to some of these problems. Although alpha beta algorithms for more than one-player game-tree searches show moderate parallel performance, this paper sets forth an alpha beta strategy enhanced with transposition tables in order to offer satisfactory speedups on high performance servers. When the access to the transposition tables is done in low constant delay time, the achieved speedups should approach the theoretical upper bounds of the code parallelism. We tested the strategy on a well-known combinatorial game.

In this paper, we propose an efficient method for replicating large data volumes across servers and clusters (endpoints) having geographically heterogeneous locations. The endpoints may be placed in countries with different connectivity environments while the data is as large as 5 to 10 Terabytes. Our solution relies upon a dynamic algorithm which finds the optimal paths for the data replication process from the initial node to the rest of the endpoints. In the diffusion network the edges are valued so as to maximize the utility of stable connections possessing low latency and high speed. The dynamic assessment of utilities asserts our solutions' effectiveness in topologies undergoing frequently changing connectivity conditions.