Exploiting data parallelism for efficient execution of logic programs with large knowledge bases

Abstract

A model is presented which is designed to exploit the data parallelism present in associative computers for the efficient execution of logic programs with very large knowledge bases. A scheme is described for a logical data structure representation incorporating a direct interface between lists and vectors. This interface allows the partial integration of symbolic and numerical computation on existing associative supercomputers. A data parallel goal reduction algorithm which is almost independent of the number of clauses is discussed. This associative goal reduction scheme performs parallel clause pruning and binding of variables with a single occurrence. The associative property of the model effectively reduces the cost of shallow backtracking, deep backtracking, and garbage collection.<>