Associative random access machines and data-parallel multiway binary-search join

Rapid advances in semiconductor technology have made it possible to build massively parallel processors. In addition, optical 3D storage and optical interconnections open a new opportunity due to inherent massive parallelism and non-interference of light beams. The approaches used in current parallel database research cannot take advantage of massive parallelism which can be provided by the emerging technologies, due to speedup and scaleup limitations.

In this paper, we present a computational paradigm for database machines which takes advantage of the opening opportunity for massive parallelism and discuss the validity and feasibility of the paradigm. The approach we take is based on associative computing and fine grained data parallelism which allow unlimited speedup and scaleup. Additionally, an asymptotically fast data-parallel join algorithm, which can efficiently deal with the joins in which multiple relations share a common join field, is presented. The algorithm is based on parallel sorting and parallel binary search, and performs a multiway join in O(Σs + Σ log r) where s is the cost of sorting an intermediate relation and r is the size of an input relation. The cost s of sorting is kept minimum by the algorithm.