A General Framework for Heterogeneous Associative Logic Programming

Associative computation is characterized by search by content and data parallel computation. Search by content paradigm is natural to scalable high performance heterogeneous computing since use of tagged data makes data independent of implicit addressing mechanisms. In this paper, we present an algebra for associative logic programming, an associative resolution scheme, and a generic framework of associative abstract instruction set. The model is based on integration of data alignment and use of two types of bags: data element bags and filter bags of boolean values to select and restrict computation on data elements. Use of filter bags integrated with data alignment reduces the computation and data transfer overhead; use of tagged data reduces overhead of preparing data before data transmission. The abstract instruction set has been illustrated by an example. Performance results for a simulation in a homogeneous address space are presented.