A scalable configurable architecture for the massively parallel GCA model

Abstract

The global cellular automata model (GCA) is a massively parallel computation model which extends the classical cellular automata model (CA) with dynamic global neighbors. We present for that model a data parallel architecture which is scalable in the number of parallel pipelines and which uses application specific operators (adapted operators). The instruction set consists of control and RULE instructions. A RULE computes the next cell contents for each cell in the destination object. The machine consists of P pipelines. Each pipeline has an associated primary memory bank and has access to the global memory (real or emulated multiport memory). The diffusion of particles was used as an example in order to demonstrate the adaptive operators, the machine programming and its performance. Particles which point to each other within a defined neighborhood search space are interchanged. The pointers are modified in each generation by apseudo random function. The machine with up to 32 pipelines was synthesized for an Altera FPGA for that application.