A multiprocessor architecture for the massively parallel model GCA

The GCA (global cellular automata) model consists of a collection of cells which change their states synchronously depending on the states of their neighbors like in the classical CA model. In differentiation to the CA model the neighbors are not fixed and local, they are variable and global. The GCA model is applicable to a wide range of parallel algorithms. In this paper, a multiprocessor architecture for the massively parallel GCA model is presented. In contrast to a special purpose implementation of a GCA algorithm the multiprocessor system allows the implementation in a flexible way through programming. The architecture mainly consists of a number of cell processors and a network. The cell processors are dedicated RISC processors, the network is a crossbar implemented with multiplexers. Only read-accesses through the network are necessary in the GCA model leading to a simplified structure. A system with 32 processors was implemented as a prototype on a FPGA. The analysis and implementation results have shown that the performance of the system scales very well with the number of processors

1. Introduction

The GCA (Global Cellular Automata) model [8] is an extension of the classical CA (Cellular Automata) model [9]. In the CA model the cells are arranged in a fixed grid with fixed connections to their local neighbors. Each cell computes its next state by the application of a local rule depending on its own state and the states of its neighbors. The data accesses to the neighbors states are read-only and therefore no write conflicts can occur. The rule can be applied to all cells in parallel and therefore the model is inherently massively parallel. The CA model is suited to all kind of applications with local communication, like physical fields, lattice-gas models, models of growth, moving particles, fluid flow, routing problems, picture processing, genetic algorithms, and cellular neural networks.