Cellular automata machines: a new environment for modeling
Cellular automata machines: a new environment for modeling
The Cellular Processor Architecture CEPRA-1X and Its Configuration by CDL
IPDPS '00 Proceedings of the 15 IPDPS 2000 Workshops on Parallel and Distributed Processing
GCA: Global Cellular Automata. A Flexible Parallel Model
PaCT '01 Proceedings of the 6th International Conference on Parallel Computing Technologies
GCA: A Massively Parallel Model
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Implementation of the Massively Parallel Model GCA
PARELEC '04 Proceedings of the international conference on Parallel Computing in Electrical Engineering
Theory of Self-Reproducing Automata
Theory of Self-Reproducing Automata
Generated Horizontal and Vertical Data Parallel GCA Machines for the N-Body Force Calculation
ARCS '09 Proceedings of the 22nd International Conference on Architecture of Computing Systems
A scalable configurable architecture for the massively parallel GCA model
International Journal of Parallel, Emergent and Distributed Systems - Advances in Parallel and Distributed Computational Models
A Multiprocessor Architecture with an Omega Network for the Massively Parallel Model GCA
SAMOS '09 Proceedings of the 9th International Workshop on Embedded Computer Systems: Architectures, Modeling, and Simulation
A multiprocessor architecture for the massively parallel model GCA
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
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The GCA (Global Cellular Automata) model is a very interesting and flexible model which can be used to implement all kind of parallel algorithms. The GCA model consists of a field of cells similar the Cellular Automata model. Each cell has links to a set of remote cells which can be dynamically changed from generation to generation. A cell reads the remote neighbors' states and then changes its own state according to a local rule. The model is massively parallel because all cells can change their states independently and in parallel. We have investigated how the GCA model can be implemented efficiently in hardware using a Field Programmable Gate Array (FPGA) prototyping platform. We have implemented a fully parallel architecture where all cells operate fully in parallel and other architectures where the cells are stored in memories in order to handle a large number of cells. We are showing that in the fully parallel architecture a speed-up of around 190 is realistic on a modern FPGA platform compared to a software implementation on a PC. In the partially parallel architecture based on memories the speed-up will be lower but the number of cells is only restricted by the capacity of the memories.