FPGA Implementations of the Massively Parallel GCA Model
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 14 - Volume 15
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
The GCA-w Massively Parallel Model
PaCT '09 Proceedings of the 10th International Conference on Parallel Computing Technologies
A multiprocessor architecture for the massively parallel model GCA
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Multilane single GCA-w based expressway traffic model
ACRI'10 Proceedings of the 9th international conference on Cellular automata for research and industry
The massively parallel computing model GCA
Euro-Par 2010 Proceedings of the 2010 conference on Parallel processing
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We had introduced the massively parallel global cellular automata (GCA) model. Parallel algorithms derived from applications can be mapped straight forward onto this model. In this model a cell in the cell field is dynamically connected (access pattern, dynamic neighbourhood) to other cells. The model can be implemented by pointers stored in the cell state. Via these pointers, each cell has read access to any other cell in the cell field, and the pointers may be changed from generation to generation. We have investigated different types of the model in order of minimize hardware/software implementation cost. So we have classified the GCA into types with respect to space, time or data dependency of the access pattern. We have investigated a number of different GCA algorithms and found out, that in most cases a time dependent access pattern is sufficient. To find out the usefulness of the data dependent access pattern we constructed a sophisticated merge sort algorithm, in which the target addresses are computed in contrast to classical algorithms where the data elements are moved. It turned out, that we could not achieve a speed up which we expected compared to an algorithm implemented on the more simple time dependent model. This is another confirmation that it is sufficient to implement only the time and space dependent model and thus reduce the hardware/software implementation cost.