GCA: a massively parallel model

We have previously introduced the massively parallel global cellular automata (GCA) model. Parallel algorithms derived from applications can be mapped straightforward 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.