Fast bio-inspired computation using a GPU-based systemic computer

AbstractBiology is inherently parallel. Models of biological systems and bio-inspired algorithms also share this parallelism, although most are simulated on serial computers. Previous work created the systemic computer – a new model of computation designed to exploit many natural properties observed in biological systems, including parallelism. The approach has been proven through two existing implementations and many biological models and visualizations. However to date the systemic computer implementations have all been sequential simulations that do not exploit the true potential of the model. In this paper the first ever parallel implementation of systemic computation is introduced. The GPU Systemic Computation Architecture is the first implementation that enables parallel systemic computation by exploiting the multiple cores available in graphics processors. Comparisons with the serial implementation when running two programs at different scales show that as the number of systems increases, the parallel architecture is several hundred times faster than the existing implementations, making it feasible to investigate systemic models of more complex biological systems.

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