Brook for GPUs: stream computing on graphics hardware
ACM SIGGRAPH 2004 Papers
Development, evaluation and benchmarking of simulation software for biomolecule-based computing
Natural Computing: an international journal
Axioms for bigraphical structure
Mathematical Structures in Computer Science
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
GPU acceleration of cutoff pair potentials for molecular modeling applications
Proceedings of the 5th conference on Computing frontiers
Rapid evaluation and evolution of neural models using graphics card hardware
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Systemic computation: A model of interacting systems with natural characteristics
International Journal of Parallel, Emergent and Distributed Systems - Emergent Computation
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Genetic and Evolutionary Computation Conference
Crash-proof systemic computing: a demonstration of native fault-tolerance and self-maintenance
ACST '08 Proceedings of the Fourth IASTED International Conference on Advances in Computer Science and Technology
CMSB'04 Proceedings of the 20 international conference on Computational Methods in Systems Biology
The continuous differential ant-stigmergy algorithm for numerical optimization
Computational Optimization and Applications
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Biology 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.