Hybrid CPU-GPU Simulation of Hierarchical Adaptive Random Boolean Networks
Random boolean networks (RBNs) as models of gene regulatory networks are widely studied by the means of computer simulation to explore interconnections between their topology, regimes of functioning and patterns of information processing. Direct simulation of random boolean networks is known to be computationally hard because of the exponential growth of attractor lengths with an increase of a network size. In this paper, we propose hybrid CPU-GPU algorithm for parallel simulation of hierarchical adaptive RBNs. The rules of evolution of this type of RBN makes it possible to parallelize calculations both for different subnetworks and for different nodes while updating their states. In the experimental part of the study, we explore the efficiency of OpenMP and CPU-GPU algorithms for different sizes of networks and configurations of hierarchy. The results show that a hybrid algorithm performs better for a smaller number of subnetworks while OpenMP version may be preferable for a limited number of nodes in each subnetwork.
This research is financially supported by The Russian Science Foundation, Agreement 17-71-30029 with co-financing of Bank Saint Petersburg.
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