The study of dynamics on complex networks, and specifically synchronization has diverse real world applications, including animal consensus , in the brain , in chemical oscillations , and even the power grid [4, 5]. Thus understanding why a system synchronizes (or doesn’t) may allow us to prevent disaster caused by loss of synchronization (such as in the power grid), or to intervene effectively to prevent ex- cessive synchronization (such as happens with Parkinson’s disease tremors ). The dynamics of complex systems are studied both through model simulations and through data analysis. Both types of study can be computationally expensive because there may be many thousands, millions or even billions of nodes in a complex network. Simulation studies quickly become unfeasible on such large systems, and analysis can become prohibitively long. Distributed computing offers an opportunity to tackle both problems, and push the computational boundaries further in both cases. The proposed research will be two pronged, one student project will focus on creating a general open source distributed computing framework which will take the particular model and net- work as input thus. The other will project will focus on distributed computing per- taining to entropic analysis of dynamical systems, as various types of Shannon entropy [6, 7, 8, 9] are often used for such analysis.
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