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• Simulation of complex networks. • Simulation of dissemination protocols. • Parallel and Distributed Simulation (PADS). • Adaptive self-clustering for load-balancing. Complex networks are an important methodology to model several (if not all) aspects of the real world, in which multiple entities interact, in some way. While many aspects related to such interactions can be investigated by looking at the general mathematical metrics of the networks, an alternative approach lies in the simulation of some application protocol on top of (large scale) complex networks. In this paper, we present a study on this intricate problem. The complexity of the simulation is due to the need to model all the interactions among network nodes. We focus on discrete-event simulation, a simulation methodology that enables both sequential (i.e. monolithic) and Parallel And Distributed Simulation (i.e. PADS) approaches. We discuss the performance and scalability requirements that the simulator should have. We also introduce a case study based on the agent-based simulation of gossip dissemination on top of a complex network. To demonstrate the viability of this simulation technique, we focus on a tool we built to simulate complex networks. The tool exploits adaptive partitioning mechanisms, which are essential to reduce the communication overhead in the PADS. An experimental evaluation has been conducted using different network topologies and simulator setups. Results demonstrate the feasibility of the approach to simulate complex networks. [ABSTRACT FROM AUTHOR]

• A new mesh-based method for designing unstructured mesh particle-in-cell simulations. • Implementation details of a library, PUMIPic, using the mesh-based approach on GPUs. • Analysis of mesh-based scaling on a pseudo physics simulation built with PUMIPic. Unstructured mesh particle-in-cell, PIC, simulations executing on the current and next generation of massively parallel systems require new methods for both the mesh and particles to achieve performance and scalability on GPUs. The traditional approach to implementing PIC simulations defines data structures and algorithms in terms of particles with a full copy of the unstructured mesh on every process. To effectively scale the unstructured mesh and particles, mesh-based PIC uses the unstructured mesh as the predominant data structure with the particles stored in terms of the mesh entities. This paper details the PUMIPic library, a framework for developing efficient and performance-portable mesh-based PIC simulations on GPU systems. A pseudo physics simulation based on a five-dimensional gyro-kinetic code for modeling plasma physics is used to examine the performance of PUMIPic. Scaling studies of the unstructured mesh partition and number of particles are performed up to 4096 nodes of the Summit system at Oak Ridge National Laboratory. The studies show that mesh-based PIC can utilize a partitioned mesh and maintain scaling up to system limitations. [ABSTRACT FROM AUTHOR]