Flooding Peer-to-Peer (P2P) networks form the basis of services such as the electronic currency system Bitcoin. The decentralized architecture enables robustness against failure. However, knowledge of the network's topology can allow adversaries to attack specific peers in order to, e.g., isolate certain peers or even partition the network. Knowledge of the topology might be gained by observing the flooding process, which is inherently possible in such networks, and performing a timing analysis on the observations. In this paper we present a timing analysis method that targets flooding P2P networks and show its theoretical and practical feasibility. A validation in the real-world Bitcoin network proves the possibility of inferring network links of actively participating peers with substantial precision and recall (both ∼ 40 %), potentially enabling attacks on the network. Additionally, we analyze the countermeasure of trickling and quantify the tradeoff between the effectiveness of the countermeasure and the expected performance penalty. The analysis shows that inappropriate parametrization can actually facilitate inference attacks.

We present a simulation model of the Bitcoin peer-to-peer network, a widely deployed distributed electronic currency system. The model enables evaluations of the feasibility and cost of attacks on the Bitcoin network at full scale of 6,000 nodes. The simulation model is based on unmodified code from core segments of the Bitcoin reference implementation used by 99% of nodes. Parametrization of the model is performed based on large-scale measurements of the real-world network. We present preliminary validation results showing a reasonable correspondence of the propagation of messages in the Bitcoin network compared with simulation results. We apply the model to study the feasibility of a partitioning attack on the network and show that the attack is sensitive to the churn of the attacking nodes.

Graphics processing units (GPUs) have been shown to be wellsuited to accelerate agent-based simulations. A fundamental challenge in agent-based simulations is the resolution of conflicts arising when agents compete for simulated resources, which may introduce substantial overhead. A variety of conflict resolution methods on the GPU have been proposed in the literature. In this paper, we systematize and compare these methods and propose two simple new variants. We present performance measurements on the example of the well-known segregation model. We show that the choice of conflict resolution method can substantially affect the simulation performance. Further, although methods in which agents actively indicate their interest in a resource require the use of costly atomic operations, these methods generally outperform the alternatives.

Due to decelerating gains in single-core CPU performance, computationally expensive simulations are increasingly executed on highly parallel hardware platforms. Agent-based simulations, where simulated entities act with a certain degree of autonomy, frequently provide ample opportunities for parallelisation. Thus, a vast variety of approaches proposed in the literature demonstrated considerable performance gains using hardware platforms such as many-core CPUs and GPUs, merged CPU-GPU chips as well as FPGAs. Typically, a combination of techniques is required to achieve high performance for a given simulation model, putting substantial burden on modellers. To the best of our knowledge, no systematic overview of techniques for agent-based simulations on hardware accelerators has been given in the literature. To close this gap, we provide an overview and categorization of the literature according to the applied techniques. Since at the current state of research, challenges such as the partitioning of a model for execution on heterogeneous hardware are still a largely manual process, we sketch directions for future research towards automating the hardware mapping and execution. This survey targets modellers seeking an overview of suitable hardware platforms and execution techniques for a specific simulation model, as well as methodology researchers interested in potential research gaps requiring further exploration.

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