Exploiting the parallelism of large-scale application-layer networks by adaptive GPU-based simulation
Philipp Andelfinger,Hannes Hartenstein +1 more
- pp 3471-3482
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TLDR
A GPU-based simulator engine that performs all steps of large-scale network simulations on a commodity many-core GPU and adapts its configuration at runtime in order to balance parallelism and overheads to achieve high performance for a given network model and scenario is presented.Abstract:
We present a GPU-based simulator engine that performs all steps of large-scale network simulations on a commodity many-core GPU. Overhead is reduced by avoiding unnecessary data transfers between graphics memory and main memory. On the example of a widely deployed peer-to-peer network, we analyze the parallelism in large-scale application-layer networks, which suggests the use of thousands of concurrent processor cores for simulation. The proposed simulator employs the vast number of parallel cores in modern GPUs to exploit the identified parallelism and enables substantial simulation speedup. The simulator adapts its configuration at runtime in order to balance parallelism and overheads to achieve high performance for a given network model and scenario. A performance evaluation for simulations of networks comprising up to one million peers demonstrates a speedup of up to 19.5 compared with an efficient sequential implementation and shows the effectiveness of the runtime adaptation to different network conditions.read more
Citations
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A Survey on Agent-based Simulation Using Hardware Accelerators
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Time Warp on the GPU: Design and Assessment
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TL;DR: This work presents the design and implementation of an optimistic fully GPU-based parallel discrete-event simulator based on the Time Warp synchronization algorithm, and shows that in most cases, the increase in parallelism when using optimistic synchronization significantly outweighs the increased overhead for state keeping and rollbacks.
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A Survey on Agent-based Simulation using Hardware Accelerators
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Performance Evaluation of Priority Queues for Fine-Grained Parallel Tasks on GPUs
TL;DR: This work performs a performance evaluation of GPU-based priority queue implementations for two applications: discrete-event simulation and parallel A* path searches on grids and presents performance measurements covering linear queue designs, implicit binary heaps, splay trees, and a GPU-specific proposal from the literature.
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Transitioning Spiking Neural Network Simulators to Heterogeneous Hardware
TL;DR: This paper proposes a transition approach for CPU-based SNN simulators to enable the execution on heterogeneous hardware with only limited modifications to an existing simulator code base, and without changes to model code.
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