The cost of conservative synchronization in parallel discrete event simulations
Journal of the ACM (JACM)
GTW: a time warp system for shared memory multiprocessors
WSC '94 Proceedings of the 26th conference on Winter simulation
Parallel and Distribution Simulation Systems
Parallel and Distribution Simulation Systems
µsik " A Micro-Kernel for Parallel/Distributed Simulation Systems
Proceedings of the 19th Workshop on Principles of Advanced and Distributed Simulation
Discrete-event Execution Alternatives on General Purpose Graphical Processing Units (GPGPUs)
Proceedings of the 20th Workshop on Principles of Advanced and Distributed Simulation
Efficient Execution on GPUs of Field-Based Vehicular Mobility Models
Proceedings of the 22nd Workshop on Principles of Advanced and Distributed Simulation
An approach for the effective utilization of GP-GPUs in parallel combined simulation
Proceedings of the 40th Conference on Winter Simulation
Event-driven gate-level simulation with GP-GPUs
Proceedings of the 46th Annual Design Automation Conference
SCGPSim: a fast SystemC simulator on GPUs
Proceedings of the 2010 Asia and South Pacific Design Automation Conference
An analysis of queuing network simulation using GPU-based hardware acceleration
ACM Transactions on Modeling and Computer Simulation (TOMACS)
GPGPU-aided ensemble empirical-mode decomposition for EEG analysis during anesthesia
IEEE Transactions on Information Technology in Biomedicine
Multi-level Parallelism for Time- and Cost-Efficient Parallel Discrete Event Simulation on GPUs
PADS '12 Proceedings of the 2012 ACM/IEEE/SCS 26th Workshop on Principles of Advanced and Distributed Simulation
Massively parallel Modelling & Simulation of large crowd with GPGPU
The Journal of Supercomputing
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The graphic processing unit (GPU) can perform some large-scale simulations in an economical way. However, harnessing the power of a GPU to discrete event simulation (DES) is difficult because of the mismatch between GPU's synchronous execution mode and DES's asynchronous time advance mechanism. In this paper, we present a GPU-based simulation kernel (gDES) to support DES and propose three algorithms to support high efficiency. Since both limited parallelism and redundant synchronization affect the performance of DES based on a GPU, we propose a breadth-expansion conservative time window algorithm to increase the degree of parallelism while retaining the number of synchronizations. By using the expansion method, it can import as many as possible 'safe' events. The irregular and dynamic requirement for storing the events leads to uneven and sparse memory usage, thereby causing waste of memory and unnecessary overhead. A memory management algorithm is proposed to store events in a balanced and compact way by using a lightweight stochastic method. When events processed by threads in a warp have different types, the performance of gDES decreases rapidly because of branch divergence. An event redistribution algorithm is proposed by reassigning events of the same type to neighboring threads to reduce the probability of branch divergence. We analyze the superiority of the proposed algorithms and gDES with a series of experiments. Compared to a CPU-based simulator on a multicore platform, the gDES can achieve up to 11脙聴, 5脙聴, and 8脙聴 speedup in PHOLD, QUEUING NETWORK, and epidemic simulation, respectively.