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A GPU-based discrete event simulation kernel

机译：基于GPU的离散事件仿真内核

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摘要

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× , 5× , and 8× speedup in PHOLD, QUEUING NETWORK, and epidemic simulation, respectively.
机译：图形处理单元（GPU）可以经济的方式执行一些大规模仿真。但是，由于GPU的同步执行模式与DES的异步时间提前机制之间的不匹配，难以将GPU的功能用于离散事件仿真（DES）。在本文中，我们提出了一种基于GPU的仿真内核（gDES）以支持DES，并提出了三种算法来支持高效。由于有限并行性和冗余同步都会影响基于GPU的DES的性能，因此我们提出了一种广度扩展的保守时间窗口算法，以在保持同步次数的同时提高并行度。通过使用扩展方法，它可以导入尽可能多的“安全”事件。存储事件的不规则和动态要求导致不均匀且稀疏的内存使用，从而造成内存浪费和不必要的开销。提出了一种内存管理算法，通过使用轻量级随机方法以平衡而紧凑的方式存储事件。当线程束中的线程处理的事件具有不同的类型时，由于分支分歧，gDES的性能将迅速下降。通过将相同类型的事件重新分配给相邻线程来提出事件重新分配算法，以减少分支发散的可能性。我们通过一系列实验分析了所提出算法和gDES的优越性。与多核平台上基于CPU的仿真器相比，gDES可以在PHOLD，QUEUING NETWORK和流行病仿真中分别实现11倍，5倍和8倍的加速。

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