基于GPU的扩展辅助同步保守时间管理算法

Tan Wenjie, Yao Yiping, Zhu Feng
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引用次数: 5

摘要

图形处理单元(GPU)为以经济的方式实现大规模仿真提供了机会。GPU的性能依赖于高并行性,但采用同步保守时间管理算法进行离散事件仿真会满足并行性有限的场景。即使应用程序本身具有高并行性,这种冲突也会导致糟糕的性能。为了解决这一问题,我们提出了一种扩展辅助同步保守时间管理算法。它使用运行时信息来扩大“安全”事件的时间范围,并使用扩展方法来导入“安全”事件。通过将一系列展开与事件计算交织在一起,可以组装更多事件以并行处理。此外,采用模拟退火算法控制展开次数。通过在低并行性和不必要的扩展之间找到平衡,它有助于在不同条件下实现稳定的性能。实验表明,该算法的性能提高了30%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An expansion-aided synchronous conservative time management algorithm on GPU
The graphic processing unit (GPU) brings an opportunity to implement large scale simulations in an economical way. GPU's performance relies on high parallelism, but using synchronous conservative time management algorithm for discrete event simulation will meet the scenarios with limited parallelism. This conflict leads to bad performance even though the application itself has high parallelism. To solve this problem, we propose an expansion-aided synchronous conservative time management algorithm. It uses runtime information to enlarge the time bound of "safe" events, and uses an expansion method to import "safe" events. By interleaving a series of expansions with event computation, more events can be assembled to be processed in parallel. Moreover, a simulated annealing algorithm is adopted to control the number of expansions. It helps achieve stable performance under different conditions by finding a balance between low parallelism and unnecessary expansions. Experiments demonstrate that the proposed algorithm can achieve up to a 30% performance improvement.
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