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首页> 外文会议>2016 7th International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems >Visual Data-Analytics of Large-Scale Parallel Discrete-Event Simulations
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Visual Data-Analytics of Large-Scale Parallel Discrete-Event Simulations

机译：大规模并行离散事件模拟的可视数据分析

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Parallel discrete-event simulation (PDES) is an important tool in the codesign of extreme-scale systems because PDES provides a cost-effective way to evaluate designs of high-performance computing systems. Optimistic synchronization algorithms for PDES, such as Time Warp, allow events to be processed without global synchronization among the processing elements. A rollback mechanism is provided when events are processed out of timestamp order. Although optimistic synchronization protocols enable the scalability of large-scale PDES, the performance of the simulations must be tuned to reduce the number of rollbacks and provide an improved simulation runtime. To enable efficient large-scale optimistic simulations, one has to gain insight into the factors that affect the rollback behavior and simulation performance. We developed a tool for ROSS model developers that gives them detailed metrics on the performance of their large-scale optimistic simulations at varying levels of simulation granularity. Model developers can use this information for parameter tuning of optimistic simulations in order to achieve better runtime and fewer rollbacks. In this work, we instrument the ROSS optimistic PDES framework to gather detailed statistics about the simulation engine. We have also developed an interactive visualization interface that uses the data collected by the ROSS instrumentation to understand the underlying behavior of the simulation engine. The interface connects real time to virtual time in the simulation and provides the ability to view simulation data at different granularities. We demonstrate the usefulness of our framework by performing a visual analysis of the dragonfly network topology model provided by the CODES simulation framework built on top of ROSS. The instrumentation needs to minimize overhead in order to accurately collect data about the simulation performance. To ensure that the instrumentation does not introduce unnecessary overhead, we perform a scaling study that compares instrumented ROSS simulations with their noninstrumented counterparts in order to determine the amount of perturbation when running at different simulation scales.
机译：并行离散事件仿真（PDES）是极端规模系统代码中的重要工具，因为PDES提供了一种经济高效的方法来评估高性能计算系统的设计。用于PDES的乐观同步算法（例如时间扭曲）允许事件被处理而无需在处理元素之间进行全局同步。当按时间戳顺序处理事件时，将提供回滚机制。尽管乐观同步协议可以实现大规模PDES的可伸缩性，但是必须调整仿真的性能以减少回滚次数并提供改进的仿真运行时间。为了实现有效的大规模乐观仿真，必须深入了解影响回滚行为和仿真性能的因素。我们为ROSS模型开发人员开发了一个工具，可为他们提供在不同仿真粒度级别下大规模乐观仿真性能的详细指标。模型开发人员可以使用此信息进行乐观模拟的参数调整，以实现更好的运行时间和更少的回滚。在这项工作中，我们使用ROSS乐观PDES框架进行收集，以收集有关仿真引擎的详细统计信息。我们还开发了一个交互式可视化界面，该界面使用ROSS仪器收集的数据来了解模拟引擎的基本行为。该界面在仿真中将实时与虚拟时间连接起来，并提供了查看不同粒度的仿真数据的功能。我们通过对基于ROSS之上的CODES仿真框架提供的蜻蜓网络拓扑模型进行可视化分析，来证明我们框架的有用性。仪器需要最小化开销，以便准确收集有关仿真性能的数据。为确保仪器不会带来不必要的开销，我们进行了比例缩放研究，将仪器模拟的ROSS模拟与非仪器模拟的ROSS模拟进行比较，以确定在不同模拟比例下运行时的摄动量。

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