Measuring Network Latency Variation Impacts to High Performance Computing Application Performance
Measuring Network Latency Variation Impacts to High Performance Computing Application Performance
复制标题
测量网络延迟变化对高性能计算应用程序性能的影响
DOI：
10.1145/3184407.3184427
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Apon, Amy
中科院分区：
文献类型：
--
作者：
Underwood, Robert;Anderson, Jason;Apon, Amy
在本文中，我们研究了延迟变化与延迟平均值对应用程序运行时，库性能和数据包传输的影响。我们的贡献包括网络延迟注入器的设计和实现，适用于大多数QLogic和Mellanox InfiniBand卡。我们适合的延迟平均值和变化的统计分布，以不同程度的网络竞争的一系列并行应用程序的工作负载。我们使用统计分布来表征延迟变化对应用程序降级的影响。网络延迟变化导致的应用程序性能下降程度取决于应用程序的特性，并且可能很严重。观察到的退化从没有通信进程的应用程序的无退化到通信密集型并行应用程序的3.5倍慢。我们支持我们的结果与我们的实验观察的统计分析。对于通信密集型的高性能计算应用程序，我们显示了统计上显着的证据表明，性能的变化与网络延迟的变化比与平均网络延迟的变化更密切相关。
In this paper, we study the impacts of latency variation versus latency mean on application runtime, library performance, and packet delivery. Our contributions include the design and implementation of a network latency injector that is suitable for most QLogic and Mellanox InfiniBand cards. We fit statistical distributions of latency mean and variation to varying levels of network contention for a range of parallel application workloads. We use the statistical distributions to characterize the latency variation impacts to application degradation. The level of application degradation caused by variation in network latency depends on application characteristics, and can be significant. Observed degradation varies from no degradation for applications without communicating processes to 3.5 times slower for communication-intensive parallel applications. We support our results with statistical analysis of our experimental observations. For communication-intensive high performance computing applications, we show statistically significant evidence that changes in performance are more highly correlated with changes of variation in network latency than with changes of mean network latency alone.
DOI：
10.1145/2503210.2503247
发表时间：
2013-11
期刊：
2013 SC - International Conference for High Performance Computing, Networking, Storage and Analysis (SC)
影响因子：
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作者：
A. Bhatele;K. Mohror;S. Langer;Katherine E. Isaacs
通讯作者：
A. Bhatele;K. Mohror;S. Langer;Katherine E. Isaacs
DOI：
10.1109/ipdps.2015.92
发表时间：
2015-05
期刊：
2015 IEEE International Parallel and Distributed Processing Symposium
影响因子：
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作者：
A. Bhatele;Andrew R. Titus;Jayaraman J. Thiagarajan;Nikhil Jain;T. Gamblin;P. Bremer;M. Schulz;L. Kalé
通讯作者：
A. Bhatele;Andrew R. Titus;Jayaraman J. Thiagarajan;Nikhil Jain;T. Gamblin;P. Bremer;M. Schulz;L. Kalé
安静的社区：保护工作绩效可预测性的关键
DOI：
10.1109/ipdps.2015.87
发表时间：
2015
期刊：
2015 IEEE International Parallel and Distributed Processing Symposium
影响因子：
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作者：
Ana Jokanovic;J. Sancho;G. Rodríguez;A. Lucero;C. Minkenberg;J. Labarta
通讯作者：
J. Labarta