HPC系统延迟敏感性驱动的拥塞缓解

Archit Patke, Saurabh Jha, Haoran Qiu, J. Brandt, A. Gentile, Joe Greenseid, Z. Kalbarczyk, R. Iyer
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引用次数: 1

摘要

现代高性能计算(HPC)系统同时执行多个分布式应用程序,这些应用程序争夺高速网络,导致拥塞。因此,在生产系统中可以观察到应用程序运行时可变性和次优系统利用率。为了解决这些问题,我们提出了Netscope,这是一个基于新型延迟灵敏度度量的拥塞缓解框架。应用程序的延迟敏感性用于量化拥塞对其运行时的影响。Netscope使用延迟敏感性估计来驱动拥塞缓解机制,从而有选择地限制不易受拥塞影响的应用程序。我们在两个Cray Aries系统上对Netscope进行了评估,其中包括一台生产超级计算机,以及常见的科学应用。我们的评估表明,Netscope具有较低的培训成本,并且准确地估计了拥塞对应用程序运行时的影响,相关性在0.7和0.9之间。此外,Netscope将应用程序尾部运行时的增长减少了16.3倍,同时将系统效用中值提高了12%。
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Delay sensitivity-driven congestion mitigation for HPC systems
Modern high-performance computing (HPC) systems concurrently execute multiple distributed applications that contend for the high-speed network leading to congestion. Consequently, application runtime variability and suboptimal system utilization are observed in production systems. To address these problems, we propose Netscope, a congestion mitigation framework based on a novel delay sensitivity metric. Delay sensitivity of an application is used to quantify the impact of congestion on its runtime. Netscope uses delay sensitivity estimates to drive a congestion mitigation mechanism to selectively throttle applications that are less susceptible to congestion. We evaluate Netscope on two Cray Aries systems, including a production supercomputer, on common scientific applications. Our evaluation shows that Netscope has a low training cost and accurately estimates the impact of congestion on application runtime with a correlation between 0.7 and 0.9. Moreover, Netscope reduces application tail runtime increase by up to 16.3x while improving the median system utility by 12%.
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