面向高性能计算系统的应用感知拥塞缓解
Application-aware Congestion Mitigation for High-Performance Computing Systems
摘要：
高性能计算（HPC）系统经常经历拥塞，从而导致显著的应用性能变化。然而，拥塞对应用程序运行时的影响因应用程序而异，具体取决于其网络特性（例如带宽和延迟要求）。我们利用这一洞察力来开发Netscope，这是一个自动化的ML驱动框架，它考虑了这些网络特性来动态缓解拥塞。我们评估Netscope的四个Cray白羊座系统，包括生产超级计算机上的真正的科学应用。Netscope具有较低的训练成本，并准确地估计拥塞对应用程序运行时的影响，对于常见的科学应用程序，相关性在0.7和0.9之间。此外，我们发现Netscope将尾部运行时可变性降低了14.9倍，同时将中值系统实用性提高了12%。
英文摘要：
High-performance computing (HPC) systems frequently experience congestion leading to significant application performance variation. However, the impact of congestion on application runtime differs from application to application depending on their network characteristics (such as bandwidth and latency requirements). We leverage this insight to develop Netscope, an automated ML-driven framework that considers those network characteristics to dynamically mitigate congestion. We evaluate Netscope on four Cray Aries systems, including a production supercomputer on real scientific applications. Netscope has a lower training cost and accurately estimates the impact of congestion on application runtime with a correlation between 0.7and 0.9 for common scientific applications. Moreover, we find that Netscope reduces tail runtime variability by up to 14.9 times while improving median system utility by 12%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
