HPC I/O Throughput Bottleneck Analysis with Explainable Local Models

Mihailo Isakov, Eliakin Del Rosario, S. Madireddy, Prasanna Balaprakash, P. Carns, R. Ross, M. Kinsy
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引用次数: 19

Abstract

With the growing complexity of high-performance computing (HPC) systems, achieving high performance can be difficult because of I/O bottlenecks. We analyze multiple years’ worth of Darshan logs from the Argonne Leadership Computing Facility’s Theta supercomputer in order to understand causes of poor I/O throughput. We present Gauge: a data-driven diagnostic tool for exploring the latent space of supercomputing job features, understanding behaviors of clusters of jobs, and interpreting I/O bottlenecks. We find groups of jobs that at first sight are highly heterogeneous but share certain behaviors, and analyze these groups instead of individual jobs, allowing us to reduce the workload of domain experts and automate I/O performance analysis. We conduct a case study where a system owner using Gauge was able to arrive at several clusters that do not conform to conventional I/O behaviors, as well as find several potential improvements, both on the application level and the system level.
基于可解释局部模型的高性能计算I/O吞吐量瓶颈分析
随着高性能计算(HPC)系统的日益复杂,由于I/O瓶颈,实现高性能可能会变得很困难。我们分析了Argonne Leadership Computing Facility的Theta超级计算机多年来的Darshan日志,以了解I/O吞吐量差的原因。我们介绍Gauge:一个数据驱动的诊断工具,用于探索超级计算作业特征的潜在空间,理解作业集群的行为,并解释I/O瓶颈。我们发现一组作业乍一看是高度异构的,但却共享某些行为,并分析这些组而不是单个作业,使我们能够减少领域专家的工作量并自动化I/O性能分析。我们进行了一个案例研究,在这个案例中,一个使用Gauge的系统所有者能够找到几个不符合常规I/O行为的集群,并在应用程序级别和系统级别上发现了一些潜在的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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