Abstract

Performance variability is a major problem for extreme scale parallel computing applications that rely on bulk synchronization and collective communication. While this problem is most prominent in the context of exascale systems, it is increasingly impacting other communities such as machine learning and graph analytics. In this paper, we present an experimental performance analysis framework called varbench that is designed to precisely measure the prevalence of performance variability in a system, as well as to support workload characterization with respect to how and when a workload generates variability. We demonstrate several of varbench's capabilities as they pertain to exascale-class systems, including its utility for discovering architectural trends, for performing cross-architectural comparisons, and for understanding key statistical properties of performance distributions that have implications for how …

Metadata

publication

International Conference on Parallel Processing, 2018

year

2018

publication date

2018/8/13

authors

Brian Kocoloski, John R Lange

link

https://dl.acm.org/doi/abs/10.1145/3225058.3225125

resource_link

https://dl.acm.org/doi/pdf/10.1145/3225058.3225125

conference

International Conference on Parallel Processing