.png){kind=icon}
.png){kind=icon}
Return
Data-Driven Application-Oriented Reliability Model of a High-Performance Computing System
.png){kind=icon}
PRE
AI
DOI：10.1109/TR.2021.3085582.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Reliability analysis and performance evaluation are complementary methods to quantify nonfunctional aspects of a system. However, a range of factors such as concurrency and heterogeneity quickly exacerbate the state-space explosion problem when attempting detailed system-level modeling and simulation of high-performance computing (HPC) systems. To overcome these impediments to modeling and analysis, this article develops a hierarchical model of an application that implements checkpointing running in an HPC environment subject to application, network, and system-wide outages. The modeling approach ensures that the number of states is linear in the number of checkpoints and possesses a low constant factor for the number of recovery states most relevant to the external influences contributing to degraded application performance. We illustrate the types of analysis enabled by the model through a series of examples with parameters determined empirically from data logs of the Blue Waters supercomputer located at the University of Illinois at Urbana-Champaign. A comprehensive comparative analysis of the model parameters indicates that lowering the failure rate of network nodes would most significantly reduce application downtime. We also discuss how the modeling approach can be used to objectively assess both current and hypothetical future systems to identify competitive designs and enhancements.
Keywords:
Analytical models
Blades
Reliability
Computational modeling
Numerical models
Checkpointing
Data models
Application performance
application reliability
high-performance computing (HPC)
network outage
utilization
Journal
IF:
5.7
Papers:
2.7K
Citations:
8.5K
Organization
View more organizations