A Multi-Level, Multi-Scale Visual Analytics Approach to Assessment of
  Multifidelity HPC Systems
Shilpika Bethany Lusch Murali Emani Filippo Simini Venkatram Vishwanath Michael E. Papka Kwan-Liu Ma
The ability to monitor and interpret of hardware system events and behaviors
are crucial to improving the robustness and reliability of these systems,
especially in a supercomputing facility. The growing complexity and scale of
these systems demand an increase in monitoring data collected at multiple
fidelity levels and varying temporal resolutions. In this work, we aim to build
a holistic analytical system that helps make sense of such massive data, mainly
the hardware logs, job logs, and environment logs collected from disparate
subsystems and components of a supercomputer system. This end-to-end log
analysis system, coupled with visual analytics support, allows users to glean
and promptly extract supercomputer usage and error patterns at varying temporal
and spatial resolutions. We use multiresolution dynamic mode decomposition
(mrDMD), a technique that depicts high-dimensional data as correlated
spatial-temporal variations patterns or modes, to extract variation patterns
isolated at specified frequencies. Our improvements to the mrDMD algorithm help
promptly reveal useful information in the massive environment log dataset,
which is then associated with the processed hardware and job log datasets using
our visual analytics system. Furthermore, our system can identify the usage and
error patterns filtered at user, project, and subcomponent levels. We exemplify
the effectiveness of our approach with two use scenarios with the Cray XC40
supercomputer.