Sperhac_Gateways2017_poster.pdf (1.94 MB)
Using XDMoD to Manage Scientific Gateways
poster
posted on 2017-10-08, 13:01 authored by Jeanette SperhacJeanette Sperhac, robert deleon, thomas furlani, steven m. gallo, martins innus, matthew d. jones, jeffrey t. palmer, abani patra, ben plessinger, ryan rathsam, nikolay a. simakov, joseph p white, thomas yearke, rudra chakraborty, cynthia corneliusXDMoD, a comprehensive tool for managing computing resources, collects
and aggregates numerous metrics that describe computational jobs,
resources consumed, wait times, and quality of service. The XDMoD web
interface then enables users to chart and visualize these metrics,
allowing them to expose and plot related information about their systems
via interactive drill-down. An open-source version of XDMoD helps us
manage our own center, including several gateways, at University at
Buffalo's Center for Computational Research. In this poster, example
plots from XDMoD will showcase its current support for gateways, and
demonstrate how administrators and end users alike can benefit from its
features.
XDMoD provides numerous metrics relevant to gateways that describe resource usage and utilization, including number of jobs, CPU hours, wall times, wait times, and job sizes. These metrics are fully integrated with the XDMoD tool, and may be aggregated over any desired time frame. These metrics form the base capabilities of XDMoD. Additional modules further extend XDMoD, providing such functionality as job level performance metrics (Job Viewer).
The Job Viewer presents details about a job's executable, its accounting data, job scripts, application information, and timeseries plots of individual metrics such as CPU user, flops, parallel file system usage, and memory usage. These data reflect the details of each job and empower support personnel and resource users to troubleshoot individual job performance or evaluate job efficiency.
This poster will display a variety of metrics showcasing the capabilities of XDMoD. It will display aggregated gateway jobs and usage data collected from our own HUBzero gateways, and plotted using our own open-source instance of XDMoD. With this, we hope to introduce the utility of XDMoD for the management of gateways.
XDMoD was developed to support NSF's traditional HPC systems, but it offers a boon to the gateways community as well. Using this tool, gateways administrators can explore and visualize data that enables them to manage their existing gateways, plan wisely for future gateway systems, and help end users run jobs more efficiently and make better use of computing resources.
XDMoD provides numerous metrics relevant to gateways that describe resource usage and utilization, including number of jobs, CPU hours, wall times, wait times, and job sizes. These metrics are fully integrated with the XDMoD tool, and may be aggregated over any desired time frame. These metrics form the base capabilities of XDMoD. Additional modules further extend XDMoD, providing such functionality as job level performance metrics (Job Viewer).
The Job Viewer presents details about a job's executable, its accounting data, job scripts, application information, and timeseries plots of individual metrics such as CPU user, flops, parallel file system usage, and memory usage. These data reflect the details of each job and empower support personnel and resource users to troubleshoot individual job performance or evaluate job efficiency.
This poster will display a variety of metrics showcasing the capabilities of XDMoD. It will display aggregated gateway jobs and usage data collected from our own HUBzero gateways, and plotted using our own open-source instance of XDMoD. With this, we hope to introduce the utility of XDMoD for the management of gateways.
XDMoD was developed to support NSF's traditional HPC systems, but it offers a boon to the gateways community as well. Using this tool, gateways administrators can explore and visualize data that enables them to manage their existing gateways, plan wisely for future gateway systems, and help end users run jobs more efficiently and make better use of computing resources.