Abstract
Hundreds of physicists analyze data collected by the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider using the CMS Remote Analysis Builder and the CMS global pool to exploit the resources of the Worldwide LHC Computing Grid. Efficient use of such an extensive and expensive resource is crucial. At the same time, the CMS collaboration is committed to minimizing time to insight for every scientist, by pushing for fewer possible access restrictions to the full data sample and supports the free choice of applications to run on the computing resources. Supporting such variety of workflows while preserving efficient resource usage poses special challenges. In this paper we report on three complementary approaches adopted in CMS to improve the scheduling efficiency of user analysis jobs: automatic job splitting, automated run time estimates and automated site selection for jobs.
Highlights
The Compact Muon Solenoid (CMS) experiment [1] at CERN requires extensive capabilities for data processing, Monte Carlo simulation production, and user analysis tasks
A performance of 40,000 simultaneously running jobs and a daily completion rate of 500,000 analysis jobs have been achieved (Fig. 1). This contribution describes the current efforts focused on improving CPU utilization and scalability, and reducing user workflow turnaround time
The workload management system of the CMS experiment executes payloads in compute nodes provisioned through GlideinWMS [3] and made available as execution slots in a Vanilla Universe HTCondor [4] pool which we refer to as the Global Pool [5, 6]
Summary
The Compact Muon Solenoid (CMS) experiment [1] at CERN requires extensive capabilities for data processing, Monte Carlo simulation production, and user analysis tasks. A performance of 40,000 simultaneously running jobs and a daily completion rate of 500,000 analysis jobs have been achieved (Fig. 1). This contribution describes the current efforts focused on improving CPU utilization and scalability, and reducing user workflow turnaround time. These optimizations must be achieved without requiring any modifications of user applications
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