Abstract
In the present run of the LHC, CMS data reconstruction and simulation algorithms benefit greatly from being executed as multiple threads running on several processor cores. The complexity of the Run 2 events requires parallelization of the code to reduce the memory-per- core footprint constraining serial execution programs, thus optimizing the exploitation of present multi-core processor architectures. The allocation of computing resources for multi-core tasks, however, becomes a complex problem in itself. The CMS workload submission infrastructure employs multi-slot partitionable pilots, built on HTCondor and GlideinWMS native features, to enable scheduling of single and multi-core jobs simultaneously. This provides a solution for the scheduling problem in a uniform way across grid sites running a diversity of gateways to compute resources and batch system technologies. This paper presents this strategy and the tools on which it has been implemented. The experience of managing multi-core resources at the Tier-0 and Tier-1 sites during 2015, along with the deployment phase to Tier-2 sites during early 2016 is reported. The process of performance monitoring and optimization to achieve efficient and flexible use of the resources is also described.
Highlights
Continuing to run single-threaded applications would not be the best way to exploit current multi-core CPU architectures, as the application memory footprint would exceed the available RAM-per-core in most CPU resources pledged to CMS across the Worldwide LHC Computing Grid (WLCG) [2]
Since late 2015, CMS has been successfully employing multi-threaded jobs for standard work in certain tasks, such as data and Monte Carlo (MC) reconstruction running at Tier-1 and Tier-2 sites
The majority of the workload executed by CMS in 2016 has been run in single-core mode, for MC generation and analysis jobs
Summary
Continuing to run single-threaded applications would not be the best way to exploit current multi-core CPU architectures, as the application memory footprint would exceed the available RAM-per-core in most CPU resources pledged to CMS across the Worldwide LHC Computing Grid (WLCG) [2]. The CMS computing infrastructure needs tools to allocate multi-core jobs to its CPU resources.
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