Speaker
Description
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 in order 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 the 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 contribution will present 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 will be described, along with the current phase of deployment to Tier-2 sites during 2016. The process of performance monitoring and optimization in order to achieve efficient and flexible use of the resources will also be described.
| Primary Keyword (Mandatory) | Distributed workload management |
|---|---|
| Secondary Keyword (Optional) | Computing models |
