Improving efficiency of analysis jobs in CMS

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

Improving efficiency of analysis jobs in CMS

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

Improving the Scheduling Efficiency of a Global Multi-Core HTCondor Pool in CMS

Scheduling multi-core workflows in a global HTCondor pool is a multi-dimensional problem whose solution depends on the requirements of the job payloads, the characteristics of available resources, and the boundary conditions such as fair share and prioritization imposed on the job matching to resources. Within the context of a dedicated task force, CMS has increased significantly the scheduling efficiency of workflows in reusable multi-core pilots by various improvements to the limitations of the GlideinWMS pilots, accuracy of resource requests, efficiency and speed of the HTCondor infrastructure, and job matching algorithms

Exploring GlideinWMS and HTCondor scalability frontiers for an expanding CMS Global Pool

The CMS Submission Infrastructure Global Pool, built on Glidein-WMS andHTCondor, is a worldwide distributed dynamic pool responsible for the allocation of resources for all CMS computing workloads. Matching the continuously increasing demand for computing resources by CMS requires the anticipated assessment of its scalability limitations. In addition, the Global Plmust be able to expand in a more heterogeneous environment, in terms of resource provisioning (combining Grid, HPC and Cloud) and workload submissi.A dedicated testbed has been set up to simulate such conditions with the purpose of finding potential bottlenecks in the software or its configuration. This report provides a thorough description of the various scalabilitydimensions in size and complexity that are being explored for the future Global Pool, along with the analysis and solutions to the limitations proposed with the support of the GlideinWMS and HTCondor developer teams

Exploring GlideinWMS and HTCondor scalability frontiers for an expanding CMS Global Pool

The CMS Submission Infrastructure Global Pool, built on GlideinWMS and HTCondor, is a worldwide distributed dynamic pool responsible forthe allocation of resources for all CMS computing workloads. Matching the continuously increasing demand for computing resources by CMS requires the anticipated assessment of its scalability limitations. In addition, the Global Pool must be able to expand in a more heterogeneous environment, in terms of resource provisioning (combining Grid, HPC and Cloud) and workload submission. A dedicated testbed has been set up to simulate such conditions with the purpose of finding potential bottlenecks in the software or its configuration. This report provides a thorough description of the various scalability dimensions in size and complexity that are being explored for the future Global Pool, along with the analysis and solutions to the limitations proposed with the support of the GlideinWMS and HTCondor developer teams

Improving the Scheduling Efficiency of a Global Multi-Core HTCondor Pool in CMS

Scheduling multi-core workflows in a global HTCondor pool is a multi-dimensional problem whose solution depends on the requirements of the job payloads, the characteristics of available resources, and the boundary conditions such as fair share and prioritization imposed on the job matching to resources. Within the context of a dedicated task force, CMS has increased significantly the scheduling efficiency of workflows in reusable multi-core pilots by various improvements to the limitations of the GlideinWMS pilots, accuracy of resource requests, efficiency and speed of the HTCondor infrastructure, and job matching algorithms

Evolution of the CMS Global Submission Infrastructure for the HL-LHC Era

Efforts in distributed computing of the CMS experiment at the LHC at CERN are now focusing on the functionality required to fulfill the projected needs for the HL-LHC era. Cloud and HPC resources are expected to be dominant relative to resources provided by traditional Grid sites, being also much more diverse and heterogeneous. Handling their special capabilities or limitations and maintaining global flexibility and efficiency, while also operating at scales much higher than the current capacity, are the major challenges being addressed by the CMS Submission Infrastructure team. These proceedings discuss the risks to the stability and scalability of the CMS HTCondor infrastructure extrapolated to such a scenario, thought to be derived mostly from its growing complexity, with multiple Negotiators and schedulers flocking work to multiple federated pools. New mechanisms for enhanced customization and control over resource allocation and usage, mandatory in this future scenario, are also described

Evolution of the CMS Global Submission Infrastructure for the HL-LHC Era

Efforts in distributed computing of the CMS experiment at the LHC at CERN are now focusing on the functionality required to fulfill the projected needs for the HL-LHC era. Cloud and HPC resources are expected to be dominant relative to resources provided by traditional Grid sites, being also much more diverse and heterogeneous. Handling their special capabilities or limitations and maintaining global flexibility and efficiency, while also operating at scales much higher than the current capacity, are the major challenges being addressed by the CMS Submission Infrastructure team. These proceedings discuss the risks to the stability and scalability of the CMS HTCondor infrastructure extrapolated to such a scenario, thought to be derived mostly from its growing complexity, with multiple Negotiators and schedulers flocking work to multiple federated pools. New mechanisms for enhanced customization and control over resource allocation and usage, mandatory in this future scenario, are also described

Improving efficiency of analysis jobs in CMS

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