A. Washbrook

Multi-core job submission and grid resource scheduling for ATLAS AthenaMP

AthenaMP is the multi-core implementation of the ATLAS software framework and allows the efficient sharing of memory pages between multiple threads of execution. This has now been validated for production and delivers a significant reduction on the overall application memory footprint with negligible CPU overhead. Before AthenaMP can be routinely run on the LHC Computing Grid it must be determined how the computing resources available to ATLAS can best exploit the notable improvements delivered by switching to this multi-process model. A study into the effectiveness and scalability of AthenaMP in a production environment will be presented. Best practices for configuring the main LRMS implementations currently used by grid sites will be identified in the context of multi-core scheduling optimisation.

Evaluation of containers as a virtualisation alternative for HEP workloads

In this paper the emerging technology of Linux containers is examined and evaluated for use in the High Energy Physics (HEP) community. Key technologies required to enable containerisation will be discussed along with emerging technologies used to manage container images. An evaluation of the requirements for containers within HEP will be made and benchmarking will be carried out to asses performance over a range of HEP workflows. The use of containers will be placed in a broader context and recommendations on future work will be given.

Development of site-oriented Analytics for Grid computing centres

The field of analytics, the process of analysing data to visualise meaningful patterns and trends, has become increasingly important in scientific computing as the volume and variety of data available to process has significantly increased. There is now ongoing work in the High Energy Physics (HEP) community in this area, for example in the augmentation of systems management at WLCG computing sites. We report on work evaluating the feasibility of distributed site-oriented analytics using the Elasticsearch, Logstash and Kibana software stack and demonstrate functionality by the application of two workflows that give greater insight into site operations.