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High-Energy Physics has evolved a rich set of software packages that need to work harmoniously to carry out the key software tasks needed by experiments. The problem of consistently building and deploying these packages as a coherent software stack is one that is shared across the HEP community. To that end the HEP Software Foundation Packaging Working Group has worked to identify common solutions that can be used across experiments, with an emphasis on consistent, reproducible builds and easy deployment into CernVM-FS or containers via CI systems. We based our approach on well-identified use cases and requirements from many experiments. In this paper we summarise the work of the group in the last year and how we have explored various approaches based on package managers from industry and the scientific computing community. We give details about a solution based on the Spack package manager which has been used to build the software required by the SuperNEMO and FCC experiments and trialled for a multi-experiment software stack, Key4hep. We shall discuss changes that needed to be made to Spack to satisfy all our requirements. We show how support for a build environment for software developers is provided. [ABSTRACT FROM AUTHOR]

This talk is about sharing our recent experiences in providing data analytics platform based on Apache Spark for High Energy Physics, CERN accelerator logging system and infrastructure monitoring. The Hadoop Service has started to expand its user base for researchers who want to perform analysis with big data technologies. Among many frameworks, Apache Spark is currently getting the most traction from various user communities and new ways to deploy Spark such as Apache Mesos or Spark on Kubernetes have started to evolve rapidly. Meanwhile, notebook web applications such as Jupyter offer the ability to perform interactive data analytics and visualizations without the need to install additional software. CERN already provides a web platform, called SWAN (Service for Web-based ANalysis), where users can write and run their analyses in the form of notebooks, seamlessly accessing the data and software they need. The first part of the presentation talks about several recent integrations and optimizations to the Apache Spark computing platform to enable HEP data processing and CERN accelerator logging system analytics. The optimizations and integrations, include, but not limited to, access of kerberized resources, xrootd connector enabling remote access to EOS storage and integration with SWAN for interactive data analysis, thus forming a truly Unified Analytics Platform. The second part of the talk touches upon the evolution of the Apache Spark data analytics platform, particularly sharing the recent work done to run Spark on Kubernetes on the virtualized and container-based infrastructure in Openstack. This deployment model allows for elastic scaling of data analytics workloads enabling efficient, on-demand utilization of resources in private or public clouds. [ABSTRACT FROM AUTHOR]

Computing in the field of high energy physics requires usage of heterogeneous computing resources and IT, such as grid, high performance computing, cloud computing and big data analytics for data processing and analysis. The core of the distributed computing environment at the Joint Institute for Nuclear Research is the Multifunctional Information and Computing Complex. It includes Tier1 for CMS experiment, Tier2 site for all LHC experiments and other grid non-LHC VOs, such as BIOMED, COMPASS, NICA/MPD, NOvA, STAR and BESIII, as well as cloud and HPC infrastructures. A brief status overview of each component is presented. Particular attention is given to the development of distributed computations performed in collaboration with CERN, BNL, FNAL, FAIR, China, and JINR Member States. One of the directions for the cloud infrastructure is the development of integration methods of various cloud resources of the JINR Member State organizations in order to perform common tasks, and also to distribute a load across integrated resources. We performed cloud resources integration of scientific centers in Armenia, Azerbaijan, Belarus, Kazakhstan and Russia. Extension of the HPC component will be carried through a specialized infrastructure for HPC engineering that is being created at MICC, which makes use of the contact liquid cooling technology implemented by the Russian company JSC "RSC Technologies". Current plans are to further develop MICC as a center for scientific computing within the multidisciplinary research environment of JINR and JINR Member States, and mainly for the NICA mega-science project. [ABSTRACT FROM AUTHOR]