New directions in the CernVM file system

Prediction for requirements for the LHC computing for Run 3 and for Run 4 (HL-LHC) over the course of the next 10 years, show a considerable gap between required and available resources, assuming budgets will globally remain flat at best. This will require some radical changes to the computing models for the data processing of the LHC experiments. The use of large scale computational resources at HPC centres worldwide is expected to increase substantially the cost-e ciency of the processing. In order to pave the path towards the HL-LHC data processing, the Swiss Institute of Particle Physics (CHIPP) has taken the strategic decision to migrate the processing of all the Tier-2 workloads for ATLAS and other LHC experiments from a dedicated x86 64 cluster that has been in continuous operation and evolution since 2007, to Piz Daint, the current European flagship HPC, which ranks third in the TOP500 at the time of writing. We report on the technical challenges and solutions adopted to migrate to Piz Daint, and on the experience and measured performance for ATLAS in over one year of running in production.

Section: Software Provisioningmentioning

confidence: 99%

Production experience and performance for ATLAS data processing on a Cray XC-50 at CSCS

Sciacca

Weber

2019

“…Regardless of which pilot system is used, available resources are advertised in the global pool as standard HTCondor resources and IceCube jobs requesting GPUs eventually run on a GPU-enabled node independently of its location. The first thing every job does is load the IceCube software environment, located on CVMFS [15]. This does some basic GPU and driver detection, and helps enable OpenCL support.…”

Section: Icecube Use Casementioning

confidence: 99%

“…Open Science Grid uses a combination of CVMFS [15] and Singularity [16] to run applications inside containers that the user specifies [17]. OSG maintains some catch all singularity images out of Docker containers.…”

Section: Singularity Containers In Osgmentioning

confidence: 99%

OSG and GPUs: A tale of two use cases

Fajardo

Rynge²,

Weitzel

et al. 2019

With the increase of power and reduction of cost of GPU accelerated processors a corresponding interest in their uses in the scientific domain has spurred. OSG users are no different and they have shown an interest in accessing GPU resources via their usual workload infrastructures. Grid sites that have these kinds of resources also want to make them grid available. In this talk, we discuss the software and infrastructure challenges and limitations of the OSG implementations to make GPU's widely accessible over the grid. Two use cases are considered for this. First: IceCube, a big VO with a well-curated software stack taking advantage of GPUs with OpenCL. Second, a more general approach to supporting the grid use of industry and academia maintained machine learning libraries like Tensorflow, and Keras on the grid using Singu-a set of containers for outside libraries brings its own set of challenges looking into the future, which we discuss in our final Section 4.

“…For the continuous compilation and testing of the C++ based software, for example DD4hep, we first need to provide its dependencies, which include Geant and Root and for our use case also parts of iLCSoft. To access the dependencies, we use the docker service in Travis-CI and also install CVMFS [16]. This is shown in Listing 2.…”

Section: With Cvmfs To Provide Dependenciesmentioning

confidence: 99%

Automation and Testing for Simplified Software Deployment

Sailer

Petrič

2019

Creating software releases is one of the more tedious occupations in the life of a software developer. For this purpose we have tried to automate as many of the repetitive tasks involved as possible from getting the commits to running the software. For this simplification we rely in large parts on free collaborative services available around GitHub: issue tracking, code review (GitHub), continuous integration (Travis-CI), static code analysis (coverity). The dependencies and compilers used in the continuous integration are obtained by mounting CVMFS into a docker container. This enables running any desired compiler version (e.g., gcc 6.2, llvm 3.9) or tool (e.g, clang-format, pylint). To create tags for the software package the powerful GitHub API is used. A script was developed that first collates the release notes from the description of each pull request, commits the release notes file, and finally makes a tag. This moves the burden of writing release notesfrom the package maintainer to the individual developer. The deployment of software releases to CVMFS is handled via GitLab-CI. When a tag is made the software is built and automatically deployed. In this paper we will describe the software infrastructure used for the iLCSoft and iLCDirac projects, which are used by CLICdp and the ILC detector collaborations, and give examples of automation which might be useful for others. *