Dynamic Distribution of High-Rate Data Processing from CERN to Remote HPC Data Centers

Cameron, D.; Cardo, Nicholas P.; Conciatore, D.; Girolamo, A. Di; Dissertori, G.; Fernández, Patricia; Filipčić, A.; Gila, Miguel; Grab, C.; Elmsheuser, J.; Jankauskas, V.; Klimentov, A.; Kovalskyi, D.; Lammel, S.; Shulthess, T. C.; Sciacca, F. G.; Serfon, C.; Walker, R.; Wissing, C.

doi:10.1007/s41781-020-00052-w

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…Integration of HPCs that are transparent to central computing operations have been performed in several countries in Europe, e.g., Italy, Switzerland, Germany, and Spain, for example expanding existing Tier 1 or Tier 2 sites elastically into these machines [350][351][352], or by deploying so-called overlay batch systems [353]. It is also through this model that a validation of the physics performance of CMS software on the PowerPC architecture could be performed [354].…”

Section: High Performance Computing (Hpc)mentioning

confidence: 99%

Development of the CMS detector for the CERN LHC Run 3

Hayrapetyan,

Tumasyan,

Adam

et al. 2024

J. Inst.

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Since the initial data taking of the CERN LHC, the CMS experiment has undergone substantial upgrades and improvements. This paper discusses the CMS detector as it is configured for the third data-taking period of the CERN LHC, Run 3, which started in 2022. The entire silicon pixel tracking detector was replaced. A new powering system for the superconducting solenoid was installed. The electronics of the hadron calorimeter was upgraded. All the muon electronic systems were upgraded, and new muon detector stations were added, including a gas electron multiplier detector. The precision proton spectrometer was upgraded. The dedicated luminosity detectors and the beam loss monitor were refurbished. Substantial improvements to the trigger, data acquisition, software, and computing systems were also implemented, including a new hybrid CPU/GPU farm for the high-level trigger.

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Section: High Performance Computing (Hpc)mentioning

confidence: 99%

Development of the CMS detector for the CERN LHC Run 3

Hayrapetyan,

Tumasyan,

Adam

et al. 2024

J. Inst.

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“…Nevertheless, using the vCluster approach, we can extend the HPC cluster configuration to an extreme data-driven HTC cluster configuration, and customise recipes for user access, slurm controls and software-defined, multi-tenant, HTC-oriented file systems. Previous studies have demonstrated that the middleware for virtualised computing and storage stacks aren’t interoperable with classic, Petascale supercomputing systems (De et al, 2021) (Boccali et al 2021). The middleware has been partially containerised (Roy et al, 2020).…”

Section: Wlcg Platform Vclusters On Alpsmentioning

confidence: 99%

Versatile software-defined HPC and cloud clusters on Alps supercomputer for diverse workflows

Alam

Gila

Klein

et al. 2023

The International Journal of High Performance Computing Applica

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Supercomputers have been driving innovations for performance and scaling benefiting several scientific applications for the past few decades. Yet their ecosystems remain virtually unchanged when it comes to integrating distributed data-driven workflows, primarily due to rather rigid access methods and restricted configuration management options. X-as-a-Service model of cloud has introduced, among other features, a developer-centric DevOps approach empowering developers of infrastructure, platform to software artefacts, which, unfortunately contemporary supercomputers still lack. We introduce vClusters (versatile software-defined clusters), which is based on Infrastructure-as-code (IaC) technology. vClusters approach is a unique fusion of HPC and cloud technologies resulting in a software-defined, multi-tenant cluster on a supercomputing ecosystem, that, together with software-defined storage, enable DevOps for complex, data-driven workflows like grid middleware, alongside a classic HPC platform. IaC has been a commonplace in cloud computing, however, it lacked adoption within multi-Petascale ecosystems due to concerns related to performance and interoperability with classic HPC data centres’ ecosystems. We present an overview of the Swiss National Supercomputing Centre’s flagship Alps ecosystem as an implementation target for vClusters for HPC and data-driven workflows. Alps is based on the Cray-HPE Shasta EX supercomputing platform that includes an IaC compliant, microservices architecture (MSA) management system, which we leverage for demonstrating vClusters usage for our diverse operational workflows. We provide implementation details of two operational vClusters platforms: a classic HPC platform that is used predominantly by hundreds of users running thousands of large-scale numerical simulations batch jobs; and a widely used, data-intensive, Grid computing middleware platform used for CERN Worldwide LHC Computing Grid (WLCG) operations. The resulting solution showcases reuse and reduction of common configuration recipes across vCluster implementations, minimising operational change management overheads while introducing flexibility for managing artefacts for DevOps required by diverse workflows.

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“…Remote computing interfaces: Many customized solutions have been developing for linking scientific instruments with HPC, for example in biomedicine [82], environmental science [83,84], and disaster response [85,86]; using HPC to analyze large data [87]; and providing on-demand access to HPC [88,89]. Such applications have motivated the development of specialized interfaces for remote job submission [90,91] and for managing workloads across systems [92][93][94][95].…”

Section: Related Workmentioning

confidence: 99%