In this paper, the authors detail the development of a framework for large-scale ATLAS pMSSM reinterpretations using containerized computational workflows on the REANA platform. By following ATLAS Analysis Preservation policies, numerous analyses were preserved as Yadage workflows and added to a curated selection for the pMSSM study. The complexity lies in running thousands of these workflows to cover various pMSSM model points efficiently. The study aimed to automate running thousands of containerized workflows in parallel to facilitate typical pMSSM studies.
The computational workflows were executed at scale using the REANA platform on Kubernetes clusters ranging from 500 to 5000 cores. Various parameters were adjusted to enhance scheduling efficiency and increase throughput for pMSSM style workflows. The sequence diagrams illustrated how incoming workflows were scheduled, processed, and terminated efficiently within the system.
Benchmarking experiments were conducted to optimize and tune the REANA system for handling multiple concurrent workloads effectively. The results showed that adjusting scheduling parameters based on the type of workload was crucial for maximizing throughput and resource utilization. Testing on different computing backends ensured reproducibility and readiness for large-scale ATLAS pMSSM reinterpretations.
Overall, the study demonstrated that preserving ATLAS analyses with containerized computational workflow recipes facilitates future reuse and reinterpretation, streamlining efficient pMSSM studies across a wide range of individual analyses.
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arxiv.org
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