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Scalable ATLAS pMSSM Computational Workflows Using REANA Platform


Core Concepts
The authors developed a streamlined framework for large-scale pMSSM reinterpretations of ATLAS analyses using containerized computational workflows on the REANA platform, aiming to assess the global coverage of BSM physics.
Abstract
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.
Stats
"O(5k) computational workflows representing pMSSm model points." "Kubernetes clusters from 500 to 5000 cores." "200 new pMMs workflows every 10 minutes." "Cluster with 448 cores cannot keep up with workload." "Cluster with 1072 cores can comfortably hold incoming workload."
Quotes
"We have improved the REANA platform scheduling performance in order to maximize the scheduling throughput." "The complexity lies in having to run several thousands of these workflows in order to cover a sufficient number of pMMs model points." "The first results by the ATLAS collaborations are being published."

Deeper Inquiries

How does automating large-scale computational workflows impact research efficiency beyond physics?

Automating large-scale computational workflows has a significant impact on research efficiency across various fields beyond physics. By streamlining and containerizing workflows, researchers can save time on manual setup and configuration, allowing them to focus more on data analysis and interpretation. This automation also enhances reproducibility by ensuring that the same workflow can be easily replicated by other researchers or in future studies. Moreover, automated workflows enable scalability, making it feasible to process massive amounts of data quickly and efficiently. This increased efficiency leads to faster results, accelerated discoveries, and ultimately advances scientific knowledge across disciplines.

What potential challenges or limitations could arise from relying heavily on containerized computational workflows?

While containerized computational workflows offer numerous benefits, there are some challenges and limitations associated with heavy reliance on them. One challenge is the complexity of managing multiple containers within a workflow, which can lead to issues with version control, dependencies, and compatibility between different containers. Additionally, ensuring security within containers is crucial as vulnerabilities in one container could potentially compromise the entire workflow. Another limitation is the learning curve for researchers unfamiliar with containerization technologies, which may require additional training or expertise to effectively utilize these tools. Lastly, maintaining a balance between flexibility and standardization when using containers for diverse research needs can be challenging as customizations may conflict with standardized processes.

How might advancements in cloud infrastructure impact future scalability and reproducibility efforts in scientific research?

Advancements in cloud infrastructure have the potential to significantly impact future scalability and reproducibility efforts in scientific research. Cloud platforms offer virtually unlimited resources that can be dynamically scaled up or down based on demand, providing researchers with access to high-performance computing capabilities without upfront investments in hardware infrastructure. This scalability enables scientists to process larger datasets more efficiently while reducing processing times for complex analyses. Moreover, cloud services facilitate collaboration among geographically dispersed teams by providing centralized storage solutions for sharing data and results securely. Researchers can leverage cloud-based environments for running reproducible analyses consistently across different computing environments without worrying about compatibility issues. Overall, advancements in cloud infrastructure empower researchers to conduct cutting-edge scientific investigations at scale while promoting reproducibility through standardized computing environments accessible from anywhere globally.
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