HL-LHC R&D

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Lindsey Gray
Fermilab

HL-LHC R&D Co-Leader


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David Sperka
Boston University

HL-LHC R&D Co-Leader


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Matteo Cremonesi
Carnegie Mellon University

HL-LHC R&D Analysis Systems Coordinator


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Philip Chang
University of Florida

HL-LHC R&D Algorithms Coordinator


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David Lange
Princeton University

Computational Physicist


Overview

The HL-LHC area coordinates the centralized R&D effort for tackling the challenges in computing, data, and analysis during the HL-LHC era. We see the HL-LHC challenges as the following:

  • Reduce the resources required (such as CPU and disk) by the HL-LHC physics program to levels seen as supportable.
  • Enable analysis of much larger datasets as existing tools are not seen to scale up with the event count.
  • Grow our pool of resources by more efficiently using owned resources, leveraging new resource types (accelerators), and using new resources such as HPC centers.

This R&D area is organized into three sub-areas:

  • Analysis systems: developing new facilities and approaches for analysis during the HL-LHC era.
  • Production infrastructure research: evolving the existing grid infrastructure and systems to meet the challenges of HL-LHC.
  • Physics algorithms and tools: provide infrastructure and software to address the issues related to code performance in order to reduce computational needs for HL-LHC.

The area consists of software professionals and postdocs working on targeted year-long projects; there are significant collaborations with other projects and entities such as IRIS-HEP, Open Science Grid, HEP-CCE, ESNet, and SLATE.

Project Organization

The detailed area organization is:

  • Analysis Systems: Develop tools and analysis systems for HEP that enable both innovation and the adoption of “industry standard” analytic techniques. Enable rapid interactive analysis of PB datasets.
    • Tools for Advanced Analysis Provide interfaces and infrastructure to adapt HEP data in order to enable rapid analysis; projects include investments into columnar data such as Awkward Array.
    • Analysis Facilities. Prototype and put into production the infrastructure required for a multi-user analysis facility exploiting the newly-developed analysis tools.
  • Computing and Software Infrastructure: Explore, evaluate, prototype, and build the infrastructure necessary for HL-LHC computing.
    • Storage: Evaluate storage technologies for performance; update data formats and data-handling for efficient use and rapid transfer.
    • Provisioning of Compute Services: Simplify and automate the deployment of computing services through tools like Kubernetes.
    • HPC Integration & Development: Develop workflow infrastructure to allow efficient use of LCF HPCs.
    • Workflow Development: Research/prototype alternatives to bespoke CMS workflow management.
    • AI/ML Infrastructure: Evaluate and construct methods of integrating AI training workflows for rapid development.
  • Physics Algorithms: Provide infrastructure and software to address the issues related to code performance in order to reduce computational needs for HL-LHC.
    • Adaptation for heterogeneous architectures: Convert or extend existing algorithms to run on accelerators.
    • Algorithm Development: R&D into new algorithms, including those based on Machine Learning, that promise dramatic increases in processing speed.