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The ATLAS experiment is currently developing columnar analysis frameworks which leverage the Python data science ecosystem. We describe the construction and operation of the infrastructure necessary to support demonstrations of these frameworks, with a focus on those from IRIS-HEP. One such demonstrator aims to process the compact ATLAS data format PHYSLITE at rates exceeding 200 Gbps. Various access configurations and setups on different sites are explored, including direct access to a dCache storage system via Xrootd, the use of ServiceX, and the use of multiple XCache servers equipped with NVMe storage devices. Integral to this study was the analysis of network traffic and bottlenecks, worker node scheduling and disk configurations, and the performance of an S3 object store. The system’s overall performance was measured as the number of processing cores scaled to over 2,000 and the volume of data accessed in an interactive session approached 200 TB. The presentation will delve into the operational details and findings related to the physical infrastructure that underpins these demonstrators. 

We explore the adoption of cloud-native tools and principles to forge flexible and scalable infrastructures, aimed at supporting analysis frameworks being developed for the ATLAS experiment in the High Luminosity Large Hadron Collider (HL-LHC) era. The project culminated in the creation of a federated platform, integrating Kubernetes clusters from various providers such as Tier-2 centers, Tier-3 centers, and from the IRIS-HEP Scalable Systems Laboratory, a National Science Foundation project. A unified interface was provided to streamline the management and scaling of containerized applications. Enhanced system scalability was achieved through integration with analysis facilities, enabling spillover of Jupyter/Binder notebooks and Dask workers to Tier-2 resources. We investigated flexible deployment options for a “stretched” (over the wide area network) cluster pattern, including a centralized “lights out management” model, remote administration of Kubernetes services, and a fully autonomous site-managed cluster approach, to accommodate varied operational and security requirements. The platform demonstrated its efficacy in multi-cluster demonstrators for low-latency analyses and advanced workflows with tools such as Coffea, ServiceX, Uproot and Dask, and RDataFrame, illustrating its ability to support various processing frameworks. The project also resulted in a robust user training infrastructure for ATLAS software and computing on-boarding events. 

This study explores enhancements in analysis speed, WAN bandwidth efficiency, and data storage management through an innovative data access strategy. The proposed model introduces specialized ‘delivery’ services for data preprocessing, which include filtering and reformatting tasks executed on dedicated hardware located alongside the data repositories at CERN’s Tier-0, Tier-1, or Tier-2 facilities. Positioned near the source storage, these services are crucial for limiting redundant data transfers and focus on sending only vital data to distant analysis sites, aiming to optimize network and storage use at those sites. Within the scope of the NSF-funded FABRIC Across Borders (FAB) initiative, we assess this model using an “in-network, edge” computing cluster at CERN, outfitted with substantial processing capabilities (CPU, GPU, and advanced network interfaces). This edge computing cluster features dedicated network peering arrangements that link CERN Tier-0, the FABRIC experimental network, and an analysis center at the University of Chicago, creating a solid foundation for our research. Central to our infrastructure is ServiceX, an R&D software project under the Data Organization, Management, and Access (DOMA) group of the Institute for Research and Innovation in Software for High Energy Physics - IRIS-HEP. ServiceX is a scalable filtering and reformatting service, designed to operate within a Kubernetes environment and deliver output to an S3 object store at an analysis facility. Our study assesses the impact of server-side delivery services in augmenting the existing HEP computing model, particularly evaluating their possible integration within the broader WAN infrastructure. This model could empower Tier-1 and Tier-2 centers to become efficient data distribution nodes, enabling a more cost-effective way to disseminate data to analysis sites and object stores, thereby improving data access and efficiency. This research is experimental and serves as a demonstrator of the capabilities and improvements that such integrated computing models could offer in the HL-LHC era. 

The IRIS-HEP software institute, as a contributor to the broader HEP Python ecosystem, is developing scalable analysis infrastructure and software tools to address the upcoming HL-LHC computing challenges with new approaches and paradigms, driven by our vision of what HL-LHC analysis will require. The institute uses a “Grand Challenge” format, constructing a series of increasingly large, complex, and realistic exercises to show the vision of HL-LHC analysis. Recently, the focus has been demonstrating the IRIS-HEP analysis infrastructure at scale and evaluating technology readiness for production. As a part of the Analysis Grand Challenge activities, the institute executed a “200 Gbps Challenge”, aiming to show sustained data rates into the event processing of multiple analysis pipelines. The challenge integrated teams internal and external to the institute, including operations and facilities, analysis software tools, innovative data delivery and management services, and scalable analysis infrastructure. The challenge showcases the prototypes — including software, services, and facilities — built to process around 200 TB of data in both the CMS NanoAOD and ATLAS PHYSLITE data formats with test pipelines. The teams were able to sustain the 200 Gbps target across multiple pipelines. The pipelines focusing on event rate were able to process at over 30 MHz. These target rates are demanding; the activity revealed considerations for future testing at this scale and changes necessary for physicists to work at this scale in the future. The 200 Gbps Challenge has established a baseline on today’s facilities, setting the stage for the next exercise at twice the scale. 

As CERN approaches the launch of the High Luminosity Large Hadron Collider (HL-LHC) by the decade’s end, the computational demands of traditional simulations have become untenably high. Projections show millions of CPU-years required to create simulated datasets - with a substantial fraction of CPU time devoted to calorimetric simulations. This presents unique opportunities for breakthroughs in computational physics. We show how Quantumassisted Generative AI can be used for the purpose of creating synthetic, realistically scaled calorimetry dataset. The model is constructed by combining D-Wave’s Quantum Annealer processor with a Deep Learning architecture, increasing the timing performance with respect to first principles simulations and Deep Learning models alone, while maintaining current state-of-the-art data quality