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1. Investigating Data Access Models for ATLAS: A Case Study with FABRIC Across Borders and ServiceX

   https://doi.org/10.1051/epjconf/202533701117  

   Vukotic, Ilija; Hu, Fengping; Bryant, Lincoln; Gardner_Jr, Robert William; McKee, Shawn; Stephen, Judith; Jordan, David (October 2025, EPJ Web of Conferences)

   Szumlak, T; Rachwał, B; Dziurda, A; Schulz, M; vom_Bruch, D; Ellis, K; Hageboeck, S (Ed.)

   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. 

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2. Enabling Hyperparameter Tuning of Machine Learning Classifiers in Production

   https://doi.org/10.1109/CogMI52975.2021.00041  

   Sandha, Sandeep Singh; Aggarwal, Mohit; Saha, Swapnil Sayan; Srivastava, Mani (December 2021, 2021 IEEE Third International Conference on Cognitive Machine Intelligence (CogMI))

   Machine learning (ML) classifiers are widely adopted in the learning-enabled components of intelligent Cyber-physical Systems (CPS) and tools used in designing integrated circuits. Due to the impact of the choice of hyperparameters on an ML classifier performance, hyperparameter tuning is a crucial step for application success. However, the practical adoption of existing hyperparameter tuning frameworks in production is hindered due to several factors such as inflexible architecture, limitations of search algorithms, software dependencies, or closed source nature. To enable state-of-the-art hyperparameter tuning in production, we propose the design of a lightweight library (1) having a flexible architecture facilitating usage on arbitrary systems, and (2) providing parallel optimization algorithms supporting mixed parameters (continuous, integer, and categorical), handling runtime failures, and allowing combined classifier selection and hyperparameter tuning (CASH). We present Mango, a black-box optimization library, to realize the proposed design. Mango is currently used in production at Arm for more than 25 months and is available open-source (https://github.com/ARM-software/mango). Our evaluation shows that Mango outperforms other black-box optimization libraries in tuning hyperparameters of ML classifiers having mixed param-eter search spaces. We discuss two use cases of Mango deployed in production at Arm, highlighting its flexible architecture and ease of adoption. The first use case trains ML classifiers on the Dask cluster using Mango to find bugs in Arm's integrated circuits designs. As a second use case, we introduce an AutoML framework deployed on the Kubernetes cluster using Mango. Finally, we present the third use-case of Mango in enabling neural architecture search (NAS) to transfer deep neural networks to TinyML platforms (microcontroller class devices) used by CPS/IoT applications. 

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3. Computing at the HL-LHC and beyond

   https://doi.org/10.22323/1.478.0333  

   Gardner, Robert William (December 2024, Sissa Medialab)

   The High-Luminosity Large Hadron Collider (HL-LHC) is set to introduce unprecedented data volumes and computational demands, necessitating significant enhancements in the current LHC computing infrastructure. We summarize efforts by the experiments to integrate high-performance computing clusters and public cloud resources into their processing frameworks. We also examine the adoption of cloud technologies for implementation of advanced service infrastructure which are finding applications in Tier 2 centers and prototyping of future analysis facilities. We highlight the crucial role of scalable networking capabilities and challenge exercises to prepare for the expected increased data throughput. 

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4. High Performance Data Engineering Everywhere

   https://doi.org/10.1109/SMDS49396.2020.00022  

   Widanage, Chathura; Perera, Niranda; Abeykoon, Vibhatha; Kamburugamuve, Supun; Kanewala, Thejaka Amila; Maithree, Hasara; Wickramasinghe, Pulasthi; Uyar, Ahmet; Gunduz, Gurhan; Fox, Geoffrey (October 2020, 2020 IEEE International Conference on Smart Data Services (SMDS))

   null (Ed.)

   The amazing advances being made in the fields of machine and deep learning are a highlight of the Big Data era for both enterprise and research communities. Modern applications require resources beyond a single node's ability to provide. However this is just a small part of the issues facing the overall data processing environment, which must also support a raft of data engineering for pre- and post-data processing, communication, and system integration. An important requirement of data analytics tools is to be able to easily integrate with existing frameworks in a multitude of languages, thereby increasing user productivity and efficiency. All this demands an efficient and highly distributed integrated approach for data processing, yet many of today's popular data analytics tools are unable to satisfy all these requirements at the same time. In this paper we present Cylon, an open-source high performance distributed data processing library that can be seamlessly integrated with existing Big Data and AI/ML frameworks. It is developed with a flexible C++ core on top of a compact data structure and exposes language bindings to C++, Java, and Python. We discuss Cylon's architecture in detail, and reveal how it can be imported as a library to existing applications or operate as a standalone framework. Initial experiments show that Cylon enhances popular tools such as Apache Spark and Dask with major performance improvements for key operations and better component linkages. Finally, we show how its design enables Cylon to be used cross-platform with minimum overhead, which includes popular AI tools such as PyTorch, Tensorflow, and Jupyter notebooks. 

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5. SuperSONIC: Cloud-Native Infrastructure for ML Inferencing

   https://doi.org/10.1145/3708035.3736049  

   Kondratyev, Dmitry; Riedel, Benedikt; Chou, Yuan-Tang; Cochran-Branson, Miles; Paladino, Noah; Schultz, David; Liu, Mia; Duarte, Javier; Harris, Philip; Hsu, Shih-Chieh (July 2025, ACM)

   The increasing computational demand from growing data rates and complex machine learning (ML) algorithms in large-scale scientific experiments has driven the adoption of the Services for Optimized Network Inference on Coprocessors (SONIC) approach. SONIC accelerates ML inference by offloading it to local or remote coprocessors to optimize resource utilization. Leveraging its portability to different types of coprocessors, SONIC enhances data processing and model deployment efficiency for cutting-edge research in high energy physics (HEP) and multi-messenger astrophysics (MMA). We developed the SuperSONIC project, a scalable server infrastructure for SONIC, enabling the deployment of computationally intensive tasks to Kubernetes clusters equipped with graphics processing units (GPUs). Using NVIDIA Triton Inference Server, SuperSONIC decouples client workflows from server infrastructure, standardizing communication, optimizing throughput, load balancing, and monitoring. SuperSONIC has been successfully deployed for the CMS and ATLAS experiments at the CERN Large Hadron Collider (LHC), the IceCube Neutrino Observatory (IceCube), and the Laser Interferometer Gravitational-Wave Observatory (LIGO) and tested on Kubernetes clusters at Purdue University, the National Research Platform (NRP), and the University of Chicago. SuperSONIC addresses the challenges of the Cloud-native era by providing a reusable, configurable framework that enhances the efficiency of accelerator-based inference deployment across diverse scientific domains and industries. 

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