2025-10-14
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Taking on RISC for Energy-Efficient Computing in HEP
/ Simili, Emanuele (Glasgow U.) ; Boccali, Tommaso (INFN, Pisa) ; Muzaffar, Shahzad (CERN) ; Stewart, Gordon (Glasgow U.) ; Skipsey, Samuel (Glasgow U.) ; Borbely, Albert (Glasgow U.) ; Britton, David (Glasgow U.)
In pursuit of energy-efficient solutions for computing in High Energy Physics (HEP) we have extended our investigations of non-x86 architectures beyond the ARM platforms that we have previously studied. In this work, we have taken a first look at the RISC-V architecture for HEP workloads, leveraging advancements in both hardware and software maturity.We introduce the Pioneer Milk-V, a 64-core RISC-V machine running Fedora Linux, as our new testbed, available at ScotGrid Glasgow (UK) and INFN Bologna and Pisa (Italy). [...]
2025 - 8 p.
- Published in : EPJ Web Conf. 337 (2025) 01163
Fulltext: PDF;
In : 27th International Conference on Computing in High Energy & Nuclear Physics (CHEP2024), Kraków, Poland, 19 - 25 Oct 2024, pp.01163
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2025-10-14
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2025-10-14
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2025-10-14
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Benchmark Studies of Machine Learning Inference using SOFIE
/ Moneta, Lorenzo (CERN) ; Sengupta, Sanjiban (CERN ; U. Manchester (main)) ; Panagou, Ioanna-Maria (CERN ; Thessaly U.) ; Shah, Neel (Unlisted, CH) ; Wollenhaupt, Paul (CERN ; U. Gottingen (main))
SOFIE is a fast Machine Learning inference engine developed at CERN, capable of translating trained deep learning models—provided in ONNX, Keras, or PyTorch formats—into C++ code for efficient inference. The generated code has minimal dependencies, making it easily integrable into the data processing and analysis workflows of high-energy physics (HEP) experiments.This study presents a comprehensive benchmark analysis of SOFIE against leading machine learning frameworks for model evaluation, including PyTorch, TensorFlow XLA, and ONNX Runtime. [...]
2025 - 9 p.
- Published in : EPJ Web Conf. 337 (2025) 01183
Fulltext: PDF;
In : 27th International Conference on Computing in High Energy & Nuclear Physics (CHEP2024), Kraków, Poland, 19 - 25 Oct 2024, pp.01183
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2025-10-14
20:37 |
Thread-safe N-tuple Writing in Gaudi with TTree and Migration to RNTuple
/ Taider, Silia (CERN ; Unlisted, FR) ; Clemencic, Marco (CERN)
The software framework of the Large Hadron Collider Beauty (LHCb) experiment, Gaudi, heavily relies on the ROOT framework and its I/O subsystems for data persistence mechanisms. Gaudi internally leverages the ROOT TTree data format, as it is currently used in production by LHC experiments [...]
2025 - 7 p.
- Published in : EPJ Web Conf. 337 (2025) 01078
Fulltext: PDF;
In : 27th International Conference on Computing in High Energy & Nuclear Physics (CHEP2024), Kraków, Poland, 19 - 25 Oct 2024, pp.01078
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2025-10-14
20:37 |
EDM4hep - The common event data model for the Key4hep project
/ Carceller, Juan Miguel (CERN) ; Fila, Mateusz Jakub (CERN) ; Francois, Brieuc (CERN) ; Gaede, Frank (DESY) ; Hegner, Benedikt (CERN) ; Madlener, Thomas (DESY) ; Smiesko, Juraj (CERN) ; Sailer, André (CERN)
The common and shared event data model EDM4hep is a core part of the Key4hep project. It is the component that is used to not only exchange data between the different software pieces, but it also serves as a common language for all the components that belong to Key4hep. [...]
2025 - 8 p.
- Published in : EPJ Web Conf. 337 (2025) 01131
Fulltext: PDF;
In : 27th International Conference on Computing in High Energy & Nuclear Physics (CHEP2024), Kraków, Poland, 19 - 25 Oct 2024, pp.01131
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2025-10-14
20:37 |
Evaluating FPGA Acceleration with Intel ® oneAPI Toolkit for High-Speed Data Processing
/ Perro, Alberto (CERN ; Marseille, CPPM) ; Durante, Paolo (CERN) ; Pisani, Flavio (CERN) ; Xochelli, Eleni (CERN ; Thessaly U.)
The LHCb Experiment employs GPU cards in its first level trigger system to enhance computing efficiency, achieving a data rate of 32 Tb/s from the detector. GPUs were selected for their computational power, parallel processing capabilities, and adaptability.However, trigger tasks necessitate extensive combinatorial and bitwise operations, ideally suited for FPGA implementation. [...]
2025 - 9 p.
- Published in : EPJ Web Conf. 337 (2025) 01070
Fulltext: PDF;
In : 27th International Conference on Computing in High Energy & Nuclear Physics (CHEP2024), Kraków, Poland, 19 - 25 Oct 2024, pp.01070
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2025-10-14
20:37 |
Comparative efficiency of HEP codes across languages and architectures
/ Skipsey, Samuel Cadellin (Glasgow U.) ; Stewart, Graeme Andrew (CERN)
Recently, interest in measuring and improving the energy (and carbon) efficiency of computation in HEP, and elsewhere, has grown significantly. Measurements have been, and continue to be, made of the efficiency of various computational architectures in standardised benchmarks, but those benchmarks tend to compare only implementations in single programming languages. [...]
2025 - 8 p.
- Published in : EPJ Web Conf. 337 (2025) 01036
Fulltext: PDF;
In : 27th International Conference on Computing in High Energy & Nuclear Physics (CHEP2024), Kraków, Poland, 19 - 25 Oct 2024, pp.01036
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2025-10-14
20:37 |
Migration of CADI to Fence
/ Imran, Muhammad (NCP, Islamabad) ; Pfeiffer, Andreas (CERN) ; Shad, Rao Muhammad Atif (NCP, Islamabad) ; Closier, Joel (CERN) ; Samantas, Athanasios (CERN) ; Khan, Misha Urooj (NCP, Islamabad)
The CMS Analysis Database Interface (CADI) is a central tool for managing physics publications within the CMS experiment, tracking the life-cycle of analysis projects from inception to publication. To modernize its infrastructure and align with broader LHC technology developments, CMS has initiated a migration of CADI to the Glance framework — a system originally developed by ATLAS and later enhanced by LHCb using modular and domain-driven design. [...]
2025 - 8 p.
- Published in : EPJ Web Conf. 337 (2025) 01027
Fulltext: PDF;
In : 27th International Conference on Computing in High Energy & Nuclear Physics (CHEP2024), Kraków, Poland, 19 - 25 Oct 2024, pp.01027
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2025-10-14
20:37 |
Zero-overhead training of machine learning models with ROOT data
/ Padulano, Vincenzo Eduardo (CERN) ; Pranckietis, Kristupas (CERN ; Vilnius U. (main)) ; Moneta, Lorenzo (CERN)
The ROOT software framework is widely used in High Energy and Nuclear Physics (HENP) for storage, processing, analysis and visualization of large datasets. With the large increase in usage of ML for experiment workflows, especially lately in the last steps of the analysis pipeline, the matter of exposing ROOT data ergonomically to ML models becomes ever more pressing. [...]
2025 - 8 p.
- Published in : EPJ Web Conf. 337 (2025) 01097
Fulltext: PDF;
In : 27th International Conference on Computing in High Energy & Nuclear Physics (CHEP2024), Kraków, Poland, 19 - 25 Oct 2024, pp.01097
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